This paper investigates predefined time quantized feedback control for nonlinear large-scale systems with state quantization. To address the discontinuities in virtual control signals caused by quantized states and unmeasured states, a command filter is introduced to smooth control inputs, while a state observer based on fuzzy logic systems (FLSs) is designed to estimate unmeasured states. Furthermore, the hyperbolic tangent function is employed to eliminate control singularities. A quantized-state-based controller is developed using the backstepping technique and predefined time stability criteria, which introduces challenges in stability analysis. To tackle these challenges, a novel stability analysis framework is proposed by integrating predefined time Lyapunov stability theory and Lagrange’s Mean Value Theorem. Finally, simulations are conducted to demonstrate the effectiveness of the proposed control strategy.

The generalized homogenization-based protocol is proposed, which provides a novel direction for the challenging problem of finite-time consensus tracking (FTCT) control in singular multiagent systems (SMASs) in this paper. The proposed FTCT protocol is continuous and can be regarded as an upgrade of the distributed protocol for asymptotically stable analysis. Then, the homogeneity conditions of the closed-loop systems under the proposed protocol are derived, providing an important prerequisites for FTCT analysis. The FTCT problem for SMASs is successfully addressed by combining the construction of canonical homogeneous norm Lyapunov function and impulse free analysis. Finally, the simulation results are provided to validate the effectiveness of the proposed method.

This article studies the distributed observer-based event-triggered (ET) optimal control problem for nonlinear multiagent systems (NMASs) over jointly connected digraphs. Since agents cannot get the leader under jointly connected digraphs, a distributed observer is constructed to estimate the unknown leader. At the same time, to enhance the efficiency of communication resources utilization between agents, an ET communication mechanism is developed to schedule the agent communication. Subsequently, neural networks (NNs) are adopted to handle the unknown nonlinearities, and an NN state observer is established to reconstruct the unmeasurable states. By utilizing the backstepping technique and adaptive dynamic programming theory, a distributed observer-based ET optimal control algorithm is developed, in which a critic network is designed with a proposed weight updating law to estimate the cost function. It is proven that the presented ET optimal control approach ensures that all signals of NMASs are uniformly ultimately bounded (UUB), and the cost function is minimized. At last, the theoretical results are applied to marine surface vehicles (MSVs) to validate the efficiency of the developed optimal control strategy.

This article introduces a study on the secure control of nonlinear multiagent systems (NMASs) that are vulnerable to denial-of-service (DoS) attacks, with the added complexity that system states are not directly observable. To tackle the inherent nonlinear dynamics, a fuzzy logic systems (FLSs) framework is employed as a modeling tool. Faced with the obstacle of inaccessible system states and outputs during DoS attacks, this article introduces a switching-type secure estimator. This advanced estimator is designed to accurately reconstruct the system state from sporadic output data, ensuring continuous monitoring despite interruptions. By seamlessly integrating the switching-type secure estimator with the average dwell time (ADT) method and leveraging Lyapunov stability theory, the authors have developed an output-feedback secure control strategy. This strategy not only maintains system stability but also guarantees the convergence of consensus tracking errors, even in the presence of unknown states and ongoing DoS attacks. Finally, in order to prove the effectiveness of the consensus security controller, the practicability and reliability of the proposed control scheme are verified by simulation experiments.

