The nonlinear dynamics of an actuator are considered during the output feedback control design of a quarter-car active suspension system with uncertainties. Because of the complexity of the suspension system with hydraulic actuator dynamics, a simple and effective sliding-mode strategy is employed to obtain both controller and observer. Instead of dividing the system into an actuator subsystem and a suspension subsystem, the system is repartitioned into a linear subsystem and a nonlinear subsystem, which facilitates controller design greatly. By specifying suitable sliding functions for the two subsystems respectively, and forcing the output of the nonlinear subsystem to track the desired fictitious input of the linear subsystem, the sliding-mode controller is created. By Lyapunov theory, robust stability is analyzed. For linear growth vanishing bounded uncertainties and nonvanishing bounded uncertainties, different observer forms are given to simplify the observer in different situations. Based on the constructed sliding-mode observer, the sliding-mode output feedback control suspension closed-loop system is accomplished. The convergence of observation error is subsequently proved. Simulation results verify the effect of the presented method.

In order to deal with control constraints and the performance optimization requirements in aircraft engines, a new nonlinear model predictive control method based on an elastic BP neural network with a hybrid grey wolf optimizer is proposed in this paper. Based on the acquired aircraft engines data, the elastic BP neural network is used to train the prediction model, and the grey wolf optimization algorithm is applied to improve the selection of initial parameters in the elastic BP neural network. The accuracy of network modeling is increased as a result. By introducing the logistics chaotic sequence, the individual optimal search mechanism, and the cross operation, the novel hybrid grey wolf optimization algorithm is proposed and then used in receding horizon optimization to ensure real-time operation. Subsequently, a nonlinear model predictive controller for aircraft engine is obtained. Simulation results show that, with constraints in the control signal, the proposed nonlinear model predictive controller can guarantee that the aircraft engine has a satisfactory performance.

Autonomous driving is producing various novel achievements in integrating different control technologies with machine learning to control intelligent vehicles, i.e., learning-based control. Yet, few work formally consider the confidence of learning-based models and their residual model uncertainties. As a result, existing methods lack safety guarantees in real-world applications. To address this issue, this work proposes a Linear-Bayesian-Regression-based Model Predictive Control (MPC) method for vehicles with unknown dynamics and subject to possibly unbounded disturbances. Model parameters can be obtained by using posterior calculations via prior estimations and systems’ historical data. The expected value of resulting posterior distribution is chosen as linear model's parameters for controller design. Furthermore, the corresponding posterior distribution can be used to derive an upper bound for the variance of residual model uncertainties. In analogy to the concept of Tube-MPC, this work introduces a dynamic feedback gain which is combined with the standard nominal MPC method. It derives provable security guarantees with respect to state and input chance constraints for complex unknown dynamical systems in the form of probabilities. A convex-reformulation-based optimization method is proposed to enable efficient computation of control laws and ensure the recursive feasibility. Simulations of a Vander Pol's oscillator's control and a vehicle's tracking control illustrate the design procedure and effectiveness of the proposed method.

In this paper, a novel sliding mode fault tolerant control method is presented for aircraft engine systems with uncertainties and disturbances on the basis of adaptive diagnostic observer. By taking both sensors faults and actuators faults into account, the general model of aircraft engine control systems which is subjected to uncertainties and disturbances, is considered. Then, the corresponding augmented dynamic model is established in order to facilitate the fault diagnosis and fault tolerant controller design. Next, a suitable detection observer is designed to detect the faults effectively. Through creating an adaptive diagnostic observer and based on sliding mode strategy, the sliding mode fault tolerant controller is constructed. Robust stabilization is discussed and the closed-loop system can be stabilized robustly. It is also proven that the adaptive diagnostic observer output errors and the estimations of faults converge to a set exponentially, and the converge rate greater than some value which can be adjusted by choosing designable parameters properly. The simulation on a twin-shaft aircraft engine verifies the applicability of the proposed fault tolerant control method.