Intelligent process control using neural fuzzy techniques 1

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Intelligent Control Systems Intelligent control systems are becoming increasingly popular in today's world, with the rise of automation and smart technologies. These systems are designed to use artificial intelligence (AI) and machine learning (ML) algorithms to control and optimize various processes, from manufacturing and logistics to energy management and building automation. While the benefits of intelligent control systems are numerous, there are also some concerns and challenges that need to be addressed. One of the main advantages of intelligent control systems is their ability to improve efficiency and productivity. By using AI and ML algorithms, these systems can analyze large amounts of data and make real-time decisions based on that analysis. This can lead to faster and more accurate decision-making, which in turn can lead to increased productivity and reduced costs. For example, in a manufacturing plant, an intelligent control system can optimize the production process by adjusting the speed of machines, reducing waste, and minimizing downtime. Another benefit of intelligent control systems is their ability to improve safety and security. By using sensors and cameras, these systems can monitor and detect potential hazards or security threats in real-time. They can also automatically take action to prevent or mitigate these risks, such asshutting down a machine or alerting security personnel. This can help prevent accidents and reduce the risk of theft or other security breaches. However, there are also some concerns and challenges associated with intelligent control systems. One of the main concerns is the potential impact on jobs. As these systems become more advanced and widespread, there is a risk that they could replace humanworkers in certain industries. This could lead to job losses and economic disruption, particularly in industries that rely heavily on manual labor. Another challenge is the potential for these systems to malfunction or be hacked. While intelligent control systems are designed to be secure and reliable, there isalways a risk of technical glitches or cyber attacks. If a system were to malfunction or be hacked, it could cause serious damage or disruption to the processes it controls. This highlights the importance of ensuring that these systems are properly designed, tested, and secured. Another concern is the potential for these systems to be biased or discriminatory. AI and ML algorithmsare only as good as the data they are trained on, and if that data is biased or incomplete, the resulting system could also be biased. This could lead to unfair or discriminatory outcomes, particularly in areas such as hiring, lending, or criminal justice. It is therefore important to ensure that these systems are designed and trained with fairness and inclusivity in mind. In conclusion, intelligent control systems have the potential to revolutionize many industries and improve efficiency, productivity, safety, and security. However, there are also some concerns and challenges that need to be addressed, such as the potential impact on jobs, the risk of malfunctions or cyber attacks, and the potential for bias or discrimination. To ensure that these systems are used responsibly and ethically, it is important to involve a diverse range of stakeholders in the design and implementation process, and to prioritize transparency, accountability, and fairness.。

