New hybrid optimal controller applied to a vibration control system subjected to severe disturbances

Abstract This study presents a new hybrid optimal controller to enhance the robustness and stability of dynamic systems subjected to uncertainties and external disturbances. In the formulation of the proposed control system, the Bolza–Meyer (BM in short) criterion of the optimal controller is modified to meet the system variation, and the sliding-mode controller is modified to obtain the classical control property with the prescribed performance. The proposed controller also includes the H-infinity technique as a bridge for connecting the sub-controls and improving the robust performance of the system. Fuzzy model are used as filters to choose the optimal values for the next calculation. Hence, many advantages of fuzzy model are acquired, related to optimal control, sliding mode control, prescribed performance, and H-infinity techniques. To demonstrate these advantages, the proposed hybrid controller is applied to a vehicle seat suspension for vibration control. Superior control performance over existing hybrid controllers is demonstrated in both time and frequency domains.

[1]  Jerry M. Mendel,et al.  Simplified Interval Type-2 Fuzzy Logic Systems , 2013, IEEE Transactions on Fuzzy Systems.

[2]  Qi Zhou,et al.  Observer-Based Event-Triggered Adaptive Decentralized Fuzzy Control for Nonlinear Large-Scale Systems , 2019, IEEE Transactions on Fuzzy Systems.

[3]  Shuai Li,et al.  Adaptive Near-Optimal Control of Uncertain Systems With Application to Underactuated Surface Vessels , 2018, IEEE Transactions on Control Systems Technology.

[4]  Michael V. Basin,et al.  Optimal controller for uncertain stochastic polynomial systems , 2009, J. Frankl. Inst..

[5]  Seung-Bok Choi,et al.  Design of a new adaptive fuzzy controller and its application to vibration control of a vehicle seat installed with an MR damper , 2015 .

[6]  Frank L. Lewis,et al.  Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances , 2016, IEEE Transactions on Cybernetics.

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

[8]  Frank L. Lewis,et al.  Optimal Output-Feedback Control of Unknown Continuous-Time Linear Systems Using Off-policy Reinforcement Learning , 2016, IEEE Transactions on Cybernetics.

[9]  Chia-Feng Juang,et al.  Data-Driven Interval Type-2 Neural Fuzzy System With High Learning Accuracy and Improved Model Interpretability , 2013, IEEE Transactions on Cybernetics.

[10]  Keck Voon Ling,et al.  Inverse optimal adaptive control for attitude tracking of spacecraft , 2005, IEEE Trans. Autom. Control..

[11]  Zhong-Ping Jiang,et al.  Decentralized Adaptive Optimal Control of Large-Scale Systems With Application to Power Systems , 2015, IEEE Transactions on Industrial Electronics.

[12]  Seung-Bok Choi,et al.  A new composite adaptive controller featuring the neural network and prescribed sliding surface with application to vibration control , 2018, Mechanical Systems and Signal Processing.

[13]  Jerry M. Mendel,et al.  Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems , 2002, IEEE Trans. Fuzzy Syst..

[14]  Derong Liu,et al.  Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[15]  Seung-Bok Choi,et al.  A Novel Adaptive PID Controller with Application to Vibration Control of a Semi-Active Vehicle Seat Suspension , 2017 .

[16]  Jerry M. Mendel,et al.  Computing derivatives in interval type-2 fuzzy logic systems , 2004, IEEE Transactions on Fuzzy Systems.

[17]  Renquan Lu,et al.  Adaptive Neural Network Tracking Control for Robotic Manipulators With Dead Zone , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[18]  Seung-Bok Choi,et al.  Vibration control of a vehicle’s seat suspension featuring a magnetorheological damper based on a new adaptive fuzzy sliding-mode controller , 2016 .

[19]  Hongyi Li,et al.  Adaptive finite-time tracking control of full state constrained nonlinear systems with dead-zone , 2019, Autom..

[20]  Frank L. Lewis,et al.  Adaptive Optimal Control of Unknown Constrained-Input Systems Using Policy Iteration and Neural Networks , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Noriaki Sakamoto.  Optimal control problem via self-adaptation sliding mode controller with neural network , 2011 .

[22]  Seung-Bok Choi,et al.  Damping Force Tracking Control of MR Damper System Using a New Direct Adaptive Fuzzy Controller , 2015 .

[23]  Antonella Ferrara,et al.  Sliding mode optimal control for linear systems , 2012, J. Frankl. Inst..

[24]  John Lygeros,et al.  A Hybrid Optimal Control Approach to Fuel-Efficient Aircraft Conflict Avoidance , 2016, IEEE Transactions on Intelligent Transportation Systems.

[25]  Michael V. Basin,et al.  Sliding mode regulator as solution to optimal control problem for nonlinear polynomial systems , 2009, 2009 American Control Conference.

[26]  A. Germani,et al.  A Luenberger-like observer for nonlinear systems , 1993 .

[27]  Kyriakos G. Vamvoudakis,et al.  Asymptotically Stable Adaptive–Optimal Control Algorithm With Saturating Actuators and Relaxed Persistence of Excitation , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[28]  Xiaodong Liu,et al.  A rapid fuzzy rule clustering method based on granular computing , 2014, Appl. Soft Comput..

[29]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[30]  Seung-Bok Choi,et al.  Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper , 2014 .

[31]  Seung-Bok Choi,et al.  Design of a novel adaptive fuzzy sliding mode controller and application for vibration control of magnetorheological mount , 2014 .

[32]  Seung-Bok Choi,et al.  Vibration control of a ship engine system using high-load magnetorheological mounts associated with a new indirect fuzzy sliding mode controller , 2015 .

[33]  Dongrui Wu,et al.  Approaches for Reducing the Computational Cost of Interval Type-2 Fuzzy Logic Systems: Overview and Comparisons , 2013, IEEE Transactions on Fuzzy Systems.

[34]  Yueying Wang,et al.  A sliding mode approach to stabilization of nonlinear Markovian jump singularly perturbed systems , 2018, Autom..

[35]  Chia-Feng Juang,et al.  A Self-Evolving Interval Type-2 Fuzzy Neural Network With Online Structure and Parameter Learning , 2008, IEEE Transactions on Fuzzy Systems.

[36]  Haytham M. Fayek,et al.  A controller based on Optimal Type-2 Fuzzy Logic: systematic design, optimization and real-time implementation. , 2014, ISA transactions.

[37]  Jerry M. Mendel,et al.  Centroid of a type-2 fuzzy set , 2001, Inf. Sci..