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Neural Computing and Applications

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13.07.2020 | Original Article

Binary particle swarm optimization-based T-S fuzzy predictive controller for nonlinear automotive application

verfasst von: Elsaid Md. Abdelrahim

Erschienen in: Neural Computing and Applications | Ausgabe 7/2021

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Abstract

In this paper, a robust evolutionary computing-assisted Takagi–Sugeno fuzzy predictive controller (T-S FPC) has been developed for nonlinear vehicle fuel injection and emission control. To strengthen the performance of T-S FPC, we have applied an enhanced evolutionary computing algorithm named binary particle swarm optimization (BPSO) that achieves optimal control variable by performing minimization of the cost function iteratively, where the cost function signifies the mean square error between reference data and the actual predicted data. To examine the efficacy of the proposed system, a case study was performed for an automotive vehicle to control its fuel injection, throttle angle, and emission control under nonlinear conditions. The simulation results affirmed that the proposed BPSO-based T-S FPC model exhibits optimal performance by achieving target performance with low mean square error between expected functions and prediction outcomes. The efficiency of the proposed BPSO T-S FPC model enables it to be used for online nonlinear control purposes for any type of the vehicle systems.

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Shatrov MG, Golubkov LN, Dunin AY, Dushkin PV (2019) Pressure oscillations as a factor affecting the management of the fuel injection process in the combustion chamber of a diesel engine. In: Systems of Signals Generating and Processing in the Field of on Board Comm, Moscow, Russia, pp 1–5

Luo X, Donkers MCF, de Jager B, Willems F (2019) Systematic design of multivariable fuel injection controllers for advanced diesel combustion. IEEE Trans Control Syst Technol 27(5):1979–1990CrossRef

Espinosa J, Vandewalle J, Wertz V (2005) Fuzzy logic, identification and predictive control, advances in industrial control, Springer, London

Wang P, Zhu C, Gao J (2019) Feedforward model predictive control of fuel-air ratio for lean-burn spark-ignition gasoline engines of passenger cars. IEEE Access 7:73961–73969CrossRef

Adiyasa IW, Firmansyah E, Cahyadi AI (2018) Design of LPG injection mechanism with PID control for 1 kVA Generator Set. In:2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Kuta, pp 453–458

Adiyasa IW, Firmansyah E, Cahyadi AI, Kautsar A (2018) Design of simple ECU for LPG fuel injection on conventional generator set. In: 2018 4th International Conference on Science and Technology (ICST), Yogyakarta, pp. 1–6

Mane YB, Parshuram Sawant S (2018) Software Design and Development of Engine Control Unit for Four Cylinder Petrol Engine. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, pp 1–4

Luo X, Donkers MCF, de Jager B, Willems F (2018) Norm-based multi-pulse diesel fuel injection control with minimal cyclic combustion variation. IEEE Control Syst Lett 2(3):309–314MathSciNetCrossRef

van der Weijst R, van Keulen T, Willems F (2018) On-line fuel-efficiency optimization of Diesel engines using constrained multivariable extremum-seeking. In: 2018 European Control Conference (ECC), Limassol, pp 213–218

10.

Cai L, Mao X, Ma Z (2018) Investigation on nonlinear speed regulating system in a high-pressure common-rail diesel. In: 2018 10th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, pp 278–283

11.

Yang Z, Winward E, Mason B, Corre SL, Childs T, Shahzad A (2019) Nonlinear Model Predictive Control of a Variable Valve Timing System in a Turbocharged Spark Ignition Engine*. In: 2019 American Control Conference, Philadelphia, PA, USA, pp 4344–4349

12.

Yin L, Turesson G, Tunestål P, Johansson R Model predictive control of an advanced multiple cylinder engine with partially premixed combustion concept. In: IEEE/ASME Transactions on Mechatronics

13.

Huang C, Liu B (2019) New studies on dynamic analysis of inertial neural networks involving non-reduced order method. Neurocomputing 325(24):283–287CrossRef

14.

Huang C, Zhang H (2019) Periodicity of non-autonomous inertial neural networks involving proportional delays and non-reduced order method. Int J Biomath 12(02):1950016MathSciNetCrossRef

15.

Kouvaritakis B, Cannon M (2001) Nonlinear predictive control, theory and practice. The Institution of Engineering and Technology, StevenageCrossRef

16.

Richalet J, O’Donovan D (2009) Predictive functional control, principles and industrial applications. Springer, LondonCrossRef

17.

Mayne DQ, Rawlings JB, Rao CV, Scokaert PO (2000) Constrained model predictive control: stability and optimality. Automatica 36(6):789–814MathSciNetCrossRef

18.

