Control For Nonlinear Large-scale Systems Research Articles

Adaptive Fixed-time tracking control for large-scale nonlinear systems based on improved simplified optimized backstepping strategy.

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.

Decentralized event-trigger-based predefined-time adaptive control of large-scale nonlinear systems

Decentralized event-trigger-based predefined-time adaptive control of large-scale nonlinear systems

Composite adaptive exponential tracking control for large-scale nonlinear systems with sensor faults

The issue of composite adaptive exponential tracking control for a class of large-scale nonlinear systems under model uncertainties, external disturbances, along with multiplicative and additive time-varying sensor faults is considered in this paper. The command filtered backstepping approach is employed to address the “explosion of terms” issue inherent in standard backstepping method. A novel compensating system is incorporated to mitigate the effects of filtering errors and enhance the convergence of tracking errors. The composite estimation laws are designed by integrating the compensated tracking errors, prediction errors stemming from output estimators, along with the proportional and integral estimation errors of faulty terms. This on-line estimation framework enables achieving fast, robust and accurate estimation, even when employing low learning and modification gains. By incorporating modification terms with appropriate time-varying gains, it is demonstrated that the resulting system is globally exponentially stable. Finally, the effectiveness of the presented FTC approach is illustrated through two simulation examples.

Dynamic event-triggered control of large-scale nonlinear systems with sensor uncertainty

In this article, the dynamic event-triggered control problem is investigated for a class of large-scale nonlinear systems subject to sensor uncertainty. The unknown nonlinearities involved in each subsystem are assumed to be bounded by time-varying continuous functions multiplied by unknown constants and unmeasured states. To estimate the unmeasured states of each subsystem, a time-varying high-gain observer is constructed. Then, a dynamic event-triggered mechanism is proposed by introducing an internal dynamic variable in triggering function. Combined with the dual-domination approach, a dynamic event-triggered output feedback controller is developed for each subsystem. Subsequently, it is proved the convergence of all states is ensured based on the Lyapunov theory and the Zeno behavior can be avoided. Eventually, an example is presented to demonstrate the effectiveness of the proposed dynamic event-triggered control scheme.

Event-based adaptive active disturbance rejection control for nonlinear large-scale systems with input delay

This paper explores the active disturbance rejection control (ADRC) issue for interconnected large-scale systems with unmeasured states, input delay and a total disturbance composed of unknown constrained external disturbances. An enhanced extended state observer is built via fuzzy logic systems to identify system uncertainties and states and keep restraining the total disturbance of uncertain systems. When the triggering condition is met, the tracking differentiator component of the ADRC is able to reduce the communication overhead by successfully applying the backstepping control approach and the dynamic event-triggered mechanism. Meanwhile, the integral compensation method eliminates the negative effect cased by the input delay. Furthermore, the developed decentralised fuzzy control scheme can ensure that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded and Zeno behaviour does not occurs. Finally, an example is used to depict the validity of the control approach.

Adaptive Decentralized Fuzzy Resilient Control for Large-Scale Nonlinear Systems Against Stochastic Disturbances and DoS Attacks

Adaptive Decentralized Fuzzy Resilient Control for Large-Scale Nonlinear Systems Against Stochastic Disturbances and DoS Attacks

Decentralised adaptive neural finite-time prescribed performance control for nonlinear large-scale systems based on command filtering

In this research, the issue of decentralised adaptive neural finite-time prescribed performance control is discussed for nonstrict-feedback large-scale nonlinear interconnected systems subject to dead zones input and unknown control direction. The obstacle of ‘explosion of complex’ occurred in conventional backstepping design can be surmounted by adopting the command filter technique and nonlinearities are approximated by introducing an adaptive neural control approach. To handle the obstacles due to unknown directions and unknown interconnections, Nussbaum-type functions and two smooth functions are used and designed. Meanwhile, error compensation signals are introduced to deal with the problem associated with the dynamic surface method. To constraint the output tracking error within a predefined boundary in finite time, an improved performance function, i.e. finite-time performance function is introduced. Different from existing control results, the developed control methodology does not require any information on the boundedness of dead-zone parameters. It is further proved that the constructed controller not only assures the semi-global boundedness of all the controlled system signals, but also makes the output tracking errors reach within a predefined small set. Finally, both numerical and practical examples are supplied to further validate the effectiveness of the presented theoretic result.

