This paper addresses the problem of ride height tracking for an electronically-controlled active air suspension (AAS) system in the presence of parametric uncertainties and unmodeled dynamics. A mathematical model of a quarter vehicle with AAS system is first built on the basis of thermodynamics to describe the dynamic characteristics. Then, by employing the backstepping technique, a novel height tracking controller is proposed in order to guarantee that (i) the ride height of a vehicle can converge on a neighborhood of the desired height, achieving global uniform ultimate boundedness (GUUB); (ii) the controller is robust to the parametric uncertainties by designing parameter estimators and introducing some conservativeness in the control law to dominate the unmodeled dynamics. Moreover, a group of smooth projectors is used to ensure all estimates remain within predefined corresponding bounds. To validate the efficiency and performance of the proposed strategy, simulation and experimental results are presented and analyzed, showing that the proposed control strategy is superior to the PID controller and the recent proposed hybrid model predictive controller.

This paper addresses the problem of nonlinear height tracking control of an automobile active air suspension with the output state constraints and time-varying disturbances. The proposed control strategy guarantees that the ride height stays within a predefined range, and converges closely to an arbitrarily small neighborhood of the desired height, ensuring uniform ultimate boundedness. The designed nonlinear observer is able to compensate for the time-varying disturbances caused by external random road excitation and perturbations, achieving robust performance. Simulation results obtained from the co-simulation (AMESim-Matlab/Simulink) are given and analyzed, demonstrating the efficiency of the proposed control methodology.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov–Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.

This paper proposes a fault diagnosis method for miniature DC motors (MDCMs) in the presence of the uncertainties caused by material and random factors of the production process. In this method, the probability models of fault multiple features are established based on the advantage criterion of the maximum overall average membership to determine the distribution of fault multiple features. The fault diagnosis algorithm is synthesized to obtain the threshold ranges of fault multiple features according to different confidence levels. Experimental test results are presented and analyzed to validate the efficiency and performance of the proposed fault diagnosis method.

This paper proposes an optimal safety distribution strategy to enhance the safety, stability, and comfort of semiactive suspensions under extreme conditions. A mathematical model of the full vehicle, incorporating lateral load transfer, is developed to describe its dynamic characteristics. Backstepping technology provides virtual inputs for vertical and roll motions. By integrating the principles of energy minimization with these virtual inputs, the controller determines the appropriate damping force distribution. The asymptotic stability of the control strategy is rigorously demonstrated using Lyapunov functional analysis, ensuring that the system’s tracking error remains within acceptable bounds. Results and analysis from cosimulation validate the effectiveness and advantages of this allocation strategy.