This paper addresses the challenge of adaptive fuzzy event-triggered bipartite consensus control for a class of nonlinear multi-agent systems (MASs) with full-state asymmetric constraints, incorporating both cooperative and adversarial communication between agents. To ensure consensus tracking performance in the presence of asymmetric full-state constraints, the nonlinear MASs are augmented with an enhanced nonlinear function. Fuzzy logic systems (FLSs) are employed to handle unfamiliar nonlinearities effectively. To reduce the communication burden and enhance transient performance, an event-triggered control strategy is introduced, accompanied by the construction of an observer utilizing triggered output signals to assess unmeasured states. Furthermore, the control scheme is formulated by integrating dynamic surface control (DSC) and adaptive backstepping techniques. The validity of the proposed approach is verified through Lyapunov’s theory, demonstrating that all signals within the closed-loop system are bounded. Simulation results further support the effectiveness of the proposed methodology.Note to Practitioners—With the rapid development and deep integration of intelligent control theory, the control design of MASs is still challenging in many engineering problems. However, in practical applications, there is not only cooperation but also competition between agents, and the system states are unmeasured and subject to various forms of constraints. To solve this problem, we introduce an improved nonlinear transformation function. When network resources are limited, continuous communication is sometimes not feasible in engineering applications. To minimize the communication burden, we propose an event-triggered control strategy, which combines dynamic surface control technology and adaptive backstepping technology to design the trigger controller. The proposed scheme can effectively save resources and deal with various constraints of system states and may be applied to other similar engineering fields.

This article studies the adaptive neural network (NN) event-triggered secure control issue for stochastic nonlinear systems subject to sensor attacks. NNs are adopted to identify unknown nonlinear dynamics, and an NN state estimator is established to address the issue resulting from unmeasurable states. An NN observer is proposed to estimate unknown sensor attack signals. To save limited communication resources and reduce the number of controller updates, an event-triggered control (ETC) scheme is introduced. Then, an adaptive NN event-triggered secure control algorithm is designed by backstepping control method. The results demonstrate the stability of the control system and its consistent convergence in tracking errors under sensor attacks. Finally, simulations are shown to verify the effectiveness of the investigated theory.

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.

This article focuses on studying an adaptive fuzzy predefined-time tracking control problem for uncertain high-order nonlinear interconnected systems with input quantization. The considered plants contain unknown nonlinear functions, quantized input signals, and external disturbances. Fuzzy logic systems (FLSs) are employed to estimate the unknown nonlinear functions, and by using its structural properties, the difficulty of designing the state variable functions is simplified. By employing adaptive backstepping recursive technique combined with power-based Lyapunov functions, the predefined-time control strategy is presented. Notably, a novel predefined-time filter is used to avoid repeated differentiation of virtual control functions. By applying predefined-time Lyapunov stability theory, the stability analysis demonstrates that all signals in the closed-loop systems remain bounded and the tracking error converges within a predefined settling time. Ultimately, a simulation example is provided to illustrate the validity of the proposed control strategy.

In various practical applications, collaborative inspection systems in which multiple agents work together to accomplish inspection tasks are becoming increasingly important for enhancing operational efficiency. This paper addresses the routing problem in multi-agent collaborative inspection systems, where certain inspection points require the simultaneous presence of multiple agents to perform inspection operations. A novel approach using max-plus algebra is presented to model the collaborative inspection process and it provides a foundation for research and applications in system control and scheduling optimization. The max-plus linear (MPL) model is then converted into a Mixed Integer Linear Programming (MILP) formulation to tackle the routing problem with complex constraints inherent in the collaborative scene. Experimental results validate that the proposed MPL model and MILP approach reduce inspection completion time and waiting time at collaborative points.

Network pruning is a figurative model compression technique designed to lighten and accelerate neural network models. Most existing pruning methods prioritize the selection of filters by their importance or apply regularization based on the properties of individual filters, neglecting the internal connections within combinations of multiple filters. This work introduces a pruning method termed Combinational Filter Deletion (CFD), which incorporates a straightforward yet effective evaluation metric based on the diversity of filter combination distributions to reveal the characteristics inherent to multiple filter interactions. CFD enables the exploration of an expanded search space, offering a greater array of choices and leveraging the intrinsic information of conventional layers. Moreover, this method is both general and non-exclusive, capable of enhancing the efficacy of other single-filter-based pruning techniques.