Han H-G, Wu XL, Qiao J-F (2013) Real-time model predictive control using a self-organizing neural network. IEEE Trans Neural Netw Learn Syst 24(9):1425–1436CrossRef

19.

Sarimveis H, Bafas G (2003) Fuzzy model predictive control of nonlinear processes using genetic algorithms. Fuzzy Sets Syst 139(1):59–80CrossRef

20.

Marusak PM (2009) Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors. Appl Soft Comput J 9(3):1111–1125CrossRef

21.

Chang YC, Chen CH, Zhu ZC, Huang YW (2016) Speed control of the surface-mounted permanent-magnet synchronous motor based on takagi-sugeno fuzzy models. IEEE Trans Power Electron 31(9):6504–6510CrossRef

22.

Cairano SD, Bernardini D, Bemporad A, Kolmanovsky IV (2014) Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management. IEEE Trans Control Syst Technol 22(3):1018–1031CrossRef

23.

Ding B, Ping X (2014) Output feedback predictive control with one free control move for nonlinear systems represented by a takagi-sugenomodel. IEEE Trans Fuzzy Syst 22(2):249–263CrossRef

24.

Jiang H, Kwong CK, Chen Z, Ysim YC (2012) Chaos particle swarmoptimization andT-S fuzzy modeling approaches to constrained predictive control. Expert Syst Appl 39(1):194–201CrossRef

25.

Alfi A, Modares H (2011) System identification and control using adaptive particle swarm optimization. Appl Math Model 35(3):1210–1221MathSciNetCrossRef

26.

Zhang Y, Tao G, Chen M, Wen L (2017) Parameterization and adaptive control of multivariable noncanonical T-S fuzzy systems. IEEE Trans Fuzzy Syst 25(1):156–171CrossRef

27.

Pati AB, Chile RH, Wahane VR (2015) “Fuzzy-PSO approach for control in nonlinear systems - real time application. Michael Faraday IET Int Summit Kolkata 2015:36–42

28.

Dong XL, Han DR, Ghanari R et al (2017) Some new three-term Hestenes-Stiefel conjugate gradient methods with affine combination. Optimization 66(5):759–776MathSciNetCrossRef

29.

Wang J, Chen X, Huang L (2019) The number and stability of limit cycles for planar piecewise linear systems of node-saddle type. J Math Anal Appl 469(1):405–427MathSciNetCrossRef

30.

Feng X, Patton R (2014) Active fault tolerant control of a wind turbine via T-S fuzzy model predictive control. In: 22nd Mediterranean Conference on Control and Automation, Palermo, pp 446–451

31.

Wang J, Huang C, Huang L (2019) Discontinuity-induced limit cycles in a general planar piecewise linear system of saddle-focus type. Nonlinear Anal Hybrid Syst 33:162–178MathSciNetCrossRef

32.

Li X, Liu Z, Li J et al (2019) Existence and controllability for nonlinear fractional control systems with damping in hilbert spaces. Acta Math Sci 39:229–242MathSciNetCrossRef

33.

Cheridi DE, Mansouri N (2016) Robust H∞ fault estimation and fault tolerant control for T-S nonlinear systems with time varying delays. In: 2016 8th International Conference on Modelling, Identification and Control (ICMIC), Algiers, pp 503–508

34.

Lan J, Patton RJ (2017) Integrated design of fault-tolerant control for nonlinear systems based on fault estimation and T-S fuzzy modelling. IEEE Trans Fuzzy Syst 25(5):1141–1154CrossRef

35.

Bourahala F, Guelton K, Khaber F, Manamanni N (2016) Robust controller design for uncertain T-S fuzzy systems with time-varying delays. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, BC, pp 2337–2342

36.

Fayçal B, Noureddine N (2015) An LMI approach to robust H∞ control and stabilization analysis for uncertain T-S fuzzy systems with state and input time-delays. In: 2015 3rd International Conference on Control, Engineering and Information Technology (CEIT), Tlemcen, pp 1–6

37.

Stînean AI, Bojan-Dragos CA, Precup RE, Preitl S, Petriu EM (2015) Takagi-Sugeno PD + I fuzzy control of processes with variable moment of inertia. In: 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), Madrid, pp 1–8

38.

Wang ZP, Wu HN, Li HX (2017) Sampled-data fuzzy control for nonlinear coupled parabolic pde-ode systems. IEEE Trans Cybern 47(9):2603–2615CrossRef

39.

Wu HN, Wang ZP (2018) Observer-based H∞ sampled-data fuzzy control for a class of nonlinear parabolic PDE systems. IEEE Trans Fuzzy Syst 26(2):454–473CrossRef

40.

