Dan-Yong Li

Virtual-Point-Based Fault-Tolerant Lateral and Longitudinal Control of 4W-Steering Vehicles

This paper studies the lateral and longitudinal path tracking control of four-wheel steering autonomous vehicles. A robust and adaptive fault-tolerant tracking control strategy is proposed to simultaneously counteract modeling uncertainties, unexpected disturbances, coupling effects, as well as actuator failures. By introducing the virtual points along the longitudinal centerline of the vehicle and utilizing a state transformation, a special feature of the control gain matrix is revealed, which allows for the development of structurally simple and computationally inexpensive robust adaptive and fault-tolerant control algorithms. The closed-loop stability issues of the control scheme are analyzed using a Lyapunov-based method. A nonlinear dynamic model of a passenger vehicle is developed to simulate the performance of control design. The controller is tested and validated via computer simulations in the presence of parametric uncertainties and varying driving conditions.

Neuroadaptive Variable Speed Control of Wind Turbine With Wind Speed Estimation

It is difficult to measure the wind speed accurately in short term. This reveals challenges for wind turbine control, especially for maximum power point tracking with adaptive control strategies. In this paper, a genetic algorithm based support vector machine model is adopted to estimate the wind speed, using physically measurable signals, such as the electrical power, pitch angle, and rotor speed, while the desired rotor speed can be obtained accordingly. Further, by combining the radial basis function neural networks with adaptive algorithms, a novel virtual parameter based neuroadaptive controller is developed to accommodate the system uncertain and external disturbances. The effectiveness and performances of the proposed method are validated and demonstrated with FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and Simulink.

A Novel Approach for Active Adhesion Control of High-Speed Trains Under Antiskid Constraints

Wheel skid is highly undesirable because it could endanger the safe operation of high-speed trains. How to avoid excessive wheel skid via an active adhesion control method represents an interesting and challenging topic of research. In this work, we first introduce the conditions of antiskid operation and formulate it as a constrained tracking control problem, based on which two model-based antiskid slip velocity control laws are developed. Then, by applying two adaptive force observers to estimate the unknown and varying adhesion force and resistance, we develop an adaptive antiskid adhesion control scheme. The novelty of the proposed method is that control errors of the closed-loop system are used to online update the observer parameters, such that the predefined control precision can be ensured with the proposed observer-based adhesion control. To deal with the constrained antiskid control, a barrier Lyapunov function is constructed, and the effectiveness of the proposed control scheme is theoretically authenticated with confirmation by numerical simulation.

Neuro-Adaptive Fault-Tolerant Approach for Active Suspension Control of High-Speed Trains

Excessive lateral and roll motions of a high-speed train might endanger its operational safety. This paper investigates how to suppress those motions via an active-suspension method. By exploiting the structural properties of the system model and the triangular control gain, a new control scheme capable of attenuating immeasurable disturbances, compensating modeling uncertainties, and accommodating actuation faults is developed. Compared with most existing methods, the proposed method does not require precise information on the suspension parameters and the detail system model. Moreover, the magnitude of the actuation fault and the time instant at which the actuation fault occurs are not needed in setting up and implementing the proposed control scheme. The controller is tested and validated via computer simulations in the presence of parametric uncertainties and varying operation conditions.

Fault-tolerant tracking control of FW-steering autonomous vehicles

This paper studies the path tracking control of four-wheel steering autonomous vehicles. A robust and adaptive fault-tolerant tracking control strategy is proposed to simultaneously counteract modeling uncertainties, unexpected disturbances, coupling effects, as well as actuator failures. By exploiting a state transformation, together with the introduction of virtual points in the longitudinal centerline of the vehicle, a special feature of the control gain matrix is revealed, which allows for the development of structurally simple and computationally inexpensive robust and adaptive control algorithms. The closed-loop stability issues of the control scheme are analyzed using a Lyapunov-based method. A complex nonlinear dynamic model of a passenger vehicle is developed to simulate the dynamic motion performance and for controller design. The controller is tested and verified via computer simulations in the presence of parametric uncertainties and severe driving conditions.

Adaptive Fault-Tolerant Control of Wind Turbines With Guaranteed Transient Performance Considering Active Power Control of Wind Farms

As high-order nonlinear large-scale systems, wind farms composed of multiple wind turbines (WTs) need to adopt active power control (APC) to track the power set points, rather than the maximum power points. In this paper, the proportional distribution strategy is utilized to specify the power set point according to the available output power of each WT based on the ultra-short-term wind speed prediction. Then, we convert the APC problem into the rotor speed tracking control problem, and a robust adaptive fault-tolerant control approach based on the barrier Lyapunov function is developed to track the desired power signal of each WT with guaranteed transient performance and robustness to actuator faults. The effectiveness and the merit of the proposed approach are validated by applying it to the APC of a wind farm.

Tracking control of nonaffine systems using bio-inspired networks with auto-tuning activation functions and self-growing neurons

This paper presents a bio-inspired artificial neural network (Bio-ANN) to tackle the tracking control of complex dynamic systems. The proposed Bio-ANN is motivated by the operant conditioning of biological systems, in which we not only adaptively tune the weights but also adjust the structural parameter of basis functions automatically, significantly enhancing the learning capability of the proposed control. Furthermore, the size of the dataset needed for online ANN training is small and the overall computational cost is low. With the help of such Bio-ANN, we develop a control scheme for a class of single-input single-output non-affine systems, where the operant conditioning bionic model (OCBM) is utilized. By comparing the proposed method with existing self-organizing approaches via numerical simulations, we verify that a faster convergent rate is achieved with better control precision by using the proposed OCBM based control approach.

Uniform Rolling-Wear-Based Robust Adaptive Control of High-Speed Trains in the Presence of Actuator Differences

Small persistent differences of slip velocities due to different actuation effectiveness of driving motors/braking units may lead to severe nonuniform rolling wear or fatigue damage of a part of actuation wheelsets after long-term operation, which would shorten service life or even endanger operational safety of the high-speed train. How to eliminate the nonuniform rolling wear/fatigue damage of actuation wheelsets using the control method is a very interesting and challenging issue. In this paper, robust adaptive observers are developed to identify uncertain dynamics of the train body and actuation wheelsets, based on which a uniform rolling-wear-based traction/braking control scheme is established. It is shown that with this controller, not only the common objective of traction/braking operation is achieved, but also actuator differences are completely compensated such that the same slip velocity (implying uniform driving load and rolling wear) is ensured for all actuation wheelsets during long-term operation. Both theoretical analysis and numerical simulations validate the effectiveness of the proposed control method.

A Note on “Model-Independent Adaptive Fault-Tolerant Output Tracking Control of 4WS4WD Road Vehicles”

This paper investigates the path-tracking control problem of four-wheel-steering and four-wheel-driving (4WS4WD) road vehicles. Of particular interest is the development of an adaptive and fault-tolerant tracking control scheme capable of compensating vehicle uncertain dynamics/disturbances and actuation failures simultaneously. Control algorithms are derived without requiring detail system dynamic information. The control scheme is shown to be effective in coping with unexpected actuation faults without the need for analytically estimating bound on actuator failure variables. The proposed method is validated and demonstrated through its application to a wheeled vehicle with four steering wheels and four driving wheels, where high-precision path tracking is achieved in the face of steering faults.