This paper investigates adaptive neural network filtering control for uncertain nonlinear systems with general model state and input quantization. The plants under consideration contain quantized states, quantized input, and unknown nonlinear system functions. A universal quantizer is established for both system states and control input. In the control design process, neural networks and the command filter are used to approximate the unknown nonlinear system functions and overcome the discontinuities of virtual control signals, respectively. A new command filtering-based control strategy is proposed using the backstepping design technique. It is testified that the proposed control approach can guarantee that the closed-loop signals are semi-global uniform ultimate boundedness. A simulation example is presented to further demonstrate our proposed scheme’s effectiveness.Note to Practitioners—This work is motivated by the quantized control problem for a class of nonlinear systems with state and input quantization. In modern control engineering applications, quantization plays a crucial role due to the prevalent use of digital processors that operate with finite precision arithmetic. It is valuable and inevitable to minimize information flow, reduce communication burden, and improve system security. However, quantization will introduce significant discontinuous characteristics and strong nonlinearity, which may decrease the system’s performance and even drive the closed-loop system to instability. This paper demonstrates how to use backstepping and adaptive control methods with command filter to complete controller design and deal with the quantization effects. Therefore, it provides a feasible approach for engineering applications.

In this article, a fuzzy adaptive fixed-time asymptotic consistent control scheme is developed for a class of nonlinear multiagent systems (NMASs) with a nonstrict-feedback (NSF) structure. In the control process, a fixed-time consistency control method without control singularity is proposed by combining fuzzy logic systems (FLSs) with good approximation capability, fixed-time stability theory, and plus power integration techniques. Then, by using Barbalat's Lemma, the asymptotic stability of tracking errors and the boundedness of the controlled systems are successfully achieved, which means that the tracking errors can converge to zero in a fixed time. Finally, the effectiveness of the designed control scheme is demonstrated by a simulation example.

In this article, the fuzzy adaptive fixed-time bipartite output consistent tracking problem of nonlinear coopetition multi-agent systems (MASs) under a signed directed graph is studied. The fuzzy logic system (FLS) is used in the control scheme to approximate the unknown nonlinear dynamics. Under the framework of adaptive backstepping recursive design, an adaptive fuzzy fixed-time bipartite output consistent control method is proposed. In addition, to further enhance tracking performance, a fixed-time prescribed performance function (FTPPF) is introduced into the control design process, ensuring that the bipartite consensus tracking errors converge within a fixed time to the predefined boundary. By constructing the Lyapunov function, the fixed-time stability of the closed-loop system is proved. Eventually, the feasibility and effectiveness of the proposed adaptive bipartite output consensus control method are verified by the numerical simulation example.

This article considers the finite-time consensus control issue of high-order multi-input and multi-output nonlinear multiagent systems. In the process of establishment, this article uses fuzzy logic systems to identify unknown nonlinear dynamics. Then, according to the backstepping recursive technique and finite-time stability criterion, a fuzzy adaptive finite-time consensus control scheme is designed. At the same time, a communication-restricted control scheme is established according to the dynamic event-triggered strategy with a relative threshold to reduce the execution time of the controller and save communication resources. In addition, a novel integral Lyapunov function is established by introducing the adding power integrator technology, which proves the stability and convergence of consensus tracking errors of closed-loop systems. Finally, taking a marine surface vehicle as an example, the availability of the established event-triggered consensus control scheme is described.

This article studies the problem of an adaptive fuzzy predefined-time tracking control approach for a type of uncertain nonstrict-feedback high-order nonlinear systems with input quantization. The considered plants contain unknown nonlinear functions, input quantization, and external disturbances. Based on the backstepping recursive technique and predefined-time stability criterion, a fuzzy adaptive predefined-time control strategy is presented. To address the difficulties posed by the uncertain nonlinearities within the original systems, the fuzzy logic systems are incorporated into estimate the unknown nonlinear functions, while power integrator technology is used to overcome the hurdle presented by high-order terms. Using the predefined-time Lyapunov stability theory, the system stability analysis is provided, and it is proved that all signals in the closed-loop system are bounded within the preset time interval. Ultimately, the effectiveness of the presented control approach is corroborated through numerical simulation.

