Materials Science

Abstract This article proposes an integrated model predictive control (MPC) framework with disturbance preview information for nonlinear systems. It is assumed that the disturbance can be previewed within the prediction horizon but unknown outside the horizon. First an integrated terminal control law consisting of both feedback and feedforward is considered. Based on that, a procedure […]

Abstract Data-driven model predictive control (MPC) is an effective control method in controlling unknown constrained systems. The existing data-driven MPC methods either estimate the system online (adaptive) with extra computation efforts, or use the initially measured trajectory from offline trials to design controller. The offline trials are economically expensive for many practical systems. To overcome

ABSTRACT This article focuses on the practical stability to a part of the variables of stochastic Riemann-Liouville type fractional differential equation. The proof is considered by Lyapunov functions, stopping time technique, and stochastic analysis theory. Some numerical simulations are provided to verify the validity of the obtained results. ​International Journal of Robust and Nonlinear Control,

ABSTRACT This paper explores the output feedback consensus problem of parabolic multi-agent systems (MASs) while incorporating event-triggered observations. To mitigate communication overhead and improve communication efficiency, the distributed observer with an event-triggered mechanism is constructed. Drawing upon the observer state, the leader-follower consensus control protocol for MASs is suggested. Utilizing the relative output information between

Abstract Stochastic model predictive control (SMPC) is a popular approach to control uncertain systems by incorporating the probabilistic distribution of the uncertainty into the controller design. However, the performance of the SMPC deteriorates rapidly when the system becomes nonlinear. In this article, an efficient SMPC is derived after decomposing the discrete nonlinear systems into a

ABSTRACT This paper centers on a kind of linear quadratic stochastic optimal control problem driven by conditional mean-field stochastic differential equations under partial information. In this context, the cost functional is permitted to be indefinite. At the outset, we present a broad overview of optimal control with the aid of the adjoint equation. However, the

ABSTRACT This article first gives an improved practical finite-time stability criterion for stochastic nonlinear systems, which offers a new idea for providing a faster convergence time. This criterion is generalized to more general stochastic nonlinear systems whose states may contain uncertainties, moreover, to the condition of Lyapunov functions including both powers less than 1 and

ABSTRACT When the physical properties of mechanical systems align with the structure of the model, the continuous-time (CT) systems can be effectively represented by an interpretable and parsimonious additive formal models. This article addresses the parameter estimation challenges of additive CT autoregressive moving average (ACTARMA) systems. Based on the maximum likelihood principle, the optimality conditions

ABSTRACT The research on disturbance rejection control for robotic arms in assisted puncture surgery systems aims to improve the precision of puncture surgeries. In this article, an improved sliding mode control (SMC) strategy for robotic arm with active disturbance rejection capability for assisted puncture surgery is proposed. First, a nonlinear extended state observer (NESO) is

ABSTRACT In the paper, we design an adaptive neural network fault-tolerant controller (FTC) with the distributed sliding-mode estimator. This controller is suitable for the a type of nonlinear multi-agent systems (MASs) containing the actuator faults and the asymmetric time-varying state constraints (ATVSCs). In practical applications, the actuator faults and ATVSCs will affect the stability of