Control of an Active Suspension System Subject to Random Component Failures

1990 ◽  
Vol 112 (1) ◽  
pp. 94-99 ◽  
Author(s):  
C. D. de Benito ◽  
S. J. Eckert
Keyword(s):  
Fault Tolerant ◽  
Active Suspension ◽  
Suspension System ◽  
Control Algorithms ◽  
Random Component ◽  
Certainty Equivalence ◽  
On Certainty ◽  
Automotive Systems ◽  
Actuator Failures ◽  
Component Failures

Two fault-tolerant control algorithms based on certainty-equivalence and dual control approximations, are described in this paper. These algorithms maintain the system performance in the presence of component failures. Their performance is demonstrated by simulations of an active suspension system subjected to sensor and actuator failures. Both algorithms provide viable solutions to the problem of faulttolerant control in automotive systems. However, the certainty-equivalence control algorithm is simpler to implement.

Adaptive fault-tolerant control for active suspension systems based on the terminal sliding mode approach

2019 ◽  
Vol 234 (2) ◽  
pp. 501-511
Author(s):  
Amirhossein Kazemipour ◽  
Alireza B Novinzadeh
Keyword(s):  
Control Strategy ◽  
Control Method ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Fault Tolerant Control ◽  
Active Suspension ◽  
Suspension System ◽  
Terminal Sliding Mode ◽  
Car Suspension ◽  
Suspension Model

In this paper, a control system is designed for a vehicle active suspension system. In particular, a novel terminal sliding-mode-based fault-tolerant control strategy is presented for the control problem of a nonlinear quarter-car suspension model in the presence of model uncertainties, unknown external disturbances, and actuator failures. The adaptation algorithms are introduced to obviate the need for prior information of the bounds of faults in actuators and uncertainties in the model of the active suspension system. The finite-time convergence of the closed-loop system trajectories is proved by Lyapunov's stability theorem under the suggested control method. Finally, detailed simulations are presented to demonstrate the efficacy and implementation of the developed control strategy.

Neural Network Based Fault Tolerant Control for a Semi-Active Suspension

2019 ◽  
Author(s):  
Sergio Alberto Rueda Villanoba ◽  
Carlos Borrás Pinilla
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Tolerant ◽  
Mr Damper ◽  
Fault Tolerant Control ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Force Model ◽  
The Neural Network

Abstract In this study a Neural Network based fault tolerant control is proposed to accommodate oil leakages in a magnetorheological suspension system based in a half car dynamic model. This model consists of vehicle body (spring mass) connected by the MR suspension system to two lateral wheels (unsprung mass). The semi-active suspension system is a four states nonlinear model; it can be written as a state space representation. The main objectives of a suspension are: Isolate the chassis from road disturbances (passenger comfort) and maintain contact between tire and road to provide better maneuverability, safety and performance. On the other hand, component faults/failures are inevitable in all practical systems, the shock absorbers of semi-active suspensions are prone to fail due to fluid leakage but quickly detect and diagnose this fault in the system, avoid major damage to the system and ensure the safety of the driver. To successfully achieve desirable control performance, it is necessary to have a damping force model which can accurately represent the highly nonlinear and hysteretic dynamic of the MR damper. To simulate parameters of the damper, a quasi-static model was applied, quasi-static approaches are based on non-newtonian yield stress fluids flow by using the Bingham MR Damper Model, relating the relative displacement of the piston, the frictional force, a damping constant, the stiffness of the elastic element of the damper and an offset force. The Fault detection and isolation module is based on residual generation algorithms. The residua r is computed as the difference between the displacement signal of functional and faulty model, when the residual is close to zero, the process is free of faults, while any change in r represents a faulty scheme then a wavelet transform, (Morlet wave function) is used to determine the natural frequencies and amplitudes of displacement and acceleration signal during the failure, this module provides parameters to the neural network controller in order to accommodate the failure using compensation forces from the remaining healthy damper. The neural network uses the error between the plant output and the neural network plant for computing the required electric current to correct the malfunction using the inverse dynamics function of the MR damper model. Consequently, a bump condition, and a random profile road (ISO 8608) described by the power spectral density (PSD) of its vertical displacement, is used as disturbance of control system. The performance of the proposed FTC structure is demonstrated trough simulation. Results shows that the control system could reduce the effect of the partial fault of the MR Damper on system performance.

Applicative Fault Tolerant Control for semi-active suspension system: Preliminary results

2013 ◽  
Author(s):  
Sebastien Varrier ◽  
Carlos A. Vivas-Lopez ◽  
Jorge de-J. Lozoya-Santos ◽  
Juan C. Tudon M. ◽  
Damien Koenig ◽  
...  
Keyword(s):  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Active Suspension ◽  
Suspension System ◽  
Preliminary Results

Fault detection and fault tolerant control of vehicle semi-active suspension system with magneto-rheological damper

2020 ◽  
Vol 30 (1) ◽  
pp. 014004
Author(s):  
Xiumei Du ◽  
Gaowei Han ◽  
Miao Yu ◽  
Youxiang Peng ◽  
Xiaoying Xu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Active Suspension ◽  
Suspension System ◽  
Magneto Rheological

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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