I.J. Intelligent Systems and Applications, 2013, 06, 78-88Published Online May 2013 in MECS (/)DOI: 10.5815/ijisa.2013.06.10Evaluation Performance of IC Engine: Linear Tunable Gain Computed Torque Controller vs.Sliding Mode ControllerShahnaz Tayebi HaghighiIndustrial Electrical and Electronic Engineering SanatkadeheSabze Pasargad. CO (S.S.P. Co), NO:16 , PO.Code 71347-66773, Fourth floor , Dena Apr , Seven Tir Ave , Shiraz , IranE-mail: SSP.ROBOTIC@Samira SoltaniIndustrial Electrical and Electronic Engineering SanatkadeheSabze Pasargad. CO (S.S.P. Co), NO:16 , PO.Code 71347-66773, Fourth floor , Dena Apr , Seven Tir Ave , Shiraz , IranE-mail: SSP.ROBOTIC@Farzin PiltanIndustrial Electrical and Electronic Engineering SanatkadeheSabze Pasargad. CO (S.S.P. Co), NO:16 , PO.Code 71347-66773, Fourth floor , Dena Apr , Seven Tir Ave , Shiraz , IranE-mail: SSP.ROBOTIC@Marzieh kamgariIndustrial Electrical and Electronic Engineering SanatkadeheSabze Pasargad. CO (S.S.P. Co), NO:16 , PO.Code 71347-66773, Fourth floor , Dena Apr , Seven Tir Ave , Shiraz , IranE-mail: SSP.ROBOTIC@Saeed ZareIndustrial Electrical and Electronic Engineering SanatkadeheSabze Pasargad. CO (S.S.P. Co), NO:16 , PO.Code 71347-66773, Fourth floor , Dena Apr , Seven Tir Ave , Shiraz , IranE-mail: SSP.ROBOTIC@Abstract—Design a nonlinear controller for second order nonlinear uncertain dynamical systems (e.g., internal combustion engine) is one of the most important challenging works. This paper focuses on the comparative study between two important nonlinear controllers namely; computed torque controller (CTC) and sliding mode controller (SMC) and applied to internal combustion (IC) engine in presence of uncertainties. In order to provide high performance nonlinear methodology, sliding mode controller and computed torque controller are selected. Pure SMC and CTC can be used to control of partly known nonlinear dynamic parameters of IC engine. Pure sliding mode controller and computed torque controller have difficulty in handling unstructured model uncertainties. To solve this problem applied linear error-based tuning method to sliding mode controller and computed torque controller for adjusting the sliding surface gain ( ) and linear inner loop gain (). Since the sliding surface gain () and linear inner loop gain () are adjusted by linear error-based tuning method. In this research new and new are obtained by the previous and multiple gains updating factor. The results demonstrate that the error-based linear SMC and CTC are model-based controllers which works well in certain and uncertain system. These controllers have acceptable performance in presence of uncertainty.Index Terms—Internal Combustion Engine, Sliding Mode Controller, Computed Torque Controller, Linear Error-Based Sliding Mode Controller, Linear Error Based Computed Torque ControllerI.IntroductionThe internal combustion (IC) engine is designed to produce power from the energy that is contained in its fuel. More specifically, its fuel contains chemical energy and together with air, this mixture is burned to output mechanical power. There are various types of fuels which can be used in IC engines namely; petroleum, diesel, bio-fuels, and hydrogen [1].Modeling of an entire IC engine is a very important and complicated process because engines are nonlinear, multi inputs-multi outputs (MIMO) and time variant. Controller design is the main parts in this paper as well as the major objectives in the controller design are stability and robustness. One of the significant challenges in control algorithms is design a linear controller for nonlinear systems. When system works with various parameters and hard nonlinearities this technique is very useful in order to be implemented easily but it has some limitations such as working near the system operating point[9]. Some of IC engines which work in industrial processes are controlled by linear controllers, but linear controller design for IC engines is extremely difficult [1- 6]. Computed torque controller (CTC) is a powerful nonlinear controller which it widely used in control of IC engine. It is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law. This controller works very well when all dynamic and physical parameters are known but when the IC engine has variation in dynamic parameters, in this situation the controller has no acceptable performance[7-14]. In practice, most of physical systems (e.g., IC engine) parameters are unknown or time variant, therefore, on-line tuneable gain computed torque controller used to compensate dynamic equation of IC engine[1, 6]. Sliding mode controller (SMC) is one of the influential nonlinear controllers in certain and uncertain systems which are used to solved stability and robustness [10]. The main reason for this popularity is the attractive properties which SMCs have, such as good control performance for nonlinear systems, applicability to MIMO systems and well-established design criteria for discrete-time systems. SMC may employ unnecessarily large control signals to overcome the parametric uncertainties and difficulty in the calculation of what is known as the equivalent control [11-17]. In various dynamic parameters systems that need to be training on-line adaptive control methodology is used. Adaptive control methodology can be classified into two main groups, namely, traditional adaptive method and fuzzy adaptive method [18-22]. Fuzzy adaptive method is used in systems which want to training parameters by expert knowledge. Traditional adaptive method is used in systems which some dynamic parameters are known. In this research in order to solve disturbance rejection and uncertainty dynamic parameter, adaptive method are applied to sliding mode controller and computed torque controller.There have been several engine controller designs over the past 40 years in which the goal is to improve the efficiency and exhaust emissions of the automotive engine. A key development in the evolution was the introduction of a closed loop fuel injection control algorithm by Rivard in the 1973 [2]. This strategy was followed by an innovative linear quadratic