Gain-scheduling robust control for a tire-blow-out road vehicle

2018 ◽  
Vol 233 (2) ◽  
pp. 344-362 ◽  
Author(s):  
Houhua Jing ◽  
Zhiyuan Liu
Keyword(s):  
Robust Control ◽  
Gain Scheduling ◽  
Front Wheel ◽  
Testing System ◽  
Road Vehicle ◽  
Control Approach ◽  
Yaw Control ◽  
Control Moment ◽  
Vehicle Velocity ◽  
Driving Stability

This paper presents a robust control approach to keeping directional and driving stability for a road vehicle after a tire blow-out. Considering the time-varying vehicle velocity as well as the uncertain tire characteristics, a linear parameter varying vehicle model is built. With front wheel steering angle and yaw control moment as control inputs, a gain-scheduling H∞ controller is developed to attenuate the effects of a flat tire. An optimal control allocation law is presented to perform the yaw control moment by differential braking on the other three tires. Finally, a hardware-in-the-loop testing system, composed of the veDYNA high-fidelity software program and an actual automotive hydraulic braking system, is utilized for controller validation. The results clearly demonstrate the effectiveness of the proposed coordinated controller in improving vehicle directional stability and robustness against the disturbances caused by a tire blow-out.

A Robust Control Approach for Primary Frequency Regulation through Variable Speed Wind Turbines

2010 ◽  
Vol 130 (11) ◽  
pp. 1002-1009 ◽  
Author(s):  
Jorge Morel ◽  
Hassan Bevrani ◽  
Teruhiko Ishii ◽  
Takashi Hiyama
Keyword(s):  
Robust Control ◽  
Wind Turbines ◽  
Frequency Regulation ◽  
Variable Speed ◽  
Control Approach ◽  
Primary Frequency

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

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