Plug-and-Play Distributed Control of Large-Scale Nonlinear Systems.

A method to design plug-and-play (PnP) distributed controllers for large-scale nonlinear systems represented by interconnected Takagi-Sugeno fuzzy models with nonlinear consequent is presented in this article. From the combination of techniques to use multiple fuzzy summations and to explore the chordal decomposition of the interconnection graph associated with the large-scale nonlinear system, sufficient conditions for distributed stabilization are derived in terms of linear matrix inequalities (LMIs). Conditions specially designed to allow seamless subsystems plugging-in and unplugging operations from the large-scale system, without requiring the redesign of all previously tuned distributed controllers, are provided. The approach can be used together with fault detection and isolation (FDI) systems, and also in the context of mixed distributed and decentralized controllers operating in a network of interconnected systems. To illustrate the effectiveness of the proposed PnP approach, a network of nonlinearly coupled and heterogeneous Van der Pol oscillators is used in the numerical experiments.

Decentralized Adaptive Control of Large-Scale Nonlinear Systems With Time-Delay Interconnections and Asymmetric Dead-Zone Input

In this article, a decentralized adaptive control strategy is proposed for a class of large-scale nonlinear systems with interconnections. Each subsystem contains multiple state delays, asymmetric dead-zone inputs, and bounded time-varying disturbances. Every error subsystem is first decomposed according to input matrix assumption. Then, the decentralized adaptive controller is developed to handle uncertain functions in the subsystem. The nonlinear control gain function is constructed and used in adaptive control design. Two robust adaptive control schemes are, respectively, addressed for the cases of matched and mismatched time-delay nonlinearities. It is proved that all closed-loop signals are bounded, and the tracking error converges to an adjustable region. A simulation example is presented to show the effectiveness of the proposed method.

Finite-Time Control for Multiple Time-Delayed Fuzzy Large-Scale Systems Against State and Input Constraints

In this article, finite-time control is investigated for multiple time-delayed large-scale nonlinear systems against state and input constraints. There are multiple time-varying delays, intermittent actuator faults, and intermittent sensor faults in this model. Takagi–Sugeno fuzzy model is used to describe the nonlinear system. There are few tries to studying finite-time control for large-scale nonlinear systems. First, a dynamic output feedback controller is designed in this article to make the system finite-time bounded. Then, an augmented closed-loop model is constructed. Sufficient conditions of finite-time control are given by the fuzzy Lyapunov function. Finally, the feasibility of the approach is verified by two simulation examples.

Removing the feasibility conditions on adaptive fuzzy decentralized tracking control of large-scale nonlinear systems with full-state constraints

This work is dedicated to solving the adaptive fuzzy decentralized tracking control issue of large-scale nonlinear systems with full-state constraints. Different with barrier Lyapunov function, the main difference is that a novel nonlinear state-dependent function (NSDF) is introduced to prevent the state constraints being overstepped. Based on NSDF, the necessary feasibility conditions for virtual controllers are completely removed. Then, the prior knowledge of the unknown virtual control coefficients is no longer required since the original system is transformed via the new affine variable. Under the control strategy, three objectives on system performance are achieved: (a) all signals of the closed-loop system are bounded; (b) the subsystem output closely tracks the reference trajectory and original error is ultimately uniformly bounded; (c) the full-state constraints are not violated for all the time. At the end, two simulation examples are shown to verify the effectiveness of the control method.

Observer-based decentralized adaptive control for large-scale nonlinear systems with mixed dynamic interaction

The problem of decentralized adaptive control is investigated for a class of large-scale nonstrict-feedback nonlinear systems subject to dynamic interaction and unmeasurable states, where the dynamic interaction is related to both input and output items. First, the fuzzy logic system is utilized to tackle unknown nonlinear function with nonstrict-feedback structure. Then, by combining adaptive and backstepping technology, the proper output feedback controller is designed. Meanwhile, a fuzzy state observer is proposed to estimate the unmeasurable states. The proposed controller could guarantee that all the signals of the resulting closed-loop systems are bounded. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Notice of Retraction: Adaptive Decentralized Tracking Control for Nonlinear Large-Scale Systems

This article has been retracted at the request of the first and corresponding author, Dr. Karthik Chandran. The author has alerted the Editor-in-Chief of IJUFKS the reasons for the retraction: The proposed system was modelled with the incorrect data set. The system response has become incorrect because of this incorrect data set. Two percent of the information (mathematical assumptions) was taken from one paper without proper citation of the source.