control method in 1980 by Cassidy [3] and an optimal control and Kalman filtering design by Powers [4]. Although the theoretical design of these controllers was valid, at that time it was not realistic to implement such complex designs. Therefore, the production of these designs did not exist and engine designers did adopt the methods. Due to the increased production of the microprocessor in the 1990's, it became practical to use these microprocessors in developing more complex control and estimation algorithms that could potentially be used in production automotive engines. Specific applications of A/F ratio control based on observer measurements in the intake manifold was developed by Benninger in 1991 [5]. Another approach was to base the observer on measurements of exhaust gases measured by the oxygen sensor and on the throttle position, which was researched by Onder [6]. These observer ideas used linear observer theory. Hedrick also used the measurements of the oxygen sensor to develop a nonlinear, sliding mode approach to control the A/F ratio [7]. All of the previous control strategies were applied to engines that used only port fuel injections, where fuel was injected in the intake manifold. The development of these control strategies for direct injection was not practical because the production of direct injection automobiles did not begin until the mid 1990's. Mitsubishi began to investigate combustion control technologies for direct injection engines in 1996 [8]. Furthermore, engines that used both port fuel and direct systems appeared a couple years ago, leading to the interest of developing the corresponding control strategies. Current production A/F ratio controllers use closed loop feedback and feed forward control to achieve the desired stoichio metric mixture. These controllers use measurements from the oxygen sensor to control the desired amount of fuel that should be injected over the next engine cycle and have been able to control the A/F very well.Research on computed torque controller is significantly growing on robot manipulator application which has been reported in [1, 6, 15-16]. Vivas and Mosquera [15]have proposed a predictive functional controller and compare to computed torque controller for tracking response in uncertain environment. However both controllers have been used in feedback linearization, but predictive strategy gives better result as a performance. A computed torque control with non parametric regression models have been presented for a robot arm[16]. This controller also has been problem in uncertain dynamic models. Based on [1, 6]and [15-16] computed torque controller is a significant nonlinear controller to certain systems which it is based on feedback linearization and computes the required arm torques using the nonlinear feedback control law.In order to solve the chattering in the systems output, boundary layer method should be applied so beginning able to recommended model in the main motivation which in this method the basic idea is replace the discontinuous method by saturation (linear) method with small neighborhood of the switching surface [11-17, 38-39]. Slotine and Sastry have introducedboundary layer method instead of discontinuous method to reduce the chattering[18]. Estimated uncertainty method is used in term of uncertainty estimator to compensation of the system uncertainties. It has been used to solve the chattering phenomenon and also nonlinear equivalent dynamic. The applications of artificial intelligence, neural networks and fuzzy logic on estimated uncertainty method have been reported in [19-22]. Wu et al. [23] have proposed a simple fuzzy estimator controller beside the discontinuous and equivalent control terms to reduce the chattering. In recent years, artificial intelligence theory has been used in sliding mode control systems. Fuzzy logic controller (FLC) can be used to control nonlinear, uncertain and noisy systems. This method is free of some model-based techniques as in classical controllers. Fuzzy logic provides a method which is able to model a controller for nonlinear plant with a set of IF-THEN rules, or it can identify the control actions and describe them by using fuzzy rules. The applications of artificial intelligence, neural networks and fuzzy logic, on nonlinear systemcontrol have reported in [24-26]. Wai et al. [24-25]have proposed a fuzzy neural network (FNN) optimal control system to learn a nonlinear function in the optimal control law. This controller is divided into three main groups: arterial intelligence controller (fuzzy neural network) which it is used to compensate the system’s nonlinearity and improves by adaptive method, robust controller to reduce the error and optimal controller which is the main part of this controller. Research on applied fuzzy logic methodology in sliding mode controller (FSMC) to reduce or eliminate the high frequency oscillation (chattering), to compensate the unknown system dynamics and also to adjust the linear sliding surface slope in pure sliding mode controller considerably improves the robot manipulator control process [27-28].H.Temeltas [29] has proposed fuzzy adaption techniques for SMC to achieve robust tracking of nonlinear systems and solves the chattering problem. Conversely system’s performance is better than sliding mode controller; it is depended on nonlinear dynamic equation. Investigation on applied sliding mode methodology in fuzzy logic controller (SMFC) to reduce the fuzzy rules and refine the stability of close loop system in fuzzy logic controller has grown specially in recent years as the nonlinear system control [30-33]. Lhee et al. [32]have presented a fuzzy logic controller based on sliding mode controller to more formalize and boundary layer thickness.In various dynamic parameters systems (e.g., IC engine) which need to be training, on-line tunable gain control methodology is used. In this research in order to solve disturbance rejection and uncertainty dynamic parameter, on-line tunable method is applied to artificial sliding mode controller. F Y Hsu et al. [34]have presented adaptive fuzzy sliding mode