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To improve the ride quality of a vehicle, an enhanced vibration control method is presented for semi-active suspension (SAS) with magnetorheological (MR) damper by combining back propagation neural network (BPNN) and particle swarm optimization (PSO). Based on the test data of MR damper, a non-parametric model of MR damper using adaptive neuro-fuzzy inference system (ANFIS) is first established, and based on that, a dynamics model of the SAS system is derived. Next, a BPNN controller is designed to fulfill the effective control of the current in MR damper. Meanwhile, the improved PSO with adaptive weight and dynamic acceleration constant is introduced to optimize the weights and thresholds of the BPNN controller, which can avoid the designed BPNN falling into the local optimum and then improve the convergence rate of the designed controller. Besides, the stability of the developed controller is analyzed via Lyapunov stability theory. Different from the existing models and methods, the established model can well describe the dynamics behaviors of the actual MR damper, and the proposed control method has better adaptability, convergence speed and precision. Finally, a simulative investigation is performed to validate the effectiveness and feasibility of the proposed controller, compared to existing BP-PID control and the passive suspension, the vehicle acceleration of SAS with this proposed controller is respectively improved by 10% and 30%.

This study proposes a non-fragile fault-tolerant control design based on adaptive robust observer for a type of Markov-type active suspension system with sensor and actuator faults under the external road disturbance. First, the faulty active suspension system is reconfigured into an augmented system by extending the sensor fault as part of the system state vector. Then, an appropriate adaptive observer is designed to estimate the sensor fault, the actuator fault, as well as the active suspension system states simultaneously. Afterwards, an expected non-fragile fault-tolerant control scheme based on state feedback technology is presented to asymptotically stabilize the vertical and pitch motions of the faulty active suspension system. Benefit from its non-fragility, the proposed controller has been proven to be highly stable against its internal gain perturbation. Finally, the comparative simulation results of a half-vehicle active suspension system are provided to validate the effectiveness of the proposed fault-tolerant controller. It is shown that the designed adaptive observer can precisely estimate the sensor and actuator faults, together with the active suspension system states, and the designed non-fragile fault-tolerant control controller can effectively compensate the performance loss of the faulty active suspension system and improve the reliability of the vehicle active suspension system.

The objective of this paper is to retain the desirable dynamics performances and improve ride quality for active suspensions with the actuator faults and unknown road disturbances. To that end, a novel proportionalintegral observer (PIO)-based fault-tolerant tracking controller (FTTC) design is proposed for automobile active suspensions (ASSs) encountered with actuator faults and parameter uncertainties. First, the Takagi-Sugeno (T-S) fuzzy model approach is adopted to establish T-S representation of the faulty ASSs by describing vehicle dynamics system as the weighed summation of a common linear system. Afterwards, a nominal robust H∞ output feedback controller is developed to enhance the suspension performances under fault-free mode, whose output response indicators are taken as the prescribed reference trajectories. Then, a PIO-based fault estimator is designed to predict both the system states and the unmeasurable actuator faults, synchronously. On basis of this designed observer, the expected PIO-FTTC is synthesized to track the prescribed reference trajectories, and further to make up for the system performance deteriorations aroused by the actuator faults. Finally, a simulative investigation demonstrates the effectiveness and feasibility of the proposed PIO-FTTC compared to existing control approach.

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