Bumpless Transfer H∞ Anti-Disturbance Control of Switching Markovian LPV Systems Under the Hybrid Switching

Adaptive Predefined Time Control for Stochastic Switched Nonlinear Systems With Full-State Error Constraints and Input Quantization

A neural network adaptive quantized predefined-time control problem is studied for switching stochastic nonlinear systems with full-state error constraints under arbitrary switching. Unlike previous research on rapid convergence, the predefined-time stability criteria are introduced and established for stochastic nonlinear systems, ensuring the stabilization of the control system within a specified time frame. The chattering issue is avoided and it is split into two limited nonlinear functions using a hysteresis quantizer. To address the full-state error constraint problem, a universal barrier Lyapunov function is presented. The common Lyapunov function approach is used to demonstrate the stability of controlled systems. The results demonstrate that the proposed control method ensures all closed-loop signals are probabilistically practically predefined time-stabilized (PPTS), with the system output closely tracking the specified reference signal. Finally, simulated examples validate the effectiveness of the suggested control technique.

Bumpless Transfer Fault-Tolerant Control for Continuous-Time Switched Systems via Learning-Based Fault Reconstruction

This article focuses on the fault reconstruction and bumpless transfer fault-tolerant (FT) control problems for switched linear systems with magnitude-bounded disturbances and actuator faults in continuous-time domain. A new learning-based robust unknown input observer (UIO), not requiring fault differentiability and completely decoupled disturbances, is developed to accomplish fault reconstruction and state estimation. The fault reconstruction value is updated by one iteration learning on the timeline, i.e., the fault at the current moment is reconstructed by learning historical information from the previous moment. Based on the obtained estimation information, an efficient bumpless transfer FT controller is designed to counteract the fault effects and suppress the control bumps. The bumpless transfer constraint is guaranteed via a new inequality transformation method, which improves the anti-disturbance capability of the controller and also decreases the switching bumps. The solvability conditions for the bumpless transfer controller and learning-based UIO are developed under the condition of average dwell time switching. Finally, an application of the inverted pendulum controlled by a direct current motor is presented to reveal the effectiveness and applicability of the developed methods.

A novel network-based adaptive fault-tolerant control of switched nonlinear systems subject to multiple faults under prescribed performance

It is the first report about fault-tolerant-based prescribed performance control of switched nonlinear systems under multiple faults. The concerned faults include not only external faults but also actuator faults. In the process of backstepping control design, prescribed performance control is fully considered, and the combination of unknown nonlinear functions is estimated by multi-dimensional Taylor network. Finally, the developed adaptive fault-tolerant control strategy guarantees the boundedness of all controlled signals while prescribed tracking performance is satisfied. In an effort to further manifest the validity of the fault-tolerant controller, a numerical simulation and a practical simulation are introduced.

Observer-Based Adaptive NN Security Control for Switched Nonlinear Systems Against DoS Attacks: An ADT Approach

In this article, a novel switched observer-based neural network (NN) adaptive control algorithm is established, which addresses the security control problem of switched nonlinear systems (SNSs) under denial-of-service (DoS) attacks. The considered SNSs are described in lower triangular form with external disturbances and unmodeled dynamics. Note that when an attack is launched in the sensor-controller channel, the controller will not receive any message, which makes the standard backstepping controller not workable. To tackle the challenge, a set of NN adaptive observers are designed under two different situations, which can switch adaptively depending on the DoS attack on/off. Further, an NN adaptive controller is constructed and the dynamic surface control method is borrowed to surmount the complexity explosion phenomenon. To eliminate double damage from DoS attacks and switches, a set of switching laws with average dwell time are designed via the multiple Lyapunov function method, which in combination with the proposed controllers, guarantees that all the signals in the closed-loop system are bounded. Finally, an illustrative example is offered to verify the availability of the proposed control algorithm.

Nonfragile Output Feedback Tracking Control for Markov Jump Fuzzy Systems Based on Integral Reinforcement Learning Scheme

In this article, a novel integral reinforcement learning (RL)-based nonfragile output feedback tracking control algorithm is proposed for uncertain Markov jump nonlinear systems presented by the Takagi-Sugeno fuzzy model. The problem of nonfragile control is converted into solving the zero-sum games, where the control input and uncertain disturbance input can be regarded as two rival players. Based on the RL architecture, an offline parallel output feedback tracking learning algorithm is first designed to solve fuzzy stochastic coupled algebraic Riccati equations for Markov jump fuzzy systems. Furthermore, to overcome the requirement of a precise system information and transition probability, an online parallel integral RL-based algorithm is designed. Besides, the tracking object is achieved and the stochastically asymptotic stability, and expected H_∞ performance for considered systems is ensured via the Lyapunov stability theory and stochastic analysis method. Furthermore, the effectiveness of the proposed control algorithm is verified by a robot arm system.

New result on event-triggered dynamic output feedback H∞ control for discrete-time switched systems

In this paper, the event-triggered dynamic output feedback H∞ controller for a discrete-time switched linear system is addressed. The switching of subsystems during the inter-event interval has always been the focus of researchers' discussions. Particularly, multiple switching is allowed to happen during an inter-event interval in this paper. To reflect the characteristics of multiple switching during an inter-event interval, the average dwell time approach is adopted in stability analysis and l2-gain performance analysis. Since the full information of system states cannot always be acquired directly, the dynamic output feedback control is taken into consideration. Furthermore, by utilizing a controller-mode-dependent Lyapunov function, we obtain criteria of global exponential l2 stability for switched systems, which are the basis of controller design. Finally, we provide a numerical example to prove the validity of the theorem proposed in this paper.

Adaptive finite-time control for switched nonlinear systems subject to multiple objective constraints via multi-dimensional Taylor network approach

The finite-time control of switched nonlinear systems subject to multiple objective constraints is investigated in this article. Firstly, with the aim of dealing with the major challenge brought by multiple objective constraints, the time-varying and asymmetric barrier function is designed, which transforms multiple objective constrained systems into unconstrained systems. Secondly, the dynamic surface control technique is introduced into the backstepping design process, and the error generated in the filtering process is reduced by constructing the error compensation systems. Then, an adaptive finite-time controller based on multi-dimensional Taylor network (MTN) is proposed. The controller proposed in this article can avoid the "singularity" problem and ensure that the objective functions never violate constraints. Finally, the effectiveness of the finite-time control strategy proposed in this article is verified by the aircraft system simulation.

An observer-based output tracking controller for electrically driven cooperative multiple manipulators with adaptive Bernstein-type approximator

Thisarticle presents an observer-based output tracking control method for electrically actuated cooperative multiple manipulators using Bernstein-type operators as a universal approximator. This efficient mathematical tool represents lumped uncertainty, including external perturbations and unmodeled dynamics. Then, adaptive laws are derived through the stability analysis to tune the polynomial coefficients. It is confirmed that all the position and force tracking errors are uniformly ultimately bounded using the Lyapunov stability theorem. The theoretical achievements are validated by applying the proposed observer-based controller to a cooperative robotic system comprised of two manipulators transporting a rigid object. The outcomes of the introduced method are also compared to RBFNN, which is a powerful state-of-the-art approximator. The results demonstrate the efficacy of the introduced adaptive control approach in controlling the system even in the presence of disturbances and uncertainties.