Control Of Nonlinear Interconnected Systems Research Articles

Adaptive fuzzy inverse optimal output feedback decentralised prescribed performance control for interconnected nonlinear systems

This paper investigates an adaptive fuzzy inverse optimal output feedback decentralised prescribed performance control problem for interconnected systems. Firstly, the fuzzy logic systems (FLSs) are utilised to model unknown nonlinear functions in each subsystem and then an auxiliary system is constructed. Based on the auxiliary system, a fuzzy decentralised state observer is formulated to estimate unmeasurable states. Secondly, by applying the adaptive backstepping design technique and the inverse optimal control theory, an adaptive fuzzy inverse optimal output feedback decentralised controller is ultimately designed. It is proved that the proposed adaptive fuzzy inverse optimal decentralised controller not only minimises the cost function but also ensures that all variables of the control systems are semi-globally ultimately bounded and that the tracking errors converge to a small residual set with the prescribed performance bound. Finally, the effectiveness of the proposed control approach is verified by a nonlinear inverted pendulum large-scale system.

Adaptive Fuzzy Security Constraint Control for Interconnected Nonlinear Systems With Deception Data Injection Attacks

Adaptive Fuzzy Security Constraint Control for Interconnected Nonlinear Systems With Deception Data Injection Attacks

General output constraints control of interconnected nonlinear systems with sensor faults

General output constraints control of interconnected nonlinear systems with sensor faults

Decentralized adaptive tracking control for interconnected nonlinear systems with unmodeled dynamics and input delays

This paper focuses on the decentralised adaptive tracking control problem for nonstrict-feedback interconnected nonlinear systems with unmodeled dynamics and input delays. To identify the unstructured uncertainties, a novel broad learning system (BLS) approximation technique is introduced in this study to solve it. On this basis, a nonlinear state observer is designed to estimate the unmeasurable state variables. A dynamic signal is introduced to deal with the effect of unmodeled dynamics, and the appropriate error transformation is utilised to handle the effect of input delays. The decentralised adaptive tracking control strategy is proposed to the considered interconnected nonlinear systems using the design procedures of backstepping control with command filter. The semi-globally uniformly ultimately boundedness (SGUUB) stability and the tracking performance of the whole closed-loop systems could be guaranteed by using the Lyapunov approach. Finally, simulation results are provided to validate the present theoretical claims.

Dynamic Event-Triggered Adaptive Neural Network Decentralized Output-Feedback Control for Nonlinear Interconnected Systems With Hybrid Cyber Attacks and Its Application

Dynamic Event-Triggered Adaptive Neural Network Decentralized Output-Feedback Control for Nonlinear Interconnected Systems With Hybrid Cyber Attacks and Its Application

Sliding Mode Preview Repetitive Control for Interconnected Nonlinear Systems

Sliding Mode Preview Repetitive Control for Interconnected Nonlinear Systems

Decentralized prescribed-time control for interconnected nonlinear systems via output-feedback

Decentralized prescribed-time control for interconnected nonlinear systems via output-feedback

Switching Control of Nonlinear Interconnected Systems With Unknown Control Directions

Switching Control of Nonlinear Interconnected Systems With Unknown Control Directions

Adaptive Prescribed Time Fuzzy Control of Interconnected Nonlinear Systems and Its Applications: A Compensation-Based Approach

Adaptive Prescribed Time Fuzzy Control of Interconnected Nonlinear Systems and Its Applications: A Compensation-Based Approach

Dynamic Event-Sampled Control of Interconnected Nonlinear Systems Using Reinforcement Learning.

We develop a decentralized dynamic event-based control strategy for nonlinear systems subject to matched interconnections. To begin with, we introduce a dynamic event-based sampling mechanism, which relies on the system's states and the variables generated by time-based differential equations. Then, we prove that the decentralized event-based controller for the whole system is composed of all the optimal event-based control policies of nominal subsystems. To derive these optimal event-based control policies, we design a critic-only architecture to solve the related event-based Hamilton-Jacobi-Bellman equations in the reinforcement learning framework. The implementation of such an architecture uses only critic neural networks (NNs) with their weight vectors being updated through the gradient descent method together with concurrent learning. After that, we demonstrate that the asymptotic stability of closed-loop nominal subsystems and the uniformly ultimate boundedness stability of critic NNs' weight estimation errors are guaranteed by using Lyapunov's approach. Finally, we provide simulations of a matched nonlinear-interconnected plant to validate the present theoretical claims.

Adaptive Exponential Convergence State-Constrained Control for Interconnected Nonlinear Systems With Time-Varying Unknown Parameters

Adaptive Exponential Convergence State-Constrained Control for Interconnected Nonlinear Systems With Time-Varying Unknown Parameters

Event-Triggered Local Control for Nonlinear Interconnected Systems Through Particle Swarm Optimization-Based Adaptive Dynamic Programming

Event-Triggered Local Control for Nonlinear Interconnected Systems Through Particle Swarm Optimization-Based Adaptive Dynamic Programming

Distributed event-triggered fuzzy control for nonlinear interconnected systems

Distributed event-triggered fuzzy control for nonlinear interconnected systems

Event‐ driven‐observer‐based adaptive distributed sliding mode fault compensation control for interconnected nonlinear systems

AbstractIn this paper, an event‐driven‐observer‐based distributed adaptive sliding mode fault compensation control method is presented for a class of nonlinear interconnected systems in the occurrence of actuator faults and disturbances. First, a novel event‐based estimator is designed to acquire the state, fault, and lumped disturbance information. Then, by utilizing the observer outputs, an adaptive tracking‐error‐based event condition is established, based on which a distributed sliding mode fault compensation controller is designed. In the proposed control framework, on the one hand, to enhance the robustness and tracking accuracy, two adaptive parameters are constructed; on the other hand, an adaptive event‐based control mechanism is integrated to save the transmission resources. The Zeno phenomenon avoidance performance is analyzed and, finally, the applications to an inverted pendulum system and a machine power system are given to verify the fault tolerance ability of the proposed method.

