Constrained model predictive control for time-varying delay systems: Application to an active car suspension

Bououden, Sofiane; Chadli, Mohammed; Zhang, Lixian; Yang, Tao

doi:10.1007/s12555-015-2009-4

Cited by 66 publications

(49 citation statements)

References 19 publications

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“…Figure depicts the upper bound of the objective function of the closed‐loop system. As the Figures show, the control performance of the FMPC strategy proposed in this paper is better than the MPC strategy proposed by Bououden et al Figures and depict control inputs of the closed‐loop system. Figure shows the triggering instants of the event‐triggered mechanism.…”

Section: Numerical Examplementioning

confidence: 76%

“…The upper bounds of the state and the control input are taken as

\overset{x}{true} = 20

and

ū = 10

, respectively. Membership functions are set by

F^{1} (x_{1}) = \frac{1}{1 + e x p (- 2 x_{1})}, F^{2} (x_{1}) = 1 - F^{1} (x_{1}) .

In order to evaluate the characteristics of the controller under the proposed FMPC strategy in this paper, we compare the effects of two controllers, one of which is designed by the RMPC strategy proposed by Bououden et al under the event‐triggering–based TOD protocol without considering the time‐varying delays and the other is designed by the proposed FMPC strategy in this paper. The comparison results are given as follows.…”

Section: Numerical Examplementioning

confidence: 99%

See 1 more Smart Citation

Dynamic output‐feedback fuzzy MPC for Takagi‐Sugeno fuzzy systems under event‐triggering–based try‐once‐discard protocol

Dong

Song

Wang

et al. 2019

Intl J Robust & Nonlinear

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Summary The fuzzy model predictive control (FMPC) problem is studied for a class of discrete‐time Takagi‐Sugeno (T‐S) fuzzy systems with hard constraints. In order to improve the network utilization as well as reduce the transmission burden and avoid data collisions, a novel event‐triggering–based try‐once‐discard (TOD) protocol is developed for networks between sensors and the controller. Moreover, due to practical difficulties in obtaining measurements, the dynamic output‐feedback method is introduced to replace the traditional state feedback method for addressing the FMPC problem. Our aim is to design a series of controllers in the framework of dynamic output‐feedback FMPC for T‐S fuzzy systems so as to find a good balance between the system performance and the time efficiency. Considering nonlinearities in the context of the T‐S fuzzy model, a “min‐max” strategy is put forward to formulate an online optimization problem over the infinite‐time horizon. Then, in light of the Lyapunov‐like function approach that fully involves the properties of the T‐S fuzzy model and the proposed protocol, sufficient conditions are derived to guarantee the input‐to‐state stability of the underlying system. In order to handle the side effects of the proposed event‐triggering–based TOD protocol, its impacts are fully taken into consideration by virtue of the S‐procedure technique and the quadratic boundedness methodology. Furthermore, a certain upper bound of the objective is provided to construct an auxiliary online problem for the solvability, and the corresponding algorithm is given to find the desired controllers. Finally, two numerical examples are used to demonstrate the validity of proposed methods.

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Section: Numerical Examplementioning

confidence: 76%

“…The upper bounds of the state and the control input are taken as

\overset{x}{true} = 20

and

ū = 10

, respectively. Membership functions are set by

F^{1} (x_{1}) = \frac{1}{1 + e x p (- 2 x_{1})}, F^{2} (x_{1}) = 1 - F^{1} (x_{1}) .

Section: Numerical Examplementioning

confidence: 99%

Dynamic output‐feedback fuzzy MPC for Takagi‐Sugeno fuzzy systems under event‐triggering–based try‐once‐discard protocol

Dong

Song

Wang

et al. 2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Zhang et al designed a semiactive controller based on the inverse model and sliding mode control strategies for the quarter-vehicle suspension with the magnetorheological damper [16]. Bououden et al investigated the problem of time-varying delays and input constraints for active suspension systems with a quartervehicle model based on the Lyapunov-Krasovskii method [17]. From the analysis results on automobile active suspension research, we learned that much of the relevant literature focus on a quarter-or a half-car model that cannot fully reflect or evaluate riding comfort of a whole vehicle [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of Automobile Vibration System Based on Fuzzy Theory under Different Road Excitation Information

Chen

Zhou

2018

Complexity

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A fuzzy increment controller is designed aimed at the vibration system of automobile active suspension with seven degrees of freedom (DOF). For decreasing vibration, an active control force is acquired by created Proportion-Integration-Differentiation (PID) controller. The controller's parameters are adjusted by a fuzzy increment controller with self-modifying parameters functions, which adopts the deviation and its rate of change of the body's vertical vibration velocity and the desired value in the position of the front and rear suspension as the input variables based on 49 fuzzy control rules. Adopting Simulink, the fuzzy increment controller is validated under different road excitation, such as the white noise input with four-wheel correlation in time-domain, the sinusoidal input, and the pulse input of C-grade road surface. The simulation results show that the proposed controller can reduce obviously the vehicle vibration compared to other independent control types in performance indexes, such as, the root mean square value of the body's vertical vibration acceleration, pitching, and rolling angular acceleration.

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“…LQR based fuzzy controller; Fuzzy PID controller and Linear Quadratic Controller (LQR) are designed respectively to analyze and compare the performance characteristics of the active system with the uncontrolled system or passive suspension system. In [14], a robust model predictive control algorithm for polytopic uncertain systems with time-varying delays is presented for active suspension systems. However, most often, controllers are designed for the faultless suspension system so that the closed loop meets given performance specifications in studies of active suspension.…”

Section: Introductionmentioning

confidence: 99%

Passive fault-tolerant control for vehicle active suspension system based on H2/H∞ approach

Zhang¹,

Gong²

2018

J. vibroeng.

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Abstract. In this paper, a robust passive fault-tolerant control (RPFTC) strategy based on / approach and an integral sliding mode passive fault tolerant control (ISMPFTC) strategy based on / approach for vehicle active suspension are presented with considering model uncertainties, loss of actuator effectiveness and time-domain hard constraints of the suspension system. performance index less than and performance index is minimized as the design objective, avoid choosing weighting coefficient. The half-car model is taken as an example, the robust passive fault-tolerant controller and the integral sliding mode passive fault tolerant control law is designed respectively. Three different fault modes are selected. And then compare and analyze the control effect of vertical acceleration of the vehicle body and pitch angular acceleration of passive suspension control, robust passive fault tolerant control and integral sliding mode passive fault tolerant control to verify the feasibility and effectiveness of passive fault tolerant control algorithm of active suspension. The studies we have performed indicated that the passive fault tolerant control strategy of the active suspension can improve the ride comfort of the suspension system.

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Constrained model predictive control for time-varying delay systems: Application to an active car suspension

Cited by 66 publications

References 19 publications

Dynamic output‐feedback fuzzy MPC for Takagi‐Sugeno fuzzy systems under event‐triggering–based try‐once‐discard protocol

Dynamic output‐feedback fuzzy MPC for Takagi‐Sugeno fuzzy systems under event‐triggering–based try‐once‐discard protocol

Modelling and Analysis of Automobile Vibration System Based on Fuzzy Theory under Different Road Excitation Information

Passive fault-tolerant control for vehicle active suspension system based on H2/H∞ approach

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