control which can update fuzzy rules to compensate nonlinear parameters and guarantee the stability robot manipulator controller.This paper is organized as follows: In section 2, main subject of engine operating cycle and detail dynamic formulation of modelling in IC engine, sliding mode controller and computed torque controller are presented. Detail of proposed linear error-based sliding mode controller and linear error based computed torque controller are presented in section 3. In section 4, the simulation result is presented and finally in section 5, the conclusion is presented.II.Theory2.1Dynamic Formulation of IC EngineDynamic modeling of IC engine is used to describe the nonlinear behavior of IC engine, design of model based controller such as pure variable structure controller based on nonlinear dynamic equations, and for simulation. The dynamic modeling describes the relationship between fuel to air ratio to PFI and DI and also it can be used to describe the particular dynamic effects (e.g., motor pressure, angular speed, mass of air in cylinder, and the other parameters) to behavior of system[1].The equation of an IC engine governed by the following equation [1, 4, 25, 29, 43-44]:*+*+[][][ ][][][](1)Where is port fuel injector, is direct injector, is a symmetric and positive define mass of air matrix, is the pressure of motor, is engine angular speed and is matrix mass of air in cylinder. Fuel ratio and exhaust angle are calculated by [25, 29, 43-44]:* ̈+*+ {*+{[][ ][]*+[]}}(2)The above target equivalence ratio calculation will be combined with fuel ratio calculation that will be used for controller design purpose.2.2Sliding Mode Controller:Sliding mode controller (SMC) is a powerful nonlinear controller which has been analyzed by manyresearchers especially in recent years. This theory was first proposed in the early 1950 by Emelyanov and several co-workers and has been extensively developed since then with the invention of high speed control devices[11-17].A time-varying sliding surface is given by the following equation:̃(3) where λ is the constant and it is positive. A simple solution to get the sliding condition when the dynamic parameters have uncertainty is the switching control law:̂⃗(4) Where the switching function of defined as and the ⃗⃗ is the positive constant. Based on above discussion, the control law for an IC engine is written as:(5) Where, the model-based component is compensated the nominal dynamics of systems. Therefore can calculate as follows:[*+[][ ][][][]]*+(6)Figure 1 shows pure sliding mode controller which applied to internal combustion engine.Fig. 1: Block diagram of a variable structure controller: applied to IC engineComputed Torque Controller:The central idea ofComputed torque controller (CTC) is feedbacklinearization so, originally this algorithm is calledfeedback linearization controller. It has assumed that the desired motion trajectory for the manipulator, as determined, by a path planner. Defines the trackingerror as:(7) Where e(t) is error of the plant, is desired input variable, that in our system is desired displacement, is actual displacement. If an alternative linear state-space equation in the form can be defined aṡ*+*+(8)With*+([][ ][][][])*+̂and this is known as the Brunousky canonical form. By equation (7) and (8) the Brunousky canonical form can be written in terms of the state as [1]: * ̇+*+* ̇+*+(9)Witḣ*+ [][ ][]*+[](10)Then compute the required IC engine torques using inverse of equation (10), is;̂*+( )[][ ][]*+[](11)This is a nonlinear feedback control law that guarantees tracking of desired trajectory. Selecting proportional-plus-derivative (PD) feedback for U(t) results in the PD-computed torque controller [6];̂̇*+ [][ ][]*+[](12)and the resulting linear error dynamics arė(13) According to the linear system theory, convergence of the tracking error to zero is guaranteed [6]. Where and are the controller gains. The result schemes is shown in Figure 2, in which two feedback loops, namely, inner loop and outer loop, which an inner loop is a compensate loop and an outer loop is a tracking error loop.Fig. 2: Block diagram of PD-computed torque controller: applied to IC engineThe application of proportional-plus-derivative (PD) computed torque controller to control of IC engine introduced in this research.III.MethodologyLinear error-based tunable gain sliding mode controller:Sliding mode controller has difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining sliding mode controller and linear error-based tuning method which this method can helps to eliminate the chattering in presence of switching function method and improves th e system’s tracking performance by online tuning method. In this research the nonlinear equivalent dynamic (equivalent part) formulation problem in uncertain system is solved by using on-line linear error-based tuning theorem. In this method linear error-based theorem is applied to sliding mode controller to adjust the sliding surface slope. Sliding mode controller has difficulty in handling unstructured model uncertainties and this controller’s performance is sensitive to sliding surface slope coefficient. It is possible to solve above challenge by combining linear error-based tuning method and sliding mode controller which this methodology can help to improve system’s tracking performance by on-line tuning (linear error-based tuning) method. Based on above discussion, compute the best value of sliding surface slope coefficient has played important role to improve system’s tracking performance especially when the system parameters are unknown or uncertain. This problem is solved by tuning the surface slope coefficient () of the sliding mode controller continuously in real-time. In this methodology, the system’s performance is improved with respect to the pure sliding mode controller. Figure 3 shows the linear error-based tuning sliding mode controller. Based on (5) and (6) to adjust the sliding surface slope coefficient we define ̂| as the linear error-based tuning methodology.Fig. 3: Block diagram of a linear error-based sliding mode controller: applied to IC