Multi-Objective Robust Control for Vehicle Active Suspension Systems via Parameterized Controller

Abstract

A parameterized controller design approach is proposed to solve the problem of multiobjective control for vehicle active suspension systems by using symbolic computation. The considered model is a quarter-vehicle model of the active suspension system. The multi-objective robust control performances include the sprung mass acceleration, suspension deflection, and tire deflection. Based on dissipative Hamiltonian systems and Lyapunov function, a multi-objective H ∞ controller design approach is developed, which can avoid solving Hamilton-Jacobi-Issacs equations. Then, an algorithm of solving semi-positive definite polynomial with tuning parameters is proposed by using symbolic computation. Furthermore, a method of parameter optimization is proposed. Simulations and comparation show that the control performance is significantly improved comparing with passive controlled systems and existing other control systems for active suspension systems. INDEX TERMS Multi-objective control, active suspension system, parameterized controller, symbolic computation, dissipative Hamiltonian systems.

Pareto optimality based PID controller design for vehicle active suspension system using grasshopper optimization algorithm

“…(10) and ( 11) is modeled by the sinusoidal equations given by Eqs. (18) and (19). The performance improvement shown in Figs.…”

“…Utkarsh et al [17] proposed a sliding mode control technique based on linear disturbance observer for active suspension systems with nonideal actuators. Cao et al [18] proposed a multiobjective H-infinity parameterized controller using Lyapunov and symbolic computation for vehicle active suspension systems. Na et al [19] developed a novel control technique for active suspension systems with unknown nonlinearities using suspension error for full-car active suspension systems with unknown nonlinearities without function approximation.…”

A reinforcement learning backstepping‐based control design for a full vehicle active Macpherson suspension system

“…Therefore, active suspension systems have been admitted its outstanding performance in comfort and vehicle handling to the passengers. In the previous researches, a number of control strategies for AS have been proposed to improve the suspension performance, such as fuzzy neural network control [22][23][24], H ∞ control [25][26][27], model predictive control [28], linear quadratic Gaussian (LQG) control [29], sliding mode control (SMC) [30][31][32], robust control [33,34] etc. Wang et al [28] introduced a robust model predictive controller (RMPC) for a seven DoFs AS system model.…”

Enhancing the Braking Performance of a Vehicle Through the Proper Control of the Active Suspension System

“…the distribution of the vehicle's weight, actuator faults, etc. ), several control methods have been proposed based on the robust control [12], [13], fault tolerance control [14], adaptive dynamic surface control [15], adaptive neural networks [16], [17], and fuzzy-logic control [18], [19]. The common goal of the aforementioned control methods is the reduction of the vertical acceleration of the sprung mass of the vehicle, by regulating the actuator force.…”