This article addresses the issue of the fuzzy adaptive prescribed performance control (PPC) design for nonstrict feedback multiple input multiple output (MIMO) nonlinear systems in finite time. Unknown nonlinear functions are handled via fuzzy-logic systems (FLSs). By combining the adaptive backstepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which can not only avoid the computational complexity issue but also improve the control performance in contrast to the traditional DSC control methods. Furthermore, to make the tracking errors have the prescribed performance in finite time, a new Lyapunov function is constructed by considering the transform error constraint. Based on the designed Lyapunov functions, it is proved that all the signals of the controlled systems are semiglobal practical finite-time stability (SGPFS). Finally, a simulation example is provided to illustrate the feasibility and validity of the put forward control scheme.

In this article, an adaptive fault-tolerant control (FTC) method and a fractional-order dynamic surface control (DSC) algorithm are jointly proposed to deal with the stabilization problem for a class of multiple-input-multiple-output (MIMO) switched fractional-order nonlinear systems with actuator faults and arbitrary switching. In each MIMO subsystem and each switched subsystem, the neural networks (NNs) are utilized to identify the complicated unknown nonlinearities. A fractional filter DSC technology is adopted to conquer the issue of "explosion of complexity," which may occur when some functions are repeatedly derived. The common Lyapunov function method is used to restrain arbitrary switching problems in the system, and the actuator compensation technique is introduced to tackle the failure faults and bias faults in the actuators. By combining the backstepping DSC design technique and fractional-order stability theory, a novel NN adaptive switching FTC algorithm is proposed. Under the operation of the proposed algorithm, the stability and control performance of the fractional-order systems can be guaranteed. Finally, a simulation example of a permanent magnet synchronous motor (PMSM) system reveals the feasibility and effectiveness of the developed scheme.

In this article, the research on fixed-time tracking control problem of multiple-input multiple-output nonlinear systems with nonstrict feedback control structure is carried out. By means of the fuzzy logic systems, the unknown system nonlinear dynamics are identified, and the “algebraic loop problem” is solved. Then, through the combination of adaptive backstepping recursive technology and adding power integration technology, a nonsingular fixed-time adaptive tracking control scheme is presented. Under the action of the presented control scheme, the system can track the specified reference signal within a fixed time independent of the initial state of the system, and the tracking control error can converge to zero asymptotically. Next, on the basis of the fixed-time Lyapunov stability theory, the practical fixed-time stability of the closed-loop system is theoretically demonstrated. Finally, two simulations verify the validity and practicability of the proposed control scheme.

This article investigates the event-triggered predefined time output feedback control design problem for nonlinear interconnected systems with nonstrict feedback control structures. Compared with the existing event-triggered output feedback control design schemes, the most significant contribution of this article is that the system stability time can be preset directly. Fuzzy logic systems (FLSs) and FLSs-based state observer deal with unknown nonlinear dynamics and unmeasured states. Combining with dynamic surface control technology and event-triggered mechanism based on switching threshold strategy, an event-triggered predefined time decentralized output feedback control method is proposed, in which a predefined time filter is designed to solve the computational complexity problem. In addition, the algebraic loop problem and control singular problem are also solved, respectively, by applying the property of fuzzy basis function and L’Hospital’s rule. Utilizing the predefined time Lyapunov stability theory, the system stability analysis is given. Finally, the effectiveness and practicability of the proposed control method are verified by practical system simulation cases.

This article investigates the finite-time adaptive fuzzy prescribed performance formation control problem for high-order nonlinear multiagent systems. Fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics. By combining the dynamic surface control technique and backstepping recursive design, an adaptive fuzzy prescribed performance formation control algorithm is developed. To reduce unnecessary transmission of network resources, a novel event-triggered mechanism is proposed in the control method. Subsequently, based on the adding power integral method and finite-time stability theory, it is proved that all signals of the controlled system are bounded and the tracking errors do not exceed the prescribed performance bounds in a finite time. Simulation results are given to verify that the presented formation control scheme achieves desired results.