enginê|(14) If minimum error () is defined by;( | ̂|) (15) Where is adjusted by an adaption law and this law is designed to minimize the error’s parameters of adaption law in linear error-based tuning sliding mode controller is used to adjust the sliding surface slope coefficient. Linear error-based tuning part is a supervisory controller based on the following formulation methodology. This controller has three inputs namely; error change of error ( ̇) and the integral of error (∑) and an output namely; gain updating factor. As a summary design a linear error-based tuning is based on the following formulation:̇∑̇⇒̇∑ ̇(16)⇒∑Where is gain updating factor, (∑) is the integral of error, ( ̇) is change of error, is error and K is a coefficient.Linear error-based tuning computed torque controller: Computed torque controller has difficulty in handling unstructured model uncertainties. It is possible to solve this problem by combining computed torque controller and linear error-based tuning method which this method can helps to eliminate the error and improves the system’s tracking performance by online tuning method. In this research the nonlinear equivalent dynamic (equivalent part) formulation problem in uncertain system is solved by using on-line linear error-based tuning theorem. In this method linear error-based theorem is applied to computed torque controller to adjust the linear inner loop gain. Computed torque controller has difficulty in handling unstructured model uncertainties and this c ontroller’s performance is sensitive to linear inner loop gain. It is possible to solve above challenge by combining linear error-based tuning method and computed torque controller which this methodology can help to improve system’s tracking performance by on-line tuning (linear error-based tuning) method. Based on above discussion, compute the best value of linear inner loop gain has played important role to improve system’s tracking performance especially when the system parameters are unknown or uncertain. This problem is solved by tuning the linear inner loop gain () of the computed torque controller continuously in real-time. In this methodology, the system’s performance is improved with respect to the pure computed torque controller.̂|(17) If minimum error () is defined by;( | ̂|) (18) Where is adjusted by an adaption law and this law is designed to minimize the error’s parameters of adaption law in linear error-based tuning computed torque controller is used to adjust the linear inner loop gain. Linear error-based tuning part is a supervisory controller based on the following formulation methodology. This controller has three inputs namely; error change of error ( ̇) and the integral of error (∑) and an output namely; gain updating factor. As a summary design a linear error-based tuning is based on the following formulation:̇∑⇒ ∑ (19)⇒∑Where is gain updating factor, (∑ ) is the integral of error, ( ̇) is change of error, is error and K is a coefficient.IV. Results and DiscussionTo validate of this work; on-line tuning sliding mode controller and on-line tuning computed torque controller are compared to control and improve the response of IC engine. The simulation was implemented in MATLAB/SIMULINK environment. Fuel ratio response, controller robustness, steady state error and RMS error are compared in these controllers. It is noted that, these systems are tested by band limited white noise with a predefined 10%, 20% and 40% of relative to the input signal amplitude. This type of noise is used to external disturbance in continuous and hybrid systems.Fuel ratio response: Figure 4 is shown the fuel ratio in linear tuning SMC, SMC and linear tuning CTC in certain environment and without disturbance for desired.Fig. 4: LTSMC Vs. LTCTC: fuel ratio responseBy comparing this response, Figure 4, in LTSMC and LTCTC, both of methodologies have identical response. Based on Figure 5 it is observed that, the overshoot in LTSMC is 0%, in PD-SMC’s is 1% and in LTCTC’s is 0%, and rise time in LTSMC’s is 0.6 seconds, i n PD-SMC’s is 0.483 second and in LTCTC’s is about 0.6 seconds. From the trajectory MATLAB simulation for LTSMC, PD-SMC and LTCTC in certain system, it was seen that all of three controllers have acceptable performance.Controller robustness: Figures 5 to 7 show the power disturbance elimination in LTSMC, PD-SMC and LTCTC with disturbance. The disturbance rejection is used to test the robustness comparisons of these three controllers. A band limited white noise with predefined of 10%, 20% and 40% the power of input signal value is applied to the step trajectory. It found fairly fluctuations in trajectory responses.Fig. 5: Desired input, LTSMC, LTCTC and PD-SMC for IC engine trajectory with 10%external disturbanceFig. 6: Desired input, LTSMC, LTCTC and PD-SMC for IC engine trajectory with 20%external disturbanceBased on Figure 5; by comparing response trajectory with 10% disturbance of relative to the input signal amplitude in LTSMC, LTCTC and PD-SMC, LTSMC’s overshoot about (0%) is lower than LTCTC’s (0.5%) and PD-SMC’s (1%). PD-SMC’s rise time (0.5 seconds) is lower than LTCTC’s (0.63 second) and LTSMC’s (0.65 second).Based on Figure 6; by comparing response trajectory with 20% disturbance of relative to the input signal amplitude in LTSMC, LTCTC and PD-SMC, LTSMC’s overshoot about (0%)is lower than LTCTC’s (1.8%) and PD-SMC’s (2.1%).PD-SMC’s rise time (0.5 seconds) is lower than LTCTC’s (0.63 second) and LTSMC’s (0.66 second). Based on Figure 6, it was seen that, LTSMC’s and LTCTC performance are better than PD-SMC because LTSMC and LTCTC can auto-tune the sliding surface slope coefficient and gain as the dynamic IC e ngine parameter’s change and in presence of external disturbance whereas PD-SMC cannot.Fig. 7: Desired input, LTSMC, LTCTC and PD-SMC for IC engine trajectory with 40%external disturbanceBased on Figure 7; by comparing response trajectory with 40% disturbance of relative to the input signal amplitude in LTSMC, PD-SM C and LTCTC, LTSMC’s overshoot about (0%)is lower than LTCTC’s (6%) and PD-SMC’s (8%). PD-SMC’s rise time (0.5 seconds) is lower