Control Of Nonlinear Interconnected Systems Research Articles

General output constraints control of interconnected nonlinear systems with sensor faults

This paper delves into the problem of generalized output-constrained control for interconnected nonlinear systems, where sensor faults are modeled as time-varying unknown functions. First, a barrier function related to an output transformation function is proposed, which can meet the diverse constraint requirements by adjusting some parameters and functions. Second, the sensor fault is described as an unknown time-varying function, and its difficulties to the controller are successfully overcome with the help of adaptive control technology. Then, an output related function is introduced into the controller to handle the interconnected functions without approximators, while retaining the uncertainties in the interconnected functions. Based on the dynamic surface control technology, an adaptive general output constraint fault-tolerant controller is proposed. With the aid of the Lyapunov stability theory, it rigorously proves that the output satisfies the pre-given constraint requirements, and all signals of the closed-loop systems are bounded. Finally, the numerical simulation is presented to illustrate the validity of the proposed algorithm.

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

In this paper, we propose decentralized prescribed-time control schemes for a class of interconnected nonlinear systems by using time-varying high-gain domination approach. It is shown that prescribed-time exact regulation can be achieved by a linear observer based controller in the presence of completely unknown interactions. Besides, without significantly modifying the original controller structure, we develop a singularity-free controller with bounded time-varying feedback to drive the system state to converge to a compact set within a preset time, and ultimately converge to zero. In both of these approaches, the controller and observer designs have a dual architecture, thereby obviating the need for the separation principle (which might not be valid for nonlinear systems with mismatched uncertainties). In addition, the relationship between the upper bound of the time-varying gain and the stability of the closed-loop system is thoroughly analyzed.

Switching Control of Nonlinear Interconnected Systems With Unknown Control Directions

Switching Control of Nonlinear Interconnected Systems With Unknown Control Directions

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

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

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

This paper deals with the asynchronous distributed event-triggered control of continuous-time nonlinear interconnected systems. The nonlinear dynamics of the subsystems are represented by Takagi–Sugeno fuzzy models via the sector nonlinearity approach. Moreover, the nonlinear interconnections among the subsystems are assumed to be known and sector-bounded. In the event-triggered control setup, where the state information is asynchronously available to the local controllers only in specific time instants, it is necessary to deal with the asynchronous premise variables because they introduce extra difficulties to derive suitable co-design conditions. To deal with this issue, we propose a new triggering strategy such that the local event-triggering mechanisms (ETMs) counteract the effects of asynchronous premise variables. With this new distributed ETM, a co-design condition is proposed, and the existence of a strictly positive minimum inter-event time is proved to exclude Zeno behaviour. Moreover, a multi-objective optimization procedure is introduced to enlarge the estimate of the domain of attraction of the closed-loop equilibrium and minimize the number of transmissions provided by the ETMs. Finally, the approach is validated through the synchronization of interconnected oscillators.

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

In this work, based on the adaptive critic learning algorithms and fuzzy logic systems, the decentralized optimal control problem for interconnected nonlinear systems with unmodeled dynamics is investigated by applying a mixed data and event driven method. By introducing a modified performance index function for each auxiliary subsystem, the considered control problem of the whole interconnected system can be transformed into a group of event-driven optimal control problems of the auxiliary subsystems. Then, a three-layer neural network (NN) identifier is employed to reconstruct the completely unknown functions. Based on the obtained data-driven model, a single-critic NN is developed to solve the modified HJB equation. Meanwhile, a novel adaptive controller is designed by fuzzy logic systems to deal with the dynamical uncertainties cased by the unmodeled dynamics. Finally, a robot arm example is given to verify the effectiveness of the proposed control 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.

Distributed extended state observer design and output feedback anti‐disturbance control for nonlinear interconnected systems: A dynamic memory event‐triggered mechanism

AbstractThis paper investigates a distributed extended state observer design and output feedback anti‐disturbance control for nonlinear interconnected systems (ISs) with multiple disturbances. First, a distributed extended state observer with a sparse observer communication network is established to estimate the system states and external disturbances. Then, an output feedback controller with a disturbance compensation (DC) item is designed, where a novel dynamic memory event‐triggered mechanism (DMETM) is proposed to save communication resources. Third, under the proposed controller, the closed‐loop signals are bounded and the system states and the estimation errors converge to zero. Additionally, the system outputs satisfy performance. Finally, the simulation examples demonstrate the effectiveness of the DMETM in reducing the number of data transmissions compared to the dynamic event‐triggered mechanism (ETM).

Adaptive backstepping control for nonlinear interconnected systems with prespecified-performance-driven output triggering

This paper considers a class of nonlinear large-scale interconnected systems and proposes an event-triggered decentralized controller by using adaptive output feedback backstepping. To save the communication resource, the output measurements are transmitted to the controller based on a novel double-events-triggered (DET) mechanism driven by transient performance request. Due to the use of triggered mechanism, only discontinuous output signals can be utilized such that the derived virtual controls are discontinuous, of which the derivatives cannot be computed as often done in standard backstepping design procedure. To overcome this difficulty, a new error signal decomposition and compensation mechanism is proposed to quantify and compensate for the difference between the virtual controls derived by using the event-triggered measurement signals and the real-time system outputs. Through a nonsmooth stability analysis where the closed-loop stability is considered under different conditions of output data transmissions, it is shown that the tracking error remains within a prespecified performance bound all the time. In contrast to the existing backstepping results addressing event-triggered outputs, the designed DET mechanism does not require the virtual control signals to be reversely transmitted to the sensor side from the controller side and guarantees the communication burden can be reduced under tracking performance guarantees.