International Journal of Robust and Nonlinear Control最新文献

This paper proposes a novel event-triggered adaptive asymptotic tracking control (ATC) method for stochastic non-linear systems with unknown hysteresis. Firstly, in order to reduce the depletion of network resources while optimizing the asymptotic tracking performance of the system, a switching threshold mechanism (STM)-based event-triggered control (ETC) strategy is adopted. Secondly, a first-order filter is utilized to address the problem of the contradiction between event-triggered mechanism (ETM) output and rate-dependent hysteresis actuator input. By incorporating an enhanced backstepping technique and a bounded estimation method, it is rigorously demonstrate that the system achieves zero tracking error, effectively compensates for unknown hysteresis, and ensures that all closed-loop signals remain bounded in probability. Meanwhile, the Zeno phenomenon is excluded. Finally, the effectiveness and superiority of the proposed control scheme are verified by the simulation results.

A robust approximate constraint-following control (RACC) approach is proposed in this article for collaborative robots with inequality constraints. The trajectory-following control and boundary control of the robot are investigated. First, an explicit constraint equation for the collaborative robot system is established based on the Udwadia–Kalaba (U-K) theory. Second, due to the monotone unbounded property of the tangent function, a special function is constructed to transform the joint output angles of the constrained robot into unconstrained state variables, and a new form of the robot constraint equation is obtained. Through this transformation, the joint motion of the robot will always be confined to specified angles and follow the desired trajectory. The constraint equation ensures the safety of the robot at the algorithmic level and innovatively solves the control problem of the equality and inequality of the robot's motion. According to theoretical analysis, the control approach can deal with uncertainty and satisfy both uniform boundedness (UB) and uniform ultimate boundedness (UUB) requirements. Finally, based on the rapid controller prototype CSPACE and a two-degree-of-freedom collaborative robot platform, experimental verification is carried out. Numerical simulation and experimental results demonstrate that the proposed RACC approach with state transformation exhibits significant advantages in trajectory tracking performance and safety for collaborative robots.

For a class of continuous-time non-linear systems with saturated input and unknown non-linear disturbance, a novel event-triggered integral reinforcement learning (IRL) control strategy based on recursive terminal sliding mode (RTSM) is proposed in this paper. Firstly, a novel performance index function is designed based on RTSM and a two-player zero-sum game, and the robust control problem with saturated input and unknown disturbance can be transformed into an optimal control problem. To avoid the requirement of drift dynamics, the IRL technique is introduced. Secondly, a critic neural network is used to approximate the optimal value function, which not only simplifies algorithm implementation structure, but also relaxes initial admissible control in the learning of neural network weights. Then, considering the event-triggered mechanism, the asymptotic stability of the closed-loop system and the uniformly ultimately boundedness of weight estimation errors are proved by utilizing the Lyapunov theory. Finally, simulation results illustrate the effectiveness of the proposed control method.

This article investigates the sensor fault diagnosis problem of the discrete-time Euler–Lagrange (EL) system with multi-sensors. Firstly, the discrete-time EL system is converted into a second-order non-linear discrete-time system using the famous Dragon Gekuta method. Secondly, the proposed strategy leverages the multi-sensors data fusion framework, employing multiple local unscented Kalman filters for state estimation. Moreover, the convergence of the local estimation is analyzed such that the local estimation errors are stable in faults-free case. Thirdly, considering the sensor fault in the presence of process and measurement noises, the residual signal based on the local estimation error is designed to detect and isolate faults. The fault detection and isolation logic is conducted using $��{�\chi �}^{�2�}�$ detection, where the threshold is determined through the cumulative distribution function of $��{�\chi �}^{�2�}�$ distribution. Finally, a single-link robot is used to illustrate the effectiveness of the proposed fault diagnosis based on the multi-sensors data fusion.

In this paper, the issue of adaptive event-triggered output feedback consensus tracking dynamic surface control is discussed for nonlinear multi-agent systems (MASs) with unmodeled dynamics. The system states are estimated via K-filters. The unknown nonlinear continuous functions are approximated using radial basis function neural networks (RBFNNs). To lighten the load on communication, an event-triggered control (ETC) method with a relative threshold is developed. By using command filter backstepping technology, a unique adaptive consensus tracking control strategy is presented. Then, through Lyapunov stability analysis, all signals in the closed-loop system can be guaranteed to be semi-globally uniformly ultimately bounded (SGUUB), and the Zeno phenomenon can be avoided. Finally, simulation results validate the effectiveness of the proposed method.