This article studies the nonsingular fixed-time control problem of multiple-input multiple-output (MIMO) nonlinear systems with unmeasured states for the first time. A state observer is designed to solve the problem that system states cannot be measured. Due to the existence of the unknown system nonlinear dynamics, neural networks (NNs) are introduced to approximate them. Then, through the combination of adaptive backstepping recursive technology and adding power integration technology, a nonsingular fixed-time adaptive output feedback control algorithm is proposed, which introduces a filter to avoid the complicated derivation process of the virtual control function. According to the fixed-time Lyapunov stability theory, the practical fixed-time stability of the closed-loop system is proven, which means that all signals of the closed-loop system remain bounded in a fixed time under the proposed algorithm. Finally, the effectiveness of the proposed algorithm is verified by the numerical simulation and practical simulation.

In this article, the high-order nonlinear system is commonly studied in an underactuated weakly coupled mechanical system, the control design is difficult from the tractional control design for nonlinear systems. Thus, we study the finite-time fuzzy adaptive error constraint control problem for stochastic high-order nonlinear nonstrict feedback systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, a new error transfer variable is used to achieve the prescribed performance. Based on adding a power integrator technique and adaptive backstepping recursive control, a novel adaptive fuzzy finite-time prescribed performance control scheme is developed. By utilizing stochastically finite-time stable theory, the proposed control method can guarantee that the high-order system is semi-global finite-time stable in probability. Finally, both numerical and practical simulations are provided to verify the feasibility and effectiveness of the developed control method.

This article studies the fuzzy adaptive output feedback control design problem for nonstrict feedback multi-input–multi-output nonlinear systems with full-states prescribed performance in finite time. Fuzzy logic systems are introduced to solve the problem of unknown nonlinear dynamics. And on this basis, a fuzzy-based state observer is designed to observe the unmeasurable state. Further, by combining the adaptive back-stepping control algorithm and the nonlinear filters, a novel dynamic surface control (DSC) method is proposed, which not only solves the computational complexity explosion problem inherent in the back-stepping control algorithm, but also improves the control performance, in contrast to the traditional DSC methods with linear filters. Besides, to further improve the tracking performance under the structure of full-states prescribed performance, a new Lyapunov function is designed considering the transform error constraint. Based on the Lyapunov theory, the stability of the closed-loop system is analyzed to ensure that all signals of the closed-loop system are semiglobal practical finite-time stability. Finally, to elaborate on the feasibility and effectiveness of the proposed control method, a simulation example is provided.

The aim of this article is to study a fuzzy-based decentralized adaptive control strategy for the nonstrict-feedback fractional-order nonlinear large-scale systems with unknown control directions. In each step of the recursive processes, the fuzzy logic systems are employed to identify unknown nonlinear functions. To handle the difficulties caused by unknown control directions, a Nussbaum function technique is adopted. Furthermore, by introducing the dynamic surface control technique into the adaptive backstepping recursive design algorithm, a fuzzy-based decentralized adaptive control strategy is formulated. Both the stability of the controlled system and the convergence of the tracking errors are proved by constructing the fractional-order Lyapunov functions. Finally, the validity and effectiveness of the designed decentralized control scheme are confirmed via two simulation examples.

This article studies the fuzzy adaptive finite-time consensus control problem for high-order nonlinear multiagent systems with unknown nonlinear dynamics. In control design,fuzzy logic systems (FLSs) are adopted to approximate the unknown nonlinear dynamics, and under the frameworks of adaptive backstepping recursive design and finite-time stability theory, an adaptive fuzzy finite-time consensus control method is developed. To save communication resources and reduce the numbers of controller execution times, a dynamic event-triggered mechanism with a relative threshold is established. Subsequently, an event-triggered-based finite-time fuzzy adaptive control scheme is formulated. Furthermore, by constructing novel integral-type Lyapunov functions and adding a power integrator technique, the finite-time stability of the closed-loop system and the convergence of consensus tracking errors are proved. Finally, a numerical simulation example is provided to verify the effectiveness of the proposed adaptive event-triggered consensus control method.

