Robust fuzzy delayed sampled-data control for nonlinear active suspension systems with varying vehicle load and frequency-domain constraint

Abstract

This article studies the Takagi-Sugeno (T-S) fuzzy delayed sampled-data $$\mathcal {H}_\infty $$ H ∞ control problem for a class of intelligent suspension systems with varying vehicle load and frequency-domain constraint. The T-S fuzzy model is utilized to characterize the varying vehicle load. Considering the transmission delay, a robust fuzzy delayed sampled-data control mechanism is newly propounded for suspension systems. According to the Lyapunov stability theory, a useful theorem is proposed based on the Wirtinger inequality to guarantee the states of the active suspension system being asymptotically stabilized with the required $$\mathcal {H}_\infty $$ H ∞ performance by the proposed controller. Moreover, the suspension constrained requirements are also satisfied within the finite frequency domain. Finally, simulation examples are presented to attest to the usefulness and superiority of the proposed controller.

Dissipative-based sampled-data control for T-S fuzzy wind turbine system via fragmented-delayed state looped functional approach

“…Remark 8 It should be noted that the dissipative-based sampled-data control mechanism presented in this study is general and can be implemented in other practical systems, as well as active vehicle suspension systems [22], truck trailer models [35], multi-machine power systems [36], basic buck converters system [37] and so on. The variable speed WTS under investigation is one possible practical system in engineering.…”

“…To acquire larger sampling intervals, many researchers have been used various methods in recent works. To be specific, continuous time Lyapunov functional (CTLF) [20], discontinuous time Lyapunov functional (DTLF) [21], loopedtype Lyapunov functional (LTLF) [22] and developing some new integral inequalities for estimating sampling integrator terms [23,24]. For instance, the CTLF consists of the quadratic term V (t) = z T (t)P 1 z(t) and sampling integrator term W (t) = λ 2 (t) t t k z T (ǫ)P 2 z(ǫ)dǫ with P 1 , P 2 ≥ 0, while the DTLF consists of the quadra tic term V (t), sampling integrator term W (t), and discontinuous term W D (t) with W D (t) ≥ 0 and W D (t k ) = 0, k = 1, 2, ... for t k ≤ t ≤ t k+1 , where t k+1 and t k are sampling instants.…”

Model Predictive Control for Speed-Dependent Active Suspension System with Road Preview Information

“…However, most studies have only considered robust control strategies. In [21], the influence of the suspension system on the parameters of the spring-loaded mass with uncertainty is overcome by H ∞ -robust control, but the obvious disadvantage of this type of robust approach is that it is overly conservative, as the controller output must always consider the worst-case optimal solution. In [22], the variation in the model parameters due to changes in vehicle speed during actual vehicle driving is considered.…”

Dissipative Control for Nonlinear Singularly Perturbed Systems with Dynamic Quantization and Actuator Failure

“…Takagi and Sugeno proposed the famous T-S fuzzy model, whose principle is that the local nonlinear dynamics under each rule are represented by linear models, and then membership functions are used to approximate nonlinear systems by combining convex groups of these linear models [4]. As an effective method to deal with nonlinear systems, T-S fuzzy model is widely used in a variety of nonlinear systems [5][6][7][8][9][10][11].…”