Decentralized Adaptive Neural Inverse Optimal Control of Nonlinear Interconnected Systems.

Existing methods on decentralized optimal control of continuous-time nonlinear interconnected systems require a complicated and time-consuming iteration on finding the solution of Hamilton-Jacobi-Bellman (HJB) equations. In order to overcome this limitation, in this article, a decentralized adaptive neural inverse approach is proposed, which ensures the optimized performance but avoids solving HJB equations. Specifically, a new criterion of inverse optimal practical stabilization is proposed, based on which a new direct adaptive neural strategy and a modified tuning functions method are proposed to design a decentralized inverse optimal controller. It is proven that all the closed-loop signals are bounded and the goal of inverse optimality with respect to the cost functional is achieved. Illustrative examples validate the performance of the methods presented.

Output-Feedback Control of Interconnected Nonlinear Systems With Both State and Input Delays Based on Truncated Prediction

In this paper, we investigate decentralized output-feedback control of interconnected nonlinear systems with both state and input delays. Unlike existing related works, in this paper, both the state delays and the input delay of each subsystem are unknown and different from each other. In order to achieve global asymptotic stability of the interconnected nonlinear system based on output feedback, we construct a finite-dimensional observer based on the output information to asymptotically estimate the state information. Simultaneously, based on a truncated prediction of the system state over the delay period, a novel decentralized observer-based output-feedback controller is proposed for each subsystem. Then, under the framework of Lyapunov-Krasovskii functional, a set of conditions for the global asymptotic stability of the closed-loop system is determined. Finally, a simulation example is given to verify the effectiveness of the proposed control scheme.

Decentralized discrete adaptive sliding mode control for interconnected nonlinear systems

This article presents a study on controlling a class of large-scale systems composed of interconnected subsystems. The systems are represented by discrete block-oriented models with unknown parameters. This article proposes a novel adaptive sliding mode control strategy to address the tracking control problem and minimize the impact of interconnection. This approach leverages the robustness of sliding mode control against unmodeled dynamics and disturbances with the advantages of adaptive control. To adapt the system’s parameters, a recursive parametric estimation algorithm using least-squares techniques is employed. The proposed method offers benefits such as robustness against parameter uncertainties, handling of nonlinearities, improved tracking accuracy, and reduced chattering phenomena. The effectiveness of the method is demonstrated through the evaluation of two simulation examples.

Adaptive fuzzy decentralized optimal control for interconnected nonlinear systems with unmodeled dynamics via mixed data and event driven method

Adaptive fuzzy decentralized optimal control for interconnected nonlinear systems with unmodeled dynamics via mixed data and event driven method

Time-/Event-Triggered Adaptive Neural Asymptotic Tracking Control of Nonlinear Interconnected Systems With Unmodeled Dynamics and Prescribed Performance.

This article proposes two adaptive asymptotic tracking control schemes for a class of interconnected systems with unmodeled dynamics and prescribed performance. By applying an inherent property of radial basis function (RBF) neural networks (NNs), the design difficulties aroused from the unknown interactions among subsystems and unmodeled dynamics are overcome. Then, in order to ensure that the tracking errors can be suppressed in the specified range, the constrained control problem is transformed into the stabilization problem by using an auxiliary function. Based on the adaptive backstepping method, a time-triggered controller is constructed. It is proven that under the framework of Barbalat's lemma, all the variables in the closed-loop system are bounded and the tracking errors are further ensured to converge to zero asymptotically. Furthermore, the event-triggered strategy with a variable threshold is adopted to make more precise control such that the better system performance can be obtained, which reduces the system communication burden under the condition of limited communication resources. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed control scheme.

Design of distributed preview tracking controller for nonlinear interconnected systems with time‐delay

AbstractThe focus of this article is to design a distributed preview tracking controller for a class of nonlinear interconnected systems with time‐delay. By constructing the error systems with the information of previewable reference signal, and defining an performance index, the original preview tracking problem is converted into the control problem of the error system. Based on the relationship between the interconnection matrices and the input matrices, a distributed controller using not only the local state vector information but also the interconnection term information is designed for the error systems. Under this controller, the closed‐loop error system is decoupled. With the help of Lyapunov‐Krasovskii functional, the sufficient conditions, the asymptotic stability of the closed‐loop error system with disturbance attenuation level, are established. The distributed preview tracking controller, which is composed of state feedback, integrator, and preview feedforward, is acquired by integrating the controller of the error system. The proposed preview controller is applied to the chemical reactor recycles system and double‐parallel inverted pendulum system. The simulation verify that this controller is very effective in improving the tracking performance.