Hai Wang

Robust sliding mode control for Steer-by-Wire systems with AC motors in road vehicles

In this paper, the modeling of steer-by-wire (SbW) systems is further studied, and a sliding mode control scheme for the SbW systems with uncertain dynamics is developed. It is shown that an SbW system, from the steering motor to the steered front wheels, is equivalent to a second-order system. A sliding mode controller can then be designed based on the bound information of uncertain system parameters, uncertain self-aligning torque, and uncertain torque pulsation disturbances, in the sense that not only the strong robustness with respect to large and nonlinear system uncertainties can be obtained but also the front-wheel steering angle can converge to the handwheel reference angle asymptotically. Both the simulation and experimental results are presented in support of the excellent performance and effectiveness of the proposed scheme.

Robust Motion Control of a Linear Motor Positioner Using Fast Nonsingular Terminal Sliding Mode

A robust motion control system is essential for the linear motor (LM)-based direct drive to provide high speed and high-precision performance. This paper studies a systematic control design method using fast nonsingular terminal sliding mode (FNTSM) for an LM positioner. Compared with the conventional nonsingular terminal sliding mode control, the FNTSM control can guarantee a faster convergence rate of the tracking error in the presence of system uncertainties including payload variations, friction, external disturbances, and measurement noises. Moreover, its control input is inherently continuous, which accordingly avoids the undesired control chattering problem. We further discuss the selection criteria of the controller parameters for the LM to deal with the system dynamic constraints and performance tradeoffs. Finally, we present a robust model-free velocity estimator based on the only available position measurements with quantization noises such that the estimated velocity can be used for feedback signal to the FNTSM controller. Experimental results demonstrate the practical implementation of the FNTSM controller and verify its robustness of more accurate tracking and faster disturbance rejection compared with a conventional NTSM controller and a linear H∞ controller.

Robust Sliding Mode-Based Learning Control for Steer-by-Wire Systems in Modern Vehicles

In this paper, a robust sliding mode learning control (SMLC) scheme is developed for steer-by-wire (SbW) systems. It is shown that an SbW system with uncertain system parameters and unknown external disturbance from the interactions between the tires and the variable road surface can be modeled as a second-order system. A sliding mode learning controller can then be designed to drive both the sliding variable and the tracking error between the steered front-wheel angle and the hand-wheel reference angle to asymptotically converge to zero. The proposed SMLC scheme exhibits many advantages over the existing schemes, including: 1) no information about vehicle parameter uncertainties and self-aligning torque variations is required for controller design; and 2) the control algorithm is capable of efficiently adjusting the closed-loop response based on the most recent history of the closed-loop stability and ensuring a robust steering performance. Both simulations and experiments are presented to show the excellent steering performance and the effectiveness of the proposed learning control methodology.

Robust control for steer-by-wire systems with partially known dynamics

In this paper, a robust control scheme (RCS) for Steer-by-Wire (SbW) systems with partially known dynamics is proposed. It is shown that an SbW system can be represented by a nominal model and an unknown portion. A nominal feedback controller can then be used to stabilize the nominal model and a sliding mode compensator (SMC) is designed to remove the effects of both the unknown system dynamics and uncertain road conditions on the steering performance. For practical consideration, robust exact differentiator (RED) technique is utilized to estimate the derivatives of the position signals for controller design. It is further shown that the designed RCS is able to guarantee a robust steering performance against system and road uncertainties. The comparative experimental studies are given to verify the excellent performance of the proposed RCS for SbW systems.

Neural-network-based robust control for steer-by-wire systems with uncertain dynamics

AbstractThis study develops a neural-network-based robust control scheme for steer-by-wire systems with uncertain dynamics.n The proposed control consists of a nominal control and a nonsingular terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the effects of uncertainties can be effectively eliminated in the closed-loop system. Using the proposed neural control scheme, not only the robust steering performance against parameter variations and road disturbances is obtained, but also both the control gain and the control design complexity are greatly reduced due to the use of the RBFNN. Simulation results are demonstrated to verify the superior control performance of the proposed control scheme, in comparison with other control strategies.

Terminal sliding mode control for steer-by-wire system in electric vehicles

In this paper, the mathematical modelling of a front-wheel steer-by-wire (SBW) system is studied and a terminal sliding mode control scheme for the SBW system with uncertain dynamics is developed. It is shown that a front-wheel SBW system, from the hand-wheel to the steered front wheels, is equivalent to a second-order system. A terminal sliding mode controller can then be designed based on the information of the bounds of uncertain system dynamics, in the sense that not only a strong robustness with respect to system uncertainties and nonlinearities can be obtained, but also the front wheel steering angle can converge to the hand-wheel reference angle in a finite time. A simulation example is presented to verify the proposed control scheme.

A recurrent neural network for modeling crack growth of aluminium alloy

AbstractnA new recurrent neural model for crack growth process of aluminium alloy is developed in this work. It is shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The output feedback loops in the neural model play the role of capturing the fine changes of crack growth dynamics. The Extreme Learning Machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable-amplitude loading can be accurately modeled. The simulation results with the averaged experimental data of the 2024-T351 aluminium alloy show that the excellent modeling and prediction performance of the recurrent neural model can be achieved for fatigue crack growth of aluminium alloys.

Adaptive neural network sliding mode control for steer-by-wire-based vehicle stability control

This study develops a novel vehicle stability control (VSC) scheme using adaptive neural network sliding mode control technique for Steer-by-Wire (SbW) equipped vehicles. The VSC scheme is designed in two stages, i.e., the upper and lower level control stages. An adaptive sliding mode yaw rate controller is first proposed as the upper one to design the compensated steering angle for enabling the actual yaw rate to closely follow the desired one. Then, in the implementation of the yaw control system, the developed steering controller consists of a nominal control and a terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the actual front wheel steering angle can be driven to track the commanded angle in a finite time. The proposed novel stability control scheme possesses the following prominent superiorities over the existing ones: (i) No prior parameter information on the vehicle and tyre dynamics is required in stability control, which greatly reduces the complexity of the stability control structure. (ii) The robust stability control performance against parameter variations and road disturbances is obtained by means of ensuring the good tracking performance of yaw rate and steering angle and the strong robustness with respect to large and nonlinear system uncertainties. Simulation results are demonstrated to verify the superior control performance of the proposed VSC scheme.

Sliding mode based learning control for interconnected systems

This paper proposes a novel sliding mode based learning control for a class of interconnected systems. The Takagi-Sugeno (T-S) fuzzy modelling technique has been employed to model the interconnected subsystems. Then, based on the fuzzy models, sliding mode learning controllers have been developed to ensure that the closed-loop system is globally asymptotically stable with good tracking performance. Each sliding mode learning controller consists of a most recent control signal and a correction term. The correction term is designed to improve the most recent control signal by incorporating sliding variable information into the control of the subsequent iteration. In addition, a Lipschitz-like condition has been proposed to replace the requirement of information on the upper and the lower bounds of system parameters from the conventional sliding mode control. It differs significantly from the conventional parallel distributed compensation (PDC) in that it does not involve the problem of solving the linear matrix inequality (LMI) for the design of the learning controllers. A simulation example is presented to demonstrate the effectiveness of our proposed control scheme.