This article investigates the problem of finite-time fuzzy adaptive event-triggered control design for stochastic nonlinear nonstrict feedback systems with unmodeled dynamics. The fuzzy logic systems are adopted to identify the unknown nonlinearities and a state observer is designed to estimate the unmeasured states. Using backstepping recursive design and combining it with a varying threshold event-triggered condition, a novel event-triggered-based fuzzy adaptive finite-time control algorithm is developed, where the dynamical signal function is employed to deal with the unmodeled dynamics. A power form of the errors is used to ensure a continuous stabilizer. The semi-global finite-time stability in probability of the closed-loop system is proved based on an It∧o differential equation and finite-time stability theory. Simulations are provided to verify the effectiveness of the developed control algorithm.

This article investigates the problem of neural network (NN)-based adaptive backstepping control design for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance. NNs are used to study the uncertain control plants, and the problem of unmodeled dynamics is tackled by the combination of the changing supply function and the dynamical signal function methods. The outstanding contribution of this article is that based on the finite-time performance function (FTPF), a modified finite-time adaptive NN control design strategy is proposed, which makes the controller design simpler. Eventually, by using the Itô's differential lemma, the backstepping recursive design technique, and the FTPFs, a novel adaptive prescribed performance tracking control scheme is presented, which can guarantee that all the variables in the control system are bounded in probability, and the tracking error can converge to a specified performance range in the finite time. Finally, both numerical simulation and applied simulation examples are provided to verify the effectiveness and applicability of the proposed method.

Due to the particularity of the fractional-order derivative definition, the fractional-order control design is more complicated and difficult than the integer-order control design, and it has more practical significance. Therefore, in this article, a novel adaptive switching dynamic surface control (DSC) strategy is first presented for fractional-order nonlinear systems in the nonstrict feedback form with unknown dead zones and arbitrary switchings. In order to avoid the problem of computational complexity and to continuously obtain fractional derivatives for virtual control, the fractional-order DSC technique is applied. The virtual control law, dead-zone input, and the fractional-order adaptive laws are designed based on the fractional-order Lyapunov stability criterion. By combining the universal approximation of neural networks (NNs) and the compensation technique of unknown dead-zones, and stability theory of common Lyapunov function, an adaptive switching DSC controller is developed to ensure the stability of switched fractional-order systems in the presence of unknown dead-zone and arbitrary switchings. Finally, the validity and superiority of the proposed control method are tested by applying chaos suppression of fractional power systems and a numerical example.

In this article, we propose the swarm control for a self-organized system with fixed and switching topology, which can realize aggregation, dispersion, or switching formation when swarm moves. The self-organized system can automatically construct the communication topology for intelligent units in swarm. Swarm control can realize aggregation and dispersion of intelligent units based on its communication topology when swarm moves. The proposed swarm control, in which distances between the related intelligent units are time varying, is different from traditional swarm consensus or swarm formation maintenance. To design swarm control, we define the normalization adjacency matrix and normalization degree matrix based on communication topology. The communication topology is automatically generated based on relation-invariable persistent formation. Depending on whether the communication topology changes or not, the swarm control can be classified as fixed topology and switching topology. Then, the swarm control with fixed and switching topology is designed and analyzed, respectively. The swarm control can realize stability asymptotically when topology is fixed and realize stability in finite time when topology is switched. The simulation results show that the proposed approaches are effective.