than LTCTC’s (0.7 second) and LTSMC’s (0.8 second). Based on Figure 8, LTCTC and PD-SMC have moderately oscillation in trajectory response with regard to 40% of the input signal amplitude disturbance but LTSMC has stability in trajectory responses in presence of uncertainty and external disturbance.Steady state error:Figure 8 is shown the error performance in LTSMC, PD- SMC and LTCTC for IC engine. The error performance is used to test the disturbance effect comparisons of these controllers. In this system this time is transient time and this part of error introduced as a transient error. Besides the Steady State and RMS error in LTSMC, LTCTC and PD-SMC it is observed that, error performances in LTSMC (Steady State error =0.9e-12 and RMS error=1.1e-12) are about lower than LTCTC (Steady State error =0.7e-8 and RMS error=1e-7) and PD-SMC’s (Steady State error=1e-8 and RMS error=1.2e-6).Fig. 8: LTSMC, PD-SMC and LTCTC for steady state error without disturbanceThe LTSMC gives significant steady state error performance when compared to LTCTC and PD-SMC. When applied 40% disturbances in LTSMC the RMS error increased approximately 0.0164% (percent of increase the LTSMC RMSerror=), In LTCTC the RMS error increased approximately 6.9% (percent of increase the LTCTC RMSerror=)In PD-SMC the RMS error increased approximately 9.17% (percent of increase the PD-SMC RMS error=).In this part LTSMC, PD-SMC and LTCTC have been comparatively evaluation through MATLAB simulation, for IC engine control.V.ConclusionRefer to the research, a linear tuning sliding mode controller and linear tuning computed torque controller are design and compared with each other and applied to IC engine in presence of structure and unstructured uncertainties. Regarding to the positive points in classical sliding mode controller and computedtorquecontroller in linear tuning on-line methodology, the response is improved. Linear tuning methodology by adding to the sliding mode controller and computed torque controller has covered negative points. This implementation considerably reduces the chattering phenomenon and error in the presence of uncertainties. As a result, this controller will be able to control a wide range of IC engine with a high sampling rates because its easy to implement versus high speed markets. AcknowledgmentThe authors would like to thank the anonymous reviewers for their careful reading of this paper and for their helpful comments. This work was supported by the SSP Research and Development Corporation Program of Iran under grant no. 2012-Persian Gulf-2B. References[1]Heywood, J., “Internal Combustion EngineFundamentals”, McGraw-Hill, New York, 1988. [2]J. G. Rivard, "Closed-loop Electronic FuelInjection Control of the IC Engine," in Society of Automotive Engineers, 1973.[3]J. F. Cassidy, et al, "On the Design of ElectronicAutomotive Engine Controls using linear Quadratic Control Theory," IEEE Trans on Control Systems, vol. AC-25, October 1980. [4]W. E. Powers, "Applications of Optimal Controland Kalman Filtering to Automotive Systems,"International Journal of Vehicle Design, vol.Applications of Control Theory in the Automotive Industry, 1983.[5]N. F. Benninger, et al, "Requirements andPerfomance of Engine Management Systems under Transient Conditions," in Society of AutomotiveEngineers, 1991.[6] C. H. Onder, et al, "Model-Based MultivariableSpeed and Air-to-Fuel Ratio Control of an SIEngine," in Society of Automotive Engineers, 1993.[7]S. B. Cho, et al, "An Observer-based ControllerDesign Method for Automotive Fuel-Injection Systems," in American Controls Conference, 1993, pp. 2567-2571.[8]T. Kume, et al, "Combustion Technologies forDirect Injection SI Engine," in Society of Automotive Engineers, 1996.[9]Frank L.Lewis. Nonlinear dynamics and control,Handbook, pages 51-70. CRC press, 1999. [10]Okyak Kaynak, “Guest Editorial Special Sectionon Computationally Intelligent Methodologies and Sliding-Mode Control”, IEEE TRANSACTIONSON INDUSTRIAL ELECTRONICS, VOL. 48,NO. 1, 2001[11] F. Piltan, et al., "Artificial Control ofNonlinear Second Order Systems Based onAFGSMC," Australian Journal of Basic andApplied Sciences, 5(6), pp. 509-522, 2011.[12]Piltan, F., et al., 2011. Design sliding modecontroller for robot manipulator with artificialtunable gain. Canaidian Journal of pure andapplied science, 5 (2): 1573-1579.[13]Piltan, F., et al., 2011. Design Artificial NonlinearRobust Controller Based on CTLC and FSMC withTunable Gain, International Journal of Robotic andAutomation, 2 (3): 205-220.[14]Piltan, F., et al., 2011. Design MathematicalTunable Gain PID-Like Sliding Mode FuzzyController with Minimum Rule Base, InternationalJournal of Robotic and Automation, 2 (3): 146-156.[15]Piltan, F., et al., 2011. Design of FPGA basedsliding mode controller for robot manipulator, International Journal of Robotic and Automation, 2(3): 183-204.[16]Piltan, F., et al., 2011. A Model Free RobustSliding Surface Slope Adjustment in Sliding ModeControl for Robot Manipulator, World AppliedScience Journal, 12 (12): 2330-2336.[17]Piltan, F., et al., 2011. Design Adaptive FuzzyRobust Controllers for Robot Manipulator, WorldApplied Science Journal, 12 (12): 2317-2329. [18]Slotine, J.J. and S. Sastry, 1983. Tracking controlof non-linear systems using sliding surfaces, withapplication to robot manipulators. InternationalJournal of Control, 38: 465-492.[19]M. Ertugrul and O. Kaynak, "Neuro sliding modecontrol of robotic manipulators," Mechatronics,vol. 10, pp. 239-263, 2000.[20]P. Kachroo and M. Tomizuka, "Chatteringreduction and error convergence in the sliding-mode control of a class of nonlinear systems,"Automatic Control, IEEE Transactions on, vol. 41,pp. 1063-1068, 2002.[21]H. Elmali and N. Olgac, "Implementation ofsliding mode control with perturbation estimation(SMCPE)," Control Systems Technology, IEEETransactions on, vol. 4, pp. 79-85, 2002.[22]J. Moura and N. Olgac, "A comparative studyon simulations vs. experiments of SMCPE," 2002,pp. 996-1000.[23]B. Wu, et al., "An integral variable structurecontroller with fuzzy tuning design for electro-hydraulic driving Stewart platform," 2006, pp. 5-945.。

