Abstract
This paper is concerned with the problem of actuator fault estimation (FE) for vehicle active suspension systems. First, the fast FE approach, which combines the output error term with its derivative term in the FE algorithm, is extended to the active suspension system with actuator fault and external disturbance input. Then, considering three typical kinds of actuator faults, i.e., constant gain change fault, drift fault, and stuck fault, genetic algorithm (GA) is employed to optimize the adjustable parameters in the FE algorithm, which are usually determined by trials. Finally, simulation results of FE and fault‐tolerant control (FTC) are presented to illustrate the effectiveness and applicability of the proposed FE method.
Highlights
Vehicle suspensions play a significant role in ride comfort, safety, and the overall performance of modern vehicles
According to different control forms, vehicle suspensions are commonly classified into three types: passive, semiactive, and active suspensions. e passive suspension composed of a parallel spring and damper which has been acknowledged for its simple structure and low cost [1]. e semiactive suspension [2, 3], which consists of a spring and tunable damper, can make desirable improvements over the passive one by effectively varying the rate of the energy dissipation
In this paper, motivated by the above analysis, the fault estimation (FE) problem is studied for an active suspension system based on the parameter optimization technique. e main contributions of this paper can be summarized as follows: (1) the fast FE approach, which combines the output error term with its derivative term in the FE algorithm [26], is extended to the active suspension system with the actuator faults and external disturbance input; (2) in order to improve the FE performance, genetic algorithm (GA) is used to optimize the adjustable parameters in the FE algorithm, which are usually determined by trials
Summary
Vehicle suspensions play a significant role in ride comfort, safety, and the overall performance of modern vehicles. Compared with passive and semiactive suspensions, active suspensions are not widely used due to large energy demand and complex structure They can provide high control performance in a wide frequency range and have the best potential to overcome conflicts between ride comfort and vehicle safety. E main contributions of this paper can be summarized as follows: (1) the fast FE approach, which combines the output error term with its derivative term in the FE algorithm [26], is extended to the active suspension system with the actuator faults and external disturbance input; (2) in order to improve the FE performance, genetic algorithm (GA) is used to optimize the adjustable parameters in the FE algorithm, which are usually determined by trials. If not explicitly stated, are assumed to have compatible dimensions for algebraic operations
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