This paper solves the stochastically finite-time control problem for uncertain stochastic nonlinear systems in nontriangular form. The considered controlled plants are different from the previous results of finite-time control systems, which are the multiple-input and multiple-output (MIMO) stochastic systems with the unknown functions consisting of all states, stochastic disturbance, and immeasurable states. Fuzzy logic systems and a state filter are used to model the uncertain systems and estimate the immeasurable states, respectively. Based on the finite-time theory and Itȏ differential equation, a novel stochastically finite-time stability theorem is first raised. By combining the novel criterion and backstepping technique, an adaptive fuzzy stochastically finite-time control method is proposed. It is testified that all signals in the closed-loop signals are semiglobal finite-time stable in probability, and the tracking performances are well. Simulation example results further show the effectiveness of the proposed approach.

This paper solves the finite-time switching control issue for the nonstrict-feedback nonlinear switched systems. The controlled plants contain immeasurable states, arbitrarily switchings, and the unknown functions which are constructed with the whole states. Neural network is used to simulate the uncertain systems and a filter-based state observer is designed to estimate the immeasurable states in this paper, respectively. Based on the backstepping recursive technique and the common Lyapunov function method, a finite-time switching control method is presented. Due to the developed finite-time control strategy, the closed-loop signals can be ensured to be bounded under arbitrarily switchings, and the outputs of systems can quickly track the desired reference signals in finite time. The effectiveness of the proposed method is given through its application to a mass-spring-damper system.

This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.

This paper solves the finite-time decentralized control problem for uncertain nonlinear large-scale systems in nonstrict-feedback form. The considered controlled plants are different from the previous results of finite-time control systems, which are the nonstrict-feedback large-scale systems with the unknown functions consisting of all states, interactions, and immeasurable states. Fuzzy logic systems and a filter-based state observer are utilized to model uncertain systems and deal with the immeasurable states, respectively. By combining the backstepping recursive design with Lyapunov function theory, a finite-time adaptive fuzzy decentralized control approach is raised. It is testified that the developed control strategy can guarantee that the closed-loop signals are bounded, and the outputs of systems have satisfactory tracking performance in a finite time. A quadruple-tank process system is given to testify the effectiveness and applicability of the proposed approach.

In this paper, the problem of adaptive fuzzy decentralized optimal control is investigated for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear large-scale systems contain the unknown nonlinear functions and unmeasured states. By utilizing the fuzzy logic systems to approximate the unknown nonlinear functions and cost functions, a fuzzy state observer is established to estimate the unmeasured states. The control design is divided into two phases. First, by using the state observer and the backstepping design technique, a feedforward decentralized controller with parameters adaptive laws is designed, by which the original controlled strict-feedback nonlinear large-scale system is transformed into an equivalent affine nonlinear large-scale system. Second, by using adaptive dynamic programming theory, a feedback decentralized optimal controller is developed for the equivalent affine nonlinear system. The whole adaptive fuzzy decentralized optimal control scheme consists of a feedforward decentralized controller and a feedback decentralized optimal controller. It is shown that the proposed adaptive fuzzy decentralized optimal control approach can guarantee that all the signals in the closed-loop system are bounded, and the tracking errors converge to a small neighborhood of zero. In addition, the proposed control approach can guarantee that the cost functions are minimized. Simulation results are given to demonstrate the effectiveness of the proposed control approach.

This paper deals with the problem of adaptive fuzzy output feedback control for a class of stochastic nonlinear switched systems. The controlled system in this paper possesses unmeasured states, completely unknown nonlinear system functions, unmodeled dynamics, and arbitrary switchings. A state observer which does not depend on the switching signal is constructed to tackle the unmeasured states. Fuzzy logic systems are employed to identify the completely unknown nonlinear system functions. Based on the common Lyapunov stability theory and stochastic small-gain theorem, a new robust adaptive fuzzy backstepping stabilization control strategy is developed. The stability of the closed-loop system on input-state-practically stable in probability is proved. The simulation results are given to verify the efficiency of the proposed fuzzy adaptive control scheme.