Theoretical Computer Science 253 (2001) 61{93www。

elsevier。

com/locate/tcsDeveloping a Hybrid Programmable Logic Controller Platform for aFlexible Manufacturing SystemHenning Dierks 1University of Oldenburg，Fachbereich Informatik，Postfach 2503, 2900 Oldenburg，GermanyAbstract：In this article，we present the design and implementation of a flexible manufacturing system （FMS) control platform based on a programmable logic controller （PLC) and a personal computer (PC）—based visual man—machine interface (MMI）and data acquisition (DAS）unit。

The key aspect of an FMS is its flexibility to adapt to changes in a demanding process operation。

The PLC provides feasible solutions to FMS applications, using PC-based MMI/DAS, whereby PLCs are optimized for executing rapid sequential control strategies。

PCs running MMI/DAS front—ends make intuitive operation interfaces，full of powerful graphics and reporting tools. Information from the PC can be distributed through a company’s local area network or web using client—server technologies。

智能控制技术英语In the rapidly evolving world of technology,intelligent control systems have emerged as a crucial link between machines and humans. These systems, powered by advanced algorithms and sensors, enable precise andefficient control of various devices and processes. The integration of artificial intelligence (AI) and machine learning (ML) algorithms into control systems has further revolutionized the field, making it possible to adapt to changing environments and optimize performance in real-time. The core principle of intelligent control technologylies in its ability to process vast amounts of data, learn from past experiences, and make informed decisions. This data-driven approach allows the system to adapt to changesin the environment, predict future outcomes, and adjust its control strategies accordingly. The result is a more responsive, efficient, and reliable system that can handle complex tasks with minimal human intervention.One of the key applications of intelligent control technology is in robotics. Modern robots are equipped with sensors that provide them with a detailed understanding oftheir surroundings. By processing this information through AI and ML algorithms, robots can make split-second decisions about how to interact with their environment, ensuring smooth and efficient movement. This technology has revolutionized fields like manufacturing, where robots can now perform complex tasks with greater precision and speed than ever before.Another area where intelligent control technology has made significant impact is in the field of automation. By integrating AI and ML algorithms into control systems, itis now possible to automate processes that were previously considered too complex or unpredictable. This not only improves efficiency but also reduces the risk of human error, making operations safer and more reliable.However, the rise of intelligent control technology also presents new challenges. As systems become more autonomous, the need for robust safety measures and ethical guidelines becomes paramount. It is crucial to ensure that these systems are designed with safety as a top priority, and that they are capable of making ethical decisions in case of conflicting objectives.Despite these challenges, the future of intelligent control technology looks bright. With advances in AI, ML, and sensor technology, we can expect even moresophisticated control systems that are capable of handling even more complex tasks. As these systems become more pervasive in our daily lives, they will play a crucial role in shaping the future of industry, transportation, healthcare, and beyond.In conclusion, intelligent control technology has emerged as a key driver of innovation in modern society. By bridging the gap between machines and humans, it hasenabled unprecedented levels of precision, efficiency, and autonomy in various fields. While challenges remain, the potential of this technology is vast, and its impact on our future is sure to be profound.**智能控制技术：连接机器与人类的桥梁**在科技飞速发展的时代，智能控制系统已成为机器与人类之间的重要纽带。

