Adaptive sliding mode fault tolerant control design for uncertain nonlinear systems with multiplicative faults: Takagi–Sugeno fuzzy approach

2019 ◽  
Vol 234 (2) ◽  
pp. 147-159 ◽  
Author(s):  
Ali Ben Brahim ◽  
Slim Dhahri ◽  
Fayçal Ben Hmida ◽  
Anis Sellami
Keyword(s):  
Nonlinear Systems ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Control Design ◽  
Fault Tolerant Control ◽  
Adaptive Observer ◽  
System Stability ◽  
Uncertain Nonlinear Systems ◽  
Adaptive Sliding Mode ◽  
Takagi Sugeno

The present article deals with adaptive sliding mode fault tolerant control design for uncertain nonlinear systems, affected by multiplicative faults, that is described under Takagi–Sugeno fuzzy representation. First, we propose to conceive robust adaptive observer in order to achieve states and multiplicative faults estimation in the presence of nonlinear system uncertainties. Under the nonlinear Lipschitz condition, the observer gains are attained by solving the multi-objective optimization problem. Second, sliding mode controller is suggested to offer a solution of the closed-loop system stability even the occurrence of real fault effects. The main objective is to compensate multiplicative fault effects based on output feedback information. Sufficient conditions are developed with [Formula: see text] performances and expressed as a set of linear matrix inequalities subject to compute controller gains. Finally, simulation results, using the nonlinear model of a single-link flexible joint robot system, are given to illustrate the capability of the suggested fault tolerant control strategy to treat multiplicative faults.

An adaptive sliding mode–based fault-tolerant control design for half-vehicle active suspensions using T–S fuzzy approach

2020 ◽  
Vol 26 (17-18) ◽  
pp. 1411-1424 ◽  
Author(s):  
Hui Pang ◽  
Yuting Shang ◽  
Junjie Yang
Keyword(s):  
Controller Design ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Control Design ◽  
Fault Tolerant Control ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Fuzzy Approach ◽  
Active Suspensions ◽  
Adaptive Sliding Mode

This study proposes an improved adaptive sliding mode–based fault-tolerant control design for the improvement of dynamics performances of half-vehicle active suspensions with parametric uncertainties and actuator faults in the context of external road disturbances. To cope with the model establishment of the vehicle active suspensions, the T–S fuzzy approach and system augmentation technology are used to construct the T–S representation of the faulty augmented system, and a new adaptive law is, therefore, designed to achieve the accurate online estimation of the actuator gain and drift faults, which facilitates the desirable fault-tolerant controller design. Moreover, the proposed adaptive sliding mode–based fault-tolerant controller is synthesized, and the system stability analysis is further conducted in premise of the Lyapunov stability theory. Finally, a numerical simulation is provided to illustrate the effectiveness and robustness of the proposed controller.

Sliding mode fault-tolerant control for Takagi-Sugeno fuzzy systems with local nonlinear models: Application to inverted pendulum and cart system

2020 ◽  
pp. 014233122094936
Author(s):  
Riadh Hmidi ◽  
Ali Ben Brahim ◽  
Slim Dhahri ◽  
Fayçal Ben Hmida ◽  
Anis Sellami
Keyword(s):  
Fuzzy Systems ◽  
Inverted Pendulum ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Nonlinear Models ◽  
Fault Tolerant Control ◽  
Simultaneous Occurrence ◽  
Sensor Faults ◽  
Adaptive Sliding Mode ◽  
Takagi Sugeno

This paper proposes fault-tolerant control design for uncertain nonlinear systems described under Takagi-Sugeno fuzzy systems with local nonlinear models that satisfy the Lipschitz condition. First, by transforming sensor faults as ‘pseudo-actuator’ faults, an adaptive sliding mode observer is designed in order to simultaneously estimate system states, actuator and sensor faults despite the presence of norm-bounded uncertainties. Second, an adaptive sliding mode controller is suggested to provide a solution to stabilize the closed-loop system, even in the event of simultaneous occurrence of faults in actuators and sensors. Next, the main objective of the fault-tolerant control strategy is to compensate for the effects of fault based on the feedback information. Therefore, using the LMI optimization method, sufficient conditions are developed with [Formula: see text] to calculate the gains of the observer and the controller. Then, particular attention is paid to the simultaneous maximization, by convex multi-objective optimization, of the Lipschitz nonlinear constant in Takagi-Sugeno fuzzy modelling and uncertainties attenuation level. The results of the simulation illustrate the effectiveness of our fault-tolerant control approach using a nonlinear inverted pendulum with a cart system.

An adaptive sliding mode fault-tolerant control for semi-active suspensions with magnetorheological dampers based on T-S fuzzy vehicle models

2021 ◽  
pp. 107754632110466
Author(s):  
Haohan Yang ◽  
Qingwei Liu ◽  
Yongchao Zhang ◽  
Fan Yu
Keyword(s):  
Fuzzy Inference ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Active Suspension ◽  
Suspension System ◽  
Magnetorheological Damper ◽  
Inference System ◽  
Adaptive Sliding Mode ◽  
Takagi Sugeno

This paper investigates an improved adaptive sliding mode fault-tolerant control strategy for a magnetorheological semi-active suspension system with parametric uncertainties and actuator faults. Using the experimental data collected by a quarter-vehicle test rig, an adaptive-network-based fuzzy inference system is employed to establish a learning-based magnetorheological damper model firstly. The Takagi-Sugeno fuzzy approach is introduced to deal with the uncertainties of sprung mass and pitch rotary inertia and then the corresponding Takagi-Sugeno faulty semi-active suspension system is constructed. An adaptive sliding mode fault-tolerant controller is proposed, in which the magnetorheological damper fault gain is observed by the designed estimation law, and the asymptotical stability of the system is further analyzed. Finally, numerical simulation tests are conducted to demonstrate the effectiveness of the designed control scheme.

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