This paper studies adaptive fuzzy output feedback tracking control problem for nonstrict-feedback switched nonlinear systems. The switched systems under consideration contain unknown nonlinearities, unmeasured states, and unknown deadzones. Fuzzy logic systems are utilized to approximate the unknown nonlinearities, and a switched fuzzy state observer is designed, and thus, the immeasurable states are estimated via it. In the framework of observer-based output feedback control, and by using the certainty equivalence deadzone inverse, a novel adaptive fuzzy output feedback control design method with the parameters adaptation laws is developed. The stability of the closed-loop system and the convergence of the tracking error are proved based on Lyapunov function and the average dwell-time methods. Two simulation examples are provided to check the effectiveness of the proposed approach.

This paper investigates an adaptive fuzzy tracking control design problem for single-input and single-output uncertain nonstrict feedback nonlinear systems. For the cases of the states measurable and the states immeasurable, fuzzy logic systems are separately adopted to approximate the unknown nonlinear functions or model the uncertain nonlinear systems. In the unified framework of adaptive backstepping control design, both adaptive fuzzy state feedback and observer-based output feedback control design schemes are proposed. The stability of the closed-loop systems is proved by using Lyapunov function theory. The simulation examples are provided to confirm the effectiveness of the proposed control methods.

This paper discusses the adaptive fuzzy partial tracking errors constrained control problem for a class of uncertain stochastic nonlinear systems. The concerned systems contain the unknown nonlinear functions, unmeasured state variables, and the switching signal with average dwell time. The fuzzy logic systems are first used to approximate the unknown nonlinear functions, and a switched fuzzy state observer is developed for estimating the unmeasured states. By introducing the performance function and error transformation into the backstepping dynamic surface control design, a new observer-based adaptive fuzzy control design approach is developed. By employing the multiple Lyapunov function and the average dwell time methods, it is proved that all the signals of the resulting closed-loop system are bounded, and the partial tracking errors are confined all times within the prescribed bounds. A simulation example is provided to show the effectiveness of the proposed approach.

In this paper, the problem of adaptive fuzzy output-feedback control is investigated for a class of uncertain switched nonlinear systems in strict-feedback form. The considered switched systems contain unknown nonlinearities, dead-zone, and immeasurable states. Fuzzy logic systems are utilized to approximate the unknown nonlinear functions, a switched fuzzy state observer is designed and thus the immeasurable states are obtained by it. By applying the adaptive backstepping design principle and the average dwell time method, an adaptive fuzzy output-feedback tracking control approach is developed. It is proved that the proposed control approach can guarantee that all the variables in the closed-loop system are bounded under a class of switching signals with average dwell time, and also that the system output can track a given reference signal as closely as possible. The simulation results are given to check the effectiveness of the proposed approach.

In this paper, a partial tracking error constrained fuzzy output-feedback dynamic surface control (DSC) scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems. The considered MIMO nonlinear systems contain unknown functions and without the requirement of their states being available for the controller design. With the help of fuzzy logic systems identifying the MIMO unknown nonlinear systems, a fuzzy adaptive observer is established to estimate the unmeasured states. By transforming the tracking errors into new virtual error variables and based on the DSC backstepping recursive design technique, a new adaptive fuzzy output-feedback control method is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the partial state tracking errors are confined all times within the prescribed bounds. The simulation results and comparisons with the previous control approaches confirm the effectiveness and utility of the proposed scheme.

In this paper, an adaptive decentralized fuzzy output feedback stabilization problem is investigated for a class of uncertain stochastic nonlinear large-scale systems. The addressed stochastic nonlinear systems contain unknown nonlinear functions, unknown control direction, and without the measurements of the states. Fuzzy logic systems are used to identify the unknown nonlinear functions, and a fuzzy state filter observer is designed to estimate the unmeasured states. To solve the problem of the unknown control direction in decentralized control design, Nussbaum-type functions are introduced and new property on Nussbaum-type function is proved. Based on the backstepping recursive design technique and the established Nussbaum function property, a new robust stabilization control approach is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded in probability, and the observer errors and system output converge to a small neighborhood of the origin. A simulation example is provided to show the effectiveness of the proposed approach.