神经科学在人工智能中的应用随着人工智能的发展，越来越多的领域开始应用神经科学的相关技术。

由于神经科学将人类思维和行为研究为生物学、物理学、数学等诸多领域的跨学科科学，因此将神经科学应用到人工智能中，可以更好地模拟人类思考和行为的机制。

本文将重点讲述神经科学在人工智能中的应用，包括深度学习、图像识别和语音识别等方面。

一、深度学习深度学习是一种通过对输入数据的分层表示来学习抽象特征的机器学习技术。

该技术依赖于多层神经网络，可以用于自然语言处理、图像处理和语音处理等领域。

利用神经科学的相关研究成果，可以改善深度学习算法的效率和准确性。

比如，神经科学家们在研究大脑时发现，大脑中的神经元之间存在许多复杂的互动关系，这些关系可以用图形来表示。

而在深度学习中，我们也可以使用图形来表示神经网络的结构。

将生物学和机器学习的这些相似之处相结合，可以进一步优化深度学习算法的表现，帮助人工智能更好地学习、理解和分析各种数据。

二、图像识别在人工智能中，图像识别是一个重要的应用领域。

在许多场景下，图像识别可以为人们提供更加方便的服务。

比如，在智能家居中，可以用图像识别技术来辨别家庭成员和领养的宠物。

在工业生产中，可以用图像识别技术来监测机器的运行状态。

利用神经科学研究成果，可以提高图像识别的准确率和速度。

神经科学家们在研究大脑时发现，视觉是一种由多个神经元协同运作的复杂功能。

即使是简单的视觉识别，也涉及到大量的神经元和神经元之间的复杂互动。

因此，将神经科学的相关研究成果应用到图像识别中，可以提高图像识别的准确率。

三、语音识别语音识别也是人工智能领域中的一个重要应用。

随着语音识别技术的不断发展，人们可以用语音来控制家庭设备、操作智能手机等。

利用神经科学研究成果，可进一步提高语音识别的精准度和速度，让人工智能更加智能化和个性化。

神经科学家们在研究大脑时发现，人类的听觉系统是一种非常复杂的生物学系统。

听觉系统中涉及到大量的神经元和义齿环，这些组成可以用来产生特定的声音识别结果。

智能控制系统中英文资料对照外文翻译文献附录一：外文摘要The development and application of Intelligence controlsystemModern electronic products change rapidly is increasingly profound impact on people's lives, to people's life and working way to bring more convenience to our daily lives, all aspects of electronic products in the shadow, single chip as one of the most important applications, in many ways it has the inestimable role. Intelligent control is a single chip, intelligent control of applications and prospects are very broad, the use of modern technology tools to develop an intelligent, relatively complete functional software to achieve intelligent control system has become an imminent task. Especially in today with MCU based intelligent control technology in the era, to establish their own practical control system has a far-reaching significance so well on the subject later more fully understanding of SCM are of great help to.The so-called intelligent monitoring technology is that:" the automatic analysis and processing of the information of the monitored device". If the monitored object as one's field of vision, and intelligent monitoring equipment can be regarded as the human brain. Intelligent monitoring with the aid of computer data processing capacity of the powerful, to get information in the mass data to carry on the analysis, some filtering of irrelevant information, only provide some key information. Intelligent control to digital, intelligent basis, timely detection system in the abnormal condition, and can be the fastest and best way to sound the alarm and provide usefulinformation, which can more effectively assist the security personnel to deal with the crisis, and minimize the damage and loss, it has great practical significance, some risk homework, or artificial unable to complete the operation, can be used to realize intelligent device, which solves a lot of artificial can not solve the problem, I think, with the development of the society, intelligent load in all aspects of social life play an important reuse.Single chip microcomputer as the core of control and monitoring systems, the system structure, design thought, design method and the traditional control system has essential distinction. In the traditional control or monitoring system, control or monitoring parameters of circuit, through the mechanical device directly to the monitored parameters to regulate and control, in the single-chip microcomputer as the core of the control system, the control parameters and controlled parameters are not directly change, but the control parameter is transformed into a digital signal input to the microcontroller, the microcontroller according to its output signal to control the controlled object, as intelligent load monitoring test, is the use of single-chip I / O port output signal of relay control, then the load to control or monitor, thus similar to any one single chip control system structure, often simplified to input part, an output part and an electronic control unit ( ECU )Intelligent monitoring system design principle function as follows: the power supply module is 0~220V AC voltage into a0 ~ 5V DC low voltage, as each module to provide normal working voltage, another set of ADC module work limit voltage of 5V, if the input voltage is greater than 5V, it can not work normally ( but the design is provided for the load voltage in the 0~ 5V, so it will not be considered ), at the same time transformer on load current is sampled on the accused, the load current into a voltage signal, and then through the current - voltage conversion, and passes through the bridge rectification into stable voltage value, will realize the load the current value is converted to a single chip can handle0 ~ 5V voltage value, then the D2diode cutoff, power supply module only plays the role of power supply. Signal to the analog-to-digital conversion module, through quantization, coding, the analog voltage value into8bits of the digital voltage value, repeatedly to the analog voltage16AD conversion, and the16the digital voltage value and, to calculate the average value, the average value through a data bus to send AT89C51P0, accepted AT89C51 read, AT89C51will read the digital signal and software setting load normal working voltage reference range [VMIN, VMAX] compared with the reference voltage range, if not consistent, then the P1.0 output low level, close the relay, cut off the load on the fault source, to stop its sampling, while P1.1 output high level fault light, i.e., P1.3 output low level, namely normal lights. The relay is disconnected after about 2minutes, theAT89C51P1.0outputs high level ( software design), automatic closing relay, then to load the current regular sampling, AD conversion, to accept the AT89C51read, comparison, if consistent, then the P1.1 output low level, namely fault lights out, while P1.3 output high level, i.e. normal lamp ( software set ); if you are still inconsistent, then the need to manually switch S1toss to" repair" the slip, disconnect the relay control, load adjusting the resistance value is: the load detection and repair, and then close the S1repeatedly to the load current sampling, until the normal lamp bright, repeated this process, constantly on the load testing to ensure the load problems timely repair, make it work.In the intelligent load monitoring system, using the monolithic integrated circuit to the load ( voltage too high or too small ) intelligent detection and control, is achieved by controlling the relay and transformer sampling to achieve, in fact direct control of single-chip is the working state of the relay and the alarm circuit working state, the system should achieve technical features of this thesis are as follows (1) according to the load current changes to control relays, the control parameter is the load current, is the control parameter is the relay switch on-off and led the state; (2) the set current reference voltage range ( load normal working voltage range ), by AT89C51 chip the design of the software section, provide a basis for comparison; (3) the use of single-chip microcomputer to control the light-emitting diode to display the current state of change ( normal / fault / repair ); specific summary: Transformer on load current is sampled, a current / voltage converter, filter, regulator, through the analog-digital conversion, to accept the AT89C51chip to read, AT89C51 to read data is compared with the reference voltage, if normal, the normal light, the output port P.0high level, the relay is closed, is provided to the load voltage fault light; otherwise, P1.0 output low level, The disconnecting relay to disconnect the load, the voltage on the sampling, stop. Two minutes after closing relay, timing sampling.System through the expansion of improved, can be used for temperature alarm circuit, alarm circuit, traffic monitoring, can also be used to monitor a system works, in the intelligent high-speed development today, the use of modern technology tools, the development of an intelligent, function relatively complete software to realize intelligent control system, has become an imminent task, establish their own practical control system has a far-reaching significance. Micro controller in the industry design and application, no industry like intelligent automation and control field develop so fast. Since China and the Asian region the main manufacturing plant intelligence to improve the degree of automation, new technology to improve efficiency, have important influence on the product cost. Although the centralized control can be improved in any particular manufacturing process of the overall visual, but not for those response and processingdelay caused by fault of some key application.Intelligent control technology as computer technology is an important technology, widely used in industrial control, intelligent control, instrument, household appliances, electronic toys and other fields, it has small, multiple functions, low price, convenient use, the advantages of a flexible system design. Therefore, more and more engineering staff of all ages, so this graduate design is of great significance to the design of various things, I have great interest in design, this has brought me a lot of things, let me from unsuspectingly to have a clear train of thought, since both design something, I will be there a how to design thinking, this is very important, I think this job will give me a lot of valuable things.中文翻译：智能控制系统的开发应用现代社会电子产品日新月异正在越来越深远的影响着人们的生活，给人们的生活和工作方式带来越来越大的方便，我们的日常生活各个方面都有电子产品的影子，单片机作为其中一个最重要的应用，在很多方面都有着不可估量的作用。