Robust predictive control of wheel slip in antilock braking systems based on radial basis function neural network

Abstract Anti-Lock Braking System (ABS) is a well-known technology for vehicle safety enhancement during hard braking. The wheel slip control has been a challenging problem due to a complex behavior of the tire and strong nonlinearity in a braking process. Furthermore, the system is subjected to unknown uncertainties that would arise from changing the vehicle parameters and un-model dynamics. Thus, it is required to design a nonlinear robust control law for ABS to overcome these problems. In this paper, a novel robust prediction-based controller for ABS is proposed that guarantees the stability against uncertainties. An optimal control law is firstly designed for ABS using nonlinear predictive method. Then, the unknown uncertainties are adaptively approximated utilizing a radial basis function neural network (RBFNN). The Lyapunov approach is employed to develop an update control law to determine the network weights. Finally, some simulations are conducted to examine the performance of the proposed control system for tracking the reference wheel slip in the presence of uncertainties in different maneuvers. Also, the performance of the proposed controller is compared with the conventional sliding mode controller (SMC) through simulation results.

[1]  Dragan Aleksendrić,et al.  Neural network prediction of disc brake performance , 2009 .

[2]  Afzal Suleman,et al.  A performance evaluation of an automotive magnetorheological brake design with a sliding mode controller , 2006 .

[3]  Hao Yu,et al.  Advantages of Radial Basis Function Networks for Dynamic System Design , 2011, IEEE Transactions on Industrial Electronics.

[4]  Yong Zhang,et al.  Controller design for vehicle stability enhancement , 2006 .

[5]  Marko Robnik-Sikonja.  Data Generators for Learning Systems Based on RBF Networks , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[7]  Mehdi Mirzaei,et al.  A novel method for non-linear control of wheel slip in anti-lock braking systems , 2010 .

[8]  Hasan Komurcugil,et al.  Optimized Sliding Mode Control to Maximize Existence Region for Single-Phase Dynamic Voltage Restorers , 2016, IEEE Transactions on Industrial Informatics.

[9]  Ning Wang,et al.  Cuckoo search algorithm with membrane communication mechanism for modeling overhead crane systems using RBF neural networks , 2017, Appl. Soft Comput..

[10]  Tor Arne Johansen,et al.  Gain-scheduled wheel slip control in automotive brake systems , 2003, IEEE Trans. Control. Syst. Technol..

[11]  Dragan Aleksendric,et al.  Adaptive neuro-fuzzy wheel slip control , 2013, Expert Syst. Appl..

[12]  Chia-Ju Wu,et al.  Radial basis function networks with hybrid learning for system identification with outliers , 2011, Appl. Soft Comput..

[13]  Mehdi Mirzaei,et al.  Enhancement of vehicle braking performance on split-μ roads using optimal integrated control of steering and braking systems , 2016 .

[14]  John M. Starkey,et al.  EFFECTS OF MODEL COMPLEXITY ON THE PERFORMANCE OF AUTOMATED VEHICLE STEERING CONTROLLERS : MODEL DEVELOPMENT, VALIDATION AND COMPARISON , 1995 .

[15]  Mehdi Mirzaei,et al.  Optimization of nonlinear control strategy for anti-lock braking system with improvement of vehicle directional stability on split-μ roads , 2014 .

[16]  Mingzhe Liu,et al.  Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm , 2015, Neurocomputing.

[17]  Haralambos Sarimveis,et al.  Cooperative learning for radial basis function networks using particle swarm optimization , 2016, Appl. Soft Comput..

[18]  Bilin Aksun Güvenç,et al.  Extremum-Seeking Control of ABS Braking in Road Vehicles With Lateral Force Improvement , 2014, IEEE Transactions on Control Systems Technology.

[19]  Samarjit Kar,et al.  Applications of neuro fuzzy systems: A brief review and future outline , 2014, Appl. Soft Comput..

[20]  Luis Martinez-Salamero,et al.  Sliding-Mode-Control-Based Boost Converter for High-Voltage–Low-Power Applications , 2015, IEEE Transactions on Industrial Electronics.

[21]  Ho Gi Jung,et al.  A neural network approach to target classification for active safety system using microwave radar , 2010, Expert Syst. Appl..

[22]  Mehdi Mirzaei,et al.  Fuzzy Scheduled Optimal Control of Integrated Vehicle Braking and Steering Systems , 2017, IEEE/ASME Transactions on Mechatronics.

[23]  Radu-Emil Precup,et al.  Model-free sliding mode control of nonlinear systems: Algorithms and experiments , 2017, Inf. Sci..

[24]  Stephen J. Wright,et al.  Numerical Optimization (Springer Series in Operations Research and Financial Engineering) , 2000 .

[25]  Chun-Fei Hsu.  Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems , 2015, Neural Computing and Applications.

[26]  Antonella Ferrara,et al.  Switched/time-based adaptation for second-order sliding mode control , 2016, Autom..

[27]  Zivana Jakovljevic,et al.  Intelligent control of braking process , 2012, Expert Syst. Appl..

[28]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[29]  Ying Wang,et al.  Adaptive Fuzzy Fractional-Order Sliding Mode Controller Design for Antilock Braking Systems , 2016 .

[30]  Peter J. Gawthrop,et al.  Optimal control of nonlinear systems: a predictive control approach , 2003, Autom..

[31]  Jo Yung Wong,et al.  Theory of ground vehicles , 1978 .

[32]  Michael E. Fitzpatrick,et al.  Optimal design of a quadratic parameter varying vehicle suspension system using contrast-based Fruit Fly Optimisation , 2018, Appl. Soft Comput..

[33]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[34]  Abdel Badie Sharkawy,et al.  Genetic fuzzy self-tuning PID controllers for antilock braking systems , 2010, Eng. Appl. Artif. Intell..

[35]  Xiaogeng Liang,et al.  Backstepping dynamic surface control for an anti-skid braking system , 2015 .

[36]  Chih-Min Lin,et al.  Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems , 2013, Appl. Soft Comput..

[37]  Radu-Emil Precup,et al.  Nature-inspired optimal tuning of input membership functions of Takagi-Sugeno-Kang fuzzy models for Anti-lock Braking Systems , 2015, Appl. Soft Comput..

[38]  Chuan-Kai Lin.  Radial basis function neural network-based adaptive critic control of induction motors , 2011, Appl. Soft Comput..

[39]  Chun-Fei Hsu,et al.  Adaptive dynamic RBF neural controller design for a class of nonlinear systems , 2011, Appl. Soft Comput..

[40]  Rui Esteves Araujo,et al.  Wheel Slip Control of EVs Based on Sliding Mode Technique With Conditional Integrators , 2013, IEEE Transactions on Industrial Electronics.

[41]  Sultan Noman Qasem,et al.  Author's Personal Copy Applied Soft Computing Radial Basis Function Network Based on Time Variant Multi-objective Particle Swarm Optimization for Medical Diseases Diagnosis , 2022 .

[42]  Minqiang Li,et al.  Learning Subspace-Based RBFNN Using Coevolutionary Algorithm for Complex Classification Tasks , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[43]  Arthur E. Bryson,et al.  Applied Optimal Control , 1969 .

[44]  Hong Chen,et al.  Approximation capability to functions of several variables, nonlinear functionals, and operators by radial basis function neural networks , 1993, IEEE Trans. Neural Networks.

[45]  Carlo Sansone,et al.  Automatically building datasets of labeled IP traffic traces: A self-training approach , 2012, Appl. Soft Comput..

[46]  Cao Binggang,et al.  Driving and control of torque for direct-wheel-driven electric vehicle with motors in serial , 2011 .