Consider an offshore gangway subject to mathematical model parameter uncertainties and disturbance uncertainties. A finite-time observer (FTO) is designed to estimate the motion states of the gangway end (GE) and the total uncertain nonlinear disturbances lumped by the parameter uncertainties and disturbance uncertainties. The bandwidth of the FTO is a function of the GE position estimation error and a prescribed performance function and is time-varying, and thus our designed FTO does not rely on the bounds of the derivatives of the total uncertain nonlinear disturbances compared with conventional FTOs. Whereby, according to the idea of the active disturbance rejection control, we develop a prescribed-performance robust control law (PPRCL) for GE position stabilization such that the position stabilization error can converge into a prescribed tolerance steady-state band in a finite settling time. Simulation results with comparisons show the robustness and the advantage of our developed PPRCL.

In this study, the output formation containment control problem for heterogeneous singular multiagent systems is investigated. The purpose of this study is to drive the output of the leader agents to reach a time-varying formation and take containment action for the follower agents. First, a novel distributed adaptive protocol is designed that relies on the relative output information of agents without measuring and transmitting their states information. Then, the formation containment control problem can be simplified into an admissible analysis of coupled nonlinear singular systems by constructing a model transformation. The output formation control conditions related to the spectral radius of the weighted adjacency matrix are obtained based on the bounded real lemma and admissible analysis. In addition, the adaptive protocols based on the state of the virtual leader are proposed, which can drive the agents to achieve formation containment control more quickly when the state of the virtual leader is known. Finally, the simulation results verify the feasibility of the output formation containment control.

In this paper, a high-order, fully actuated (HOFA) system approach-based adaptive control design, with guaranteed transient and steady-state tracking performances, is proposed for trajectory tracking of a lightweight, low-cost quadrotor unmanned aerial vehicle (QUAV). Unlike most existing results on adaptive control for QUAVs, we consider the aerodynamic parameters and disturbance terms of the QUAV model as time-varying uncertainties. These uncertainties are only assumed to be bounded, with no additional restrictions imposed on their bounds and derivatives, making our approach more suitable for QUAVs in dynamic harsh environments. Moreover, in contrast to the existing first-order state-space model-based methods such as typical adaptive backstepping designs, the proposed HOFA system-based approach does not require multiple design steps and can achieve arbitrarily assignable eigenstructure like a stabilized linear system, which makes the control design very simple and easy to implement for practical applications. In addition, by introducing the prescribed performance control technique and incorporating a novel appointed-time performance function, it is shown that all tracking errors can be steered into their predesignated precision regions within their respective pre-appointed settling times. Finally, the effectiveness and advantages of the proposed method are validated through simulation.

This paper investigates the problem of energy-efficient learning control for unknown repetitive nonlinear discrete-time systems. Traditional event-triggered model-free iterative learning control (ILC) relies on data-based approximation models to construct the controller optimization criterion, which is susceptible to model identification errors and the curse of dimensionality. To mitigate this limitation, we propose a novel direct-type high-order ILC algorithm that includes online learning capabilities. The control output is derived by directly applying iterative dynamic linearization to an ideal virtual nonlinear learning controller, with learning gains being automatically calibrated in real-time using a radial basis function neural network (RBFNN). Furthermore, this strategy integrates an adaptive, relative threshold-based, event-triggered protocol that is dynamically updated based on the trained neural weights and tracking errors. This approach offers significant advantages over existing strategies. Theoretical proofs demonstrate the convergence of learning gains and tracking errors, and the theoretical results are applied to the frequency regulation of active power impact loads on an experimental platform for steel industry microgrids, validating the effectiveness and applicability of our scheme.

At this job, the adaptive control problem is investigated for a class of non-linear cyber-physical systems (CPSs), where the CPSs considered are not only subject to deception attacks and time delay, but also contain uncertain input powers. The deception attacks result in the actual values of the system state being unavailable and control gains being unknown. On the basis of the theory of Lyapunov stability, a new adaptive neural-networks-based control scheme is designed to guarantee the stability of the closed-loop system and mitigate the impact of deception attacks. Compared with the existing works in literature, (1) the input powers of the CPSs considered in this article are unknown and new controllers are constructed based on the neural network approximation technique; (2) the influence of unknown time delay is eliminated by using a novel Lyapunov–Krasovskii function. Furthermore, in order to address unknown gains caused by deception attacks, the Nussbaum gain technique is firstly extended to the CPSs with power uncertainties. Finally, the simulation results confirm the effectiveness of the control strategy presented in this work.