Random Modeling and Control of Nonlinear Active Suspension

For a quarter car with nonlinear active suspension in rough road, the problem of random modeling and control is considered. According to the relative motion principle, the influence of rough road can be seen as that force is disturbed by the noise and a random model is constructed. By an appropriate transform, the model is transformed into a lower triangular system, which can be used as backstepping method. Then a controller is designed such that the mean square of the state converges to an arbitrarily small neighborhood of zero by tuning design parameters. The simulation results illustrate the effectiveness of the proposed scheme. Therefore, the active suspension system offers better riding comfort and vehicle handing to the passengers

Observer-based robust adaptive control for uncertain stochastic Hamiltonian systems with state and input delays

This paper investigates the observer-based robust adaptive control problem for a class of stochastic Hamiltonian systems. The systems under consideration relate to parameter uncertainties, unknown state time-delay and input delay. The purpose is to design a delay-dependent observer-based adaptive control law such that for all admissible uncertainties, as well as stochasticity, the closed-loop error system is robustly asymptotically stable in the mean square. Several sufficient conditions are presented to ensure the rationality and validity of the proposed control laws and observers, which are derived based on Lyapunov functional method. Numerical simulations spell out to illustrate the effectiveness of the proposed theories

Modeling and adaptive tracking for stochastic nonholonomic constrained mechanical systems

This paper is devoted to the problem of modeling and trajectory tracking for stochastic nonholonomic dynamic systems in the presence of unknown parameters. Prior to tracking controller design, the rigorous derivation of stochastic nonholonomic dynamic model is given. By reasonably introducing so-called internal state vector, a reduced dynamic model, which is suitable for control design, is proposed. Based on the backstepping technique in vector form, an adaptive tracking controller is then derived, guaranteeing that the mean square of the tracking error converges to an arbitrarily small neighborhood of zero by tuning design parameters. The efficiency of the controller is demonstrated by a mechanics system: a vertical mobile wheel in random vibration environment

Random Modeling and Control of Nonlinear Active Suspension

For a quarter car with nonlinear active suspension in rough road, the problem of random modeling and control is considered. According to the relative motion principle, the influence of rough road can be seen as that force is disturbed by the noise and a random model is constructed. By an appropriate transform, the model is transformed into a lower triangular system, which can be used as backstepping method. Then a controller is designed such that the mean square of the state converges to an arbitrarily small neighborhood of zero by tuning design parameters. The simulation results illustrate the effectiveness of the proposed scheme. Therefore, the active suspension system offers better riding comfort and vehicle handing to the passengers

Backstepping control in vector form for stochastic hamiltonian systems

In this paper, the problem of adaptive tracking for a class of stochastic Hamiltonian control systems with unknown drift and diffusion functions is considered. Some difficulties come forth-the integral chain consists of vectors, and control and tracking errors are in different channels- which are rarely considered in the existing references about stochastic nonlinear controls. To resolve these problems, an adaptive backstepping controller in vector form is designed such that the closedloop system has a unique solution that is globally bounded in probability and the fourth moment of the tracking error converges to an arbitrarily small neighborhood of zero. As an application, the modeling and the control for spring pendulum in stochastic surroundings are researched. © 2012 Society for Industrial and Applied Mathematics.Zhaojing Wu, Mingyue Cui, and Peng Sh