Decentralized Event-Triggered Tracking of a Class of Uncertain Interconnected Nonlinear Systems Using Minimal Function Approximators

This paper investigates a decentralized tracker design problem using the filter-driven approximation technique in the presence of unknown interconnected nonlinearities. Compared with the existing recursive design methodologies using adaptive neural or fuzzy approximators for uncertain interconnected lower-triangular nonlinear systems, our main contribution is to develop the decentralized filter-driven approximation technique to compensate for unknown inherent and interconnected nonlinearities in the decentralized tracking scheme. The decentralized filter-driven approximators simply consist of a linear combination of first-order filtered signals of local state variables and a local control input, without nonlinear basis functions and weight adaptation laws used in the conventional adaptive function approximation technique. The stability analysis of the proposed decentralized tracking system is provided in the Lyapunov sense.

Decentralized adaptive stabilization of interconnected nonlinear systems with unknown non-symmetric dead-zone inputs

This article proposes a neural-network-based adaptive asynchronous event-triggered design strategy for the distributed consensus tracking of uncertain lower triangular nonlinear multi-agent systems under a directed network. Compared with the existing event-triggered recursive consensus tracking designs using multiple neural networks for each follower and continuous communications among followers, the primary contribution of this study is the development of an asynchronous event-triggered consensus tracking methodology based on a single-neural network for each follower under event-driven intermittent communications among followers. To this end, a distributed event-triggered estimator using neighbors' triggered output information is developed to estimate a leader signal. Subsequently, the estimated leader signal is used to design local trackers. Only a triggering law and a single-neural network are used to design the local tracking law of each follower, irrespective of unmatched unknown nonlinearities. The information of each follower and its neighbors is asynchronously and intermittently communicated through a directed network. Thus, the proposed asynchronous event-triggered tracking scheme can save communicational and computational resources. From the Lyapunov stability theorem, the stability of the entire closed-loop system is analyzed and the comparative simulation results demonstrate the effectiveness of the proposed control strategy.

This article investigates the problem of unified output constraints for a class of uncertain interconnected nonlinear systems, where the measurement of system states is affected by unknown drifts in the powers of the measurement functions. Compared to previous works on output constraints, the main challenge addressed in this article is the unavailability of the true system states during the controller design process and the nondifferentiability of the sensor's output functions. To achieve the control objectives, the following control scheme is proposed in this study. First, a novel barrier Lyapunov function is introduced, which is specifically designed to handle systems with unknown measurement drifts. This function can be uniformly applied to satisfy both scenarios of systems with or without output constraints. Second, the adding a power integrator (AAPI) technique and dynamic surface control (DSC) techniques are enhanced to effectively handle the unknown measurement drifts and avoid singularity problems in the controller design. The decentralized controller proposed in this article can realize that the outputs are strictly constrained within predefined boundaries and guarantees convergence of all system states to an arbitrarily small neighborhood. Finally, we provide two simulation examples to validate the effectiveness of our proposed control strategy.

In this paper, the decentralized robust output-feedback tracking control problem is investigated for a class of uncertain nonlinear interconnected systems subject to high order time varying disturbances. Firstly, generalized-proportional-integral observers (GPIOs) are designed for every subsystems such that the disturbances and unmeasured states can be recovered. Then, based on the estimation information of GPIOs and the output-feedback domination approach, a composite decentralized output-feedback controller is constructed. The proposed decentralized control scheme can not only handle the uncertain nonlinear interconnected terms, but also remove the influences the disturbances effectively via feedforward compensation manner. Finally, a practical example is conducted to demonstrate the effectiveness of the proposed control approach.

In this brief, we study the distributed consensus tracking control problem for multiple strict-feedback systems with unknown nonlinearities under a directed graph topology. It is assumed that the leader's output is time-varying and has been accessed by only a small fraction of followers in a group. The distributed dynamic surface design approach is proposed to design local consensus controllers in order to guarantee the consensus tracking between the followers and the leader. The function approximation technique using neural networks is employed to compensate unknown nonlinear terms induced from the controller design procedure. From the Lyapunov stability theorem, it is shown that the consensus errors are cooperatively semiglobally uniformly ultimately bounded and converge to an adjustable neighborhood of the origin.

This paper deals with an adaptive robust tracking control using a multilayer neural network (NN) for a class of nonlinear dynamic systems with unknown time varying state delays. Typical adaptive NN backstepping controllers for uncertain nonlinear systems with time-delay give rise to computation complexity caused by the the repeated derivatives of virtual controllers and nonlinear functions. Moreover, the combined techniques usually result in only uniformly ultimately bounded (UUB) stability caused by the inherent NN approximation error. This paper presents a control scheme that uses an integral sliding mode control as a feedback term and an adaptive neural controller as a feedforward term based on the desired compensation adaptive law (DCAL) technique. First, we develop a new DCAL formulation which avoids the explosion of complexity caused by the general NN backstepping scheme to compensate for nonlinear system uncertainties, bounded system disturbances, and unknown state time delays. Then, using a Lyapunov-Krasovskii (LK) functional, it is shown that the proposed controller renders the class of uncertain nonlinear time-delay systems asymptotically stable.