Liao QF, Cai WJ, Wang YY (2014) Effective T-S fuzzy model for decentralized control. In: 7th International Conference on Information and Automation for Sustainability, Colombo, pp 1–6

41.

Wei Y, Qiu J, Lam HK, Wu L (2017) Approaches to T-S fuzzy-affine-model-based reliable output feedback control for nonlinear ito stochastic systems. IEEE Trans Fuzzy Syst PP(99):1–1

42.

Li Y, Liu L, Feng Adaptive finite-time controller design for T-S fuzzy systems. IEEE Trans Cybern PP(99):1–12

43.

Pan J, Zhang B, Liu F, Liu F (2016) On local nonquadratic tracking control for continuous-time T-S fuzzy systems. In: 2016 35th Chinese Control Conference (CCC), Chengdu, pp 3793–3798

44.

Shengjuan H, Yang GH (2016) Input-output based fault estimation for T-S fuzzy systems with local nonlinear parts. Int J Robust Nonlinear Control 26:3611–3630. https://doi.org/10.1002/rnc.3523CrossRef

45.

Zhong Z, Wai RJ, Shao Z, Xu M (2016) Reachable set estimation and decentralized controller design for large-scale nonlinear systems with time-varying delay and input constraint. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/tfuzz.2016.2617366CrossRef

46.

Wang R, Yin G, Jin X (2016) Robust adaptive sliding mode control for nonlinear four-wheel steering autonomous Vehicles path tracking systems. In: 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), Hefei, pp 2999–3006

47.

Zayani H, Allouche M, Kharrat M, Chaabane M (2015) T-S fuzzy maximum power point tracking control of photovoltaic conversion system. In: 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, 534–539

48.

Huang C, Du H, Naghdy F, Li W (2015) Takagi-sugeno fuzzy H∞ tracking control for steer-by-wire systems. In: 2015 IEEE Conference on Control Applications (CCA), Sydney, NSW, pp 1716–1721

49.

Yin Y, Zheng X, Yang H, Zhao X (2015) Stabilization for delayed positive nonlinear systems via T-S fuzzy modeling. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Shenyang, pp 1687–1692

50.

Saroj DK, Kar I (2013) T-S fuzzy model based controller and observer design for a Twin Rotor MIMO System. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Hyderabad, pp 1–8

51.

Ben Hamouda L, Ayadi M, Langlois N (2016) Fault tolerant fuzzy-based model predictive controllers for automotive application. In: 2016 International Conference on Control, Decision and Information Technologies (CoDIT), St. Julian’s, pp 117–122

52.

Ben Hamouda L, Bennouna O, Ayadi M, Langlois N (2014) Quadratic stability and LMIs for tolerance to faults: Fuzzy model predictive control. In: 2014 18th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, pp 387–392

53.

Du H, Li W, Zhang Y (2015) Tracking control of wheel slip ratio with velocity estimation for vehicle anti-lock braking system. In: The 27th Chinese Control and Decision Conference, Qingdao, pp 1900–1905

54.

Du H, Li W (2014) Model-based Takagi-Sugeno fuzzy approach for vehicle longitudinal velocity estimation during braking. In: 2014 IEEE International Conference on Fuzzy Systems, Beijing, pp. 1851–1858

55.

Rastegar S, Araújo R, Mendes J, Matias T, Emami A (2014) Self-adaptive Takagi-Sugeno model identification methodology for industrial control processes. In: IECON 2014—40th Annual Conference of the IEEE Industrial Electronics Society, Dallas, TX, pp 281–287

56.

Chaturvedi KT, Pandit M, Srivastava L (2008) Self-organizing hierarchical particle swarm optimization for non convex economic dispatch. IEEE Trans Power Syst 23(3):1079–1087CrossRef

57.

Song Y, Chen Z, Yuan Z (2007) New chaotic PSO-based neural network predictive control for nonlinear process. IEEE Trans Neural Netw 18(2):595–600CrossRef

58.

Li Y, Shen J, Lee KY, Liu X (2012) Offset-free fuzzy model predictive control of a boiler-turbine system based on genetic algorithm. Simul Model Pract Theory 26:77–95CrossRef

59.

Martins MAF, Zanin AC, Odloak D (2014) Robust model predictive control of an industrial partial combustion fluidized-bed catalytic cracking converter. Chem Eng Res Des 92(5):917–930CrossRef

60.

Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11(7):733–764CrossRef

Titel: Binary particle swarm optimization-based T-S fuzzy predictive controller for nonlinear automotive application
verfasst von: Elsaid Md. Abdelrahim
Publikationsdatum: 13.07.2020
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05132-x

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