International Journal of Robust and Nonlinear Control最新文献

Aiming at safely capturing faulty satellites on orbit, a novel predefined-time impedance controller is designed to address the control challenges of free-flying flexible-joint space robots (FFSR) considering output constraint. The FFSR system model is transformed into a singularly perturbed form consisting of both fast and slow subsystems. For the slow subsystem, an adaptive predefined-time sliding mode observer is developed to obtain the contact torque between the end-effector and the target. To mitigate overshooting and enhance tracking precision, a predefined-time prescribed performance function is proposed, and the output constraint issue is reformulated as a coordinate transformation problem involving the trajectory tracking errors. Based on these, a predefined-time impedance controller is designed to achieve the compliant capture of the target. For the fast subsystem, a new non-singular fixed-time controller is proposed to rapidly overcome the vibration of the flexible joints. Stability analysis proves predefined-time stability of the FFSR system and the tracking errors can be maintained within a predefined region. Finally, numerical simulations indicate the feasibility and validity of the presented control strategy.

In this paper, the sampled-data-driven event-triggered secure control is discussed for a class of uncertain nonlinear cyber-physical systems under multiple attacks containing deception attacks and replay attacks. Compared with exisiting research, the studied systems take into account the uncertainty and coupling that may occur in practical situations. Notably, deception attacks impede the application of a gain design approach in control design. To address this issue, an artful state transformation is presented to convert the considered system into an auxiliary system. Based on this, a novel sampled-data-driven attack compensation scheme is devised via the gain design approach, and an event-triggered mechanism is further introduced into the scheme to cancel needless control updates, which successfully ensures state convergence meanwhile resisting the impact of multiple attacks. Particularly, the designed control scheme not only utilizes data efficiently but also avoids Zeno behaviour automatically by judging triggering conditions in sampled points. Finally, the validity of the results is verified through two simulation examples.

This article develops a predefined-time sliding mode control approach for systems with external disturbances and uncertainties through a nonlinear disturbance observer (DO). For addressing predefined-time stabilization problem of robotic manipulator system, a predefined-time sliding mode surface is proposed, ensuring system states converge to origin within a predefined-time once sliding mode surface is attained. Compared to conventional fixed-time and finite-time control strategies, a distinctive advantage of this scheme is that system settling time can be explicitly chosen in advance and independent of system states. To achieve predefined-time performance, a disturbance observer is introduced to generate the disturbance estimate, which can be incorporated into controller to counteract disturbance. To address the systems uncertainty, an adaptive law is employed to estimate the unknown upper boundary of system uncertainties. Finally, the effectiveness and performance of the proposed scheme are illustrated by simulation and experiment.

This work focuses on the development of a machine learning (ML) model-based framework for safe optimal tracking control of a class of nonlinear control-affine systems to ensure simultaneous closed-loop stability and safety. Specifically, a novel multilayer feedforward neural network (FNN) with a control-affine architecture is designed to model nonlinear dynamic systems. Subsequently, a model-based reinforcement learning (RL) framework is presented, utilizing a novel cost function with Control Lyapunov-Barrier Function (CLBF) properties, to learn both the control policy and the optimal value function for an infinite-horizon optimal tracking control problem for nonlinear systems with safety constraints. The efficacy of the proposed methodology is demonstrated through simulations of a one-link robot manipulator and a chemical process example.

In this article, the adaptive fixed-time prescribed performance (FTPP) regulation is investigated for a class of time-varying state constrained switched stochastic systems with input delay. The time-varying barrier Lyapunov function and a compensation system are presented, respectively, to deal with the design problems caused by the existence of both time-varying state constraints and input delay. Some radial basis function neural networks are used to approximate unknown functions, and the common Lyapunov function method is displayed to handle the switched signals. Besides, by designing a fixed-time prescribed performance function, the desired adaptive neural controller is constructed. Compared with the existing works for state constrained control problem, the FTPP regulation control scheme is first proposed for time-varying state constrained stochastic switched systems under input delay, and the adaptive dynamic surface control scheme with the nonlinear filter is designed to solve the problem of “explosion of complexity.” Based on the stochastic stability theory, the FTPP of system output is achieved, other system state variables are restricted in the predefined regions, and all signals of this closed-loop system remain bounded in probability. Finally, the availability of the proposed control scheme is illustrated via two simulation examples.

This paper investigates the problem of bipartite containment control for multi-agent systems (MASs) under denial-of-service (DoS) attacks with event-triggered scheme. The DoS attacks can cause disruptions to communication channels, suspending or crashing services. In order to minimize the impact of the attacks on the control process, this paper introduces a bipartite containment control protocol with a resilient event-triggered scheme, which enables the MASs not only to resist DoS attacks but also to optimize the utilization of resources and accomplish the control target. After considering the previous, the paper suggests a new observer to estimate the state of agents in order to cope with the unmeasurable state of MASs. Moreover, Zeno behavior avoidance is rendered more effective by the introduction of a positive constant in the event-triggered function. At last, simulation data and results are given, thus verifying the feasibility of solving the bipartite containment problem under DoS attacks.

In this article, we propose a data-driven networked control architecture for unknown and constrained cyber-physical systems capable of detecting networked false-data-injection attacks and ensuring plant's safety. In particular, on the controller's side, we design a novel robust anomaly detector that can discover the presence of network attacks using a data-driven outer approximation of the expected robust one-step reachable set. On the other hand, on the plant's side, we design a data-driven safety verification module, which resorts to worst-case arguments to determine if the received control input is safe for the plant's evolution. Whenever necessary, the same module is in charge of replacing the networked controller with a local data-driven set-theoretic model predictive controller, whose objective is to keep the plant's trajectory in a pre-established safe configuration until an attack-free condition is recovered. Numerical simulations involving a two-tank water system illustrate the features and capabilities of the proposed control architecture.