In this paper, we propose a new adaptive cooperative tracking control algorithm for a class of uncertain nonlinear multiagent systems with unknown control coefficients. It is assumed that there exist parametric uncertainties and unknown dynamics with the informed agent as well, and only a limited number of agents have the access to the state value of the informed agent. The proposed adaptive cooperative tracking control solves the problem of making all agents asymptotically track the desired trajectory specified by an informed agent based on the use of neural network parameterization of unknown dynamics of agents. The unknown bounds of neural network approximation errors are also estimated online. Using Lyapunov stability theorem, it is rigorously proved that asymptotically cooperative tracking can be achieved under the assumption that the sensing/communication topology among agents is undirected and connected. A simulation example is given to illustrate the effectiveness of the proposed control design.

This paper proposes a robust adaptive fuzzy control scheme for the uncertain nonlinear time-delay systems preceded by an unknown dead-zone. It is well known that time-delay and dead-zone characteristics are frequently encountered in various engineering systems. The dead-zone phenomena may occur in various components of control systems including sensors, amplifiers and actuators, especially in pneumatic valves, in electric servo motors and in hydraulic components. On the other hand, the time-delay phenomena are quite commonly found in chemical process, hydraulic systems and pneumatic systems. In most practice motion systems, the dead-zone characteristics are usually poorly known and may vary with time. Therefore, by using a description of a dead-zone and exploring the properties of this dead-zone model intuitively and mathematically, a robust adaptive fuzzy control method is presented without constructing the dead-zone inverse. According to some adaptive laws, the unknown nonlinear functions of the plant are approximated by the fuzzy logic system. Based on Lyapunov stability theorem, the proposed robust adaptive fuzzy control scheme can guarantee the robust stability of the whole closed-loop nonlinear time-delay system with an unknown dead-zone in the actuator and obtain good tracking performance as well. Finally, an example and simulation results are provided to show the efficiency of the proposed control method.

In this paper, asymptotic stability for a class of uncertain nonlinear neutral systems with a single time delay is considered. A new delay-dependent criterion is proposed to guarantee stability for such systems. The Lyapunov stability theorem and matrix inequalities are used to prove the main result. Finally, four numerical examples are given to demonstrate that our robust stability condition is less conservative than those reported in the control literature. The tolerable time-delay intervals that guarantee asymptotic stability of systems are larger than other results in those examples.

This paper considers the problem of exponential stability control for nonlinear uncertain networked control systems. Corresponding to the probability of the delays taking value in different interval. A new fuzzy networked control systems is built by employing the information of the probability distribution. With the Lyapunov stability theorem, the mean-square exponential stability conditions and the state feedback fuzzy controller design methods are given in terms of LMI.

This paper presents a finite-time fault-tolerant fuzzy adaptive controller for uncertain interconnected nonlinear systems in strict-feedback form. The controller addresses input saturation, state-dependent actuator faults, external disturbances, and unavailable states for measurement. To avoid any unexpected alteration due to failures, the proposed control scheme design addresses all types of actuator faults that are bias, drift, and loss of accuracy as additive faults, as well as a multiplicative fault that results in loss of effectiveness with nonaffine state-dependency. Restrictions on control gains and unmatched interconnections are eliminated, and the explosion complexity caused by recursive back-stepping designs is avoided. Fuzzy Systems approximate the unknown ideal control laws along side the acutator faults and disturbances. The stability analysis guarantees that tracking errors converge to a small compact set around the origin in finite time. Simulation results on a quad-rotor UAV and a mathematical system confirm the effectiveness of the proposed approach.

A novel neural network approximation scheme that is especially appropriate for adaptive control of nonlinear dynamical systems is proposed. In light of the new function approximation characterization, a neuro adaptive control framework for continuous- and discrete-time nonlinear uncertain dynamical systems is also presented. Specifically, the proposed neural network control framework is Lyapunov-based and, unlike standard neural network controllers guaranteeing ultimate boundedness, the framework guarantees partial asymptotic stability of the closed-loop system, that is, asymptotic stability with respect to part of the closed-loop system states associated with the system plant dynamics. The neuro adaptive controllers are constructed without requiring explicit knowledge of the system dynamics other than the assumption that the plant dynamics are continuous and piecewise continuously differentiable, and that the approximation error of uncertain system nonlinearities lie in a small gain-type norm bounded conic sector. This allows us to show that the standard neural network controllers are in fact capable of achieving partial asymptotic stability around the system equilibrium point for continuous-and discrete-time uncertain systems.

A robust adaptive neural network control approach is presented for a class of uncertain strict-feedback nonlinear systems with unknown dead-zone and disturbances. In the controller design, a single neural network is used to approximate the lumped unknown part of the system. By the approach, only one actual control law is implemented at the last step, and all the virtual control laws at intermediate steps need not be implemented actually. Thus, the designed controller is simpler in structure. Furthermore, the actual control law and one adaptive law can be given directly for the class of systems under study. The result of stability analysis shows that the proposed scheme can guarantee the uniform ultimate boundedness of all the closed-loop system signals, and the steady-state tracking error can be made arbitrarily small by appropriately choosing control parameters. A simulation example is given to demonstrate the effectiveness and merits of the proposed approach.

Disturbance-observer-based fuzzy terminal sliding mode control for MIMO uncertain nonlinear systems