Command filter-based adaptive fuzzy decentralized control for large-scale nonlinear systems

Command filter-based adaptive fuzzy decentralized control for large-scale nonlinear systems

$$\mathrm{H}_{\boldsymbol{\infty} }$$ output feedback control for large-scale nonlinear systems with time delay in both state and input

$$\mathrm{H}_{\boldsymbol{\infty} }$$ output feedback control for large-scale nonlinear systems with time delay in both state and input

Command filter-based event-triggered adaptive fixed-time output-feedback control for large-scale nonlinear systems

This paper studies the issue of event-triggered adaptive fixed-time control for uncertain large-scale nonlinear systems. A state observer is employed to estimate unknown states. By combining the technologies of command filter and backstepping control, a command filtered-based fixed-time adaptive control approach is proposed, which can cope with the issue of ‘complexity explosion’ in the backstepping design scheme. Meanwhile, a novel continuous hyperbolic tangent function is designed to handle the difficulty of fixed-time stability analysis with output-feedback control form, where two types of observer errors are needed in the designed Lyapunov function to achieve the fixed-time stability. Further, based on the event-triggered strategy, an event-triggered fixed-time adaptive controller is designed. Based on Lyapunov stability theory, it is proven that all signals of the closed-loop system are bounded. Finally, the effectiveness of the presented approach is validated by a simulation.

Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems With Time-Varying Output Constraints

This paper addresses the adaptive finite-time decentralized control problem for time-varying output-constrained nonlinear large-scale systems preceded by input saturation. The intermediate control functions designed are approximated by neural networks. Time-varying barrier Lyapunov functions are used to ensure that the system output constraints are never breached. An adaptive finite-time decentralized control scheme is devised by combining the backstepping approach with Lyapunov function theory. Under the action of the proposed approach, the system stability and desired control performance can be obtained in finite time. The feasibility of this control strategy is demonstrated by using simulation results.

Distributed model predictive control for nonlinear large-scale systems based on reduced-order cooperative optimisation

In this paper, a novel cooperative constrained distributed model predictive control algorithm is proposed to control the nonlinear interconnected constrained large-scale systems. In this algorithm, a novel reduced-order cooperative optimisation approach is proposed which is its main contribution that reconstructs and improves the global cost function of any local controller. In proposed algorithm, each local controller computes its optimal control by minimising the corresponding global cost function which is a combination of its own and its neighbouring subsystems’ cost functions. The sufficient conditions are derived to guarantee the recursive feasibility and closed-loop stability specifications to ensure the convergence of the overall states into the positive region which is the neighbourhood of origin. The performance of the proposed algorithm is illustrated via simulation results of a nonlinear large-scale cart-spring-damper system.

Retracted Article: Adaptive Decentralized Tracking Control for Nonlinear Large-Scale Systems

This article has been retracted at the request of the first and corresponding author, Dr. Karthik Chandran. The author has alerted the Editor-in-Chief of IJUFKS the reasons for the retraction: The proposed system was modelled with the incorrect data set. The system response has become incorrect because of this incorrect data set. Two percent of the information (mathematical assumptions) was taken from one paper without proper citation of the source.

Adaptive neural decentralized output-feedback control for nonlinear large-scale systems with input time-varying delay and saturation

For a class of nonstrict-feedback nonlinear large-scale systems with input delay, saturation and unknown virtual control gains, we propose a decentralized output-feedback control scheme in this paper. In the process of control design, a novel auxiliary system is proposed to overcome the difficulty caused by time-varying input delay. The convex combination technique is used to overcome the difficulty caused by unknown virtual control gains, and further construct the state observer. Then a decentralized output-feedback controller is designed by using the adaptive neural backstepping control approach. By Lyapunov stability theory, it is proved that the proposed decentralized output-feedback controller can ensure that tracking errors converge to a small neighborhood of the origin and other closed-loop states are bounded. Finally, the results of simulation example further verify the validity of this decentralized output-feedback control scheme.