Abstract
In this paper, active suspension system is selected as the research object. Back-stepping method is adopted, and virtual fault tolerant controller, main fault tolerant controller and adaptive law are designed by constructing Time-varying Barrier Lyapunov Function (TVBLF), to ensure that the displacement and speed of the vehicle do not violate the constraint boundary and achieve the goal of fast fault tolerance. For the unknown continuous function caused by the uncertain body mass and other factors in the system, Radial Basis Function Neural Network (RBFNN) is used for approximation. At the same time, the suspension space limitation and tire dynamic load are analyzed. Finally, the effectiveness of the proposed fault-tolerant control method is verified by simulation of deviation fault and partial failure fault.
Highlights
With the advancement of science and technology, the improvement of people’s living standards and the rapid development of the automobile manufacturing industry, vehicles as a means of transportation have gradually entered thousands of households
In order to overcome the shortcomings of passive suspension systems, automotive manufacturers designed and manufactured active suspension systems which consists of the elastic and damping components, sensors, and actuators
In order to realize the fault tolerance of the active suspension system, this paper introduces the time-varying Barrier Lyapunov function to design a fault-tolerant control scheme with time-varying constraints
Summary
With the advancement of science and technology, the improvement of people’s living standards and the rapid development of the automobile manufacturing industry, vehicles as a means of transportation have gradually entered thousands of households. There are many methods to control the active suspension system, including linear quadratic optimal control [7], [8], PID control [9], [10], adaptive control [11]–[16], neural network control [17]–[20], [29], and sliding mode variable structure control [21]–[23] These suspension control methods can improve the ride comfort and ride stability of the vehicle. Other fault tolerant control methods for suspension systems were proposed based on Linear Parameter Change (LPV) [34], neural network identification [35], and Takagi-Sugeno (T-S) fuzzy models [36]. The suspension space limitation and tire dynamic load are analyzed, and the effectiveness of the proposed fault-tolerant control method is verified by simulation of deviation fault and partial failure fault
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