Adaptive Neural Network Output-feedback Control Research Articles

Distributed Adaptive Optimization Algorithm for High-Order Nonlinear Multi-Agent Stochastic Systems with Lévy Noise.

An adaptive neural network output-feedback control strategy is proposed in this paper for the distributed optimization problem (DOP) of high-order nonlinear stochastic multi-agent systems (MASs) driven by Lévy noise. On the basis of the penalty-function method, the consensus constraint is removed and the global objective function (GOF) is reconstructed. The stability of the system is analyzed by combining the generalized Itô's formula with the Lyapunov function method. Moreover, the command filtering mechanism is introduced to solve the "complexity explosion" problem in the process of designing virtual controller, and the filter errors are compensated by introducing compensating signals. The proposed algorithm has been proved that the outputs of all agents converge to the optimal solution of the DOP with bounded errors. The simulation results demonstrate the effectiveness of the proposed approach.

Observer-based Finite-time Control of Stochastic Non-strict-feedback Nonlinear Systems

This paper investigates the observer-based adaptive finite-time neural control issue of stochastic non-strict-feedback nonlinear systems. By establishing a state observer and utilizing the approximation property of the neural network, an adaptive neural network output-feedback controller is constructed. The controller solves the issue that the states of stochastic nonlinear system cannot be measured, and assures that all signals in the closed-loop system are bounded. Different from the existing adaptive control researches of stochastic nonlinear systems with unmeasured states, the proposed control scheme can guarantee the finite-time stability of the stochastic nonlinear systems. Furthermore, the effectiveness of the proposed control approach is verified by the simulation results.

Adaptive neural network output-feedback control of multiple Ackermann steering vehicles formation including motor dynamics with a guaranteed performance

A prescribed performance output-feedback formation control of electrically driven Ackermann steering robotic vehicles is addressed in this paper. Some constraints are imposed to relative distance and angle errors between the cars and a leader. Then, constrained errors are transformed into a new second-order Euler–Lagrange formulation of unconstrained errors via the prescribed performance function technique which inherits all structural properties of the robot dynamics. Based on dynamic surface control design, an observer-based proportional–integral–derivative virtual controller with a prescribed performance is proposed at the first step. Then, an actual controller is proposed at the actuator level to generate input voltage control signals. The proposed controller takes the following advantages: (1) the possible overshoots and controller singularities are avoided based on some predefined performances of relative distance and angle errors, (2) the controller does not require linear and angular velocity measurements, (3) the unknown nonlinearities and exogenous disturbances are effectively compensated by combining a neural network and adaptive robust controller, and (4) the actuator dynamics is compensated in the inner-loop while voltage control signals are directly generated for each robot motors. Lyapunov’s stability analysis proves the stability of the closed-loop control system. Finally, numerical simulation results will show the controller performance.

Observer-based Adaptive Neural Network Output-feedback Control for Nonlinear Strict-feedback Discrete-time Systems

This paper focuses on an observer-based output-feedback controller design for a nonlinear discrete-time system. The major characteristics of this system is that all of the subsystems are in strict-feedback form and all the states of the system are not measurable. An output tracking control problem is firstly considered in this paper. NNs are utilized to approximate unknown functions, while a state observer is designed to approximatethe unvailable states. An adaptive controller is designed on the basis of the backstepping technique. On the basis of the Lyapunov analysis approach, the boundedness of all the signals is provided. The feasibility of the proposed scheme is verified through a simulation example.

Output Feedback Adaptive Control for Stochastic Non-strict-feedback System with Dead-zone

This paper focuses on the problem of adaptive neural network (NN) control for a class of nonlinear stochastic non-strict feedback system with dead-zone input. A novel adaptive NN output feedback control approach is first proposed for stochastic non-strict feedback nonlinear systems. In order to solve the problem of dead-zone input, a linear decomposition method is proposed. On the basis of the state observer, an output feedback adaptive NN controller is designed by backstepping approach. It is shown that the proposed controller guarantees that all the signals of the closed-loop systems are semi-globally uniformly bounded in probability. Simulation results further illustrate the effectiveness of the proposed approach.

Output feedback control for constrained pure-feedback systems: A non-recursive and transformational observer based approach

In this paper we investigate the control problem of uncertain pure-feedback systems under time-varying output constraints using output information only. By making use of the salient cascade properties of pure-feedback systems as well as a novel scaling function, we convert the constrained system into a normal form without constraints. Then by using only one single neural network (NN) unit for nonlinear approximation and one high-gain observer for transformed state estimation, an adaptive NN output feedback control scheme is constructed. Different from existing results in the literature, our method exhibits the following features: (1) achieving semi-global stable control with only output feedback without imposing any additional restrictive condition; (2) avoiding the recursive design procedures required by some typical approaches such as backstepping; and (3) recovering the steady-state tracking performance under the state feedback. Besides, all the signals in the closed-loop are bounded and the output constraints are never violated. The effectiveness and flexibility of the developed method is demonstrated through control design and simulation on the non-trivial aircraft short-period dynamics.

Observer-based adaptive neural control for switched stochastic pure-feedback systems with input saturation

The purpose of this paper is to investigate the problem of adaptive neural network (NN) output-feedback tracking control for input saturated switched stochastic nonlinear systems in pure-feedback form with unmeasured states. In order to facilitate the controller design process, a dead zone-based model of saturation is implemented and radial basis function NNs (RBFNNs) are employed to approximate unknown nonlinear functions and to construct an NN switched nonlinear observer to cope with difficulties raised by the presence of immeasurable state variables. Based on the adaptive backstepping technique and Lyapunov function method, an adaptive NN output feedback control scheme is developed. Furthermore, it is proved that the proposed controller can provide that, under arbitrary deterministic switching, all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a small neighborhood of the origin. Finally, simulation examples are presented to validate the effectiveness of the proposed adaptive NN control approach.

Neural network‐based output‐feedback control for stochastic high‐order non‐linear time‐delay systems with application to robot system

This study is concerned with the output-feedback control problem for a class of stochastic high-order non-linear systems with time-varying delays. A distinctive feature of the control scheme is that the restrictions on delay-dependent drift and diffusion terms are greatly relaxed by using radial basis function neural network (NN) approximation approach. Furthermore, with the approach, the specific knowledge of NN nodes and weights is not required. Under some weaker conditions, by combining dynamic surface control technique with proper Lyapunov–Krasovskii functional, an adaptive NN output-feedback controller is designed constructively such that the closed-loop system is 4-moment (or mean square) semi-globally uniformly ultimately bounded. Finally, the control scheme is applied to both a practical stochastic robot system and a numerical system to demonstrate the effectiveness of the proposed approach.

Adaptive NN output-feedback control for stochastic time-delay nonlinear systems with unknown control coefficients and perturbations

This paper addresses the problem of adaptive output-feedback control for more general class of stochastic time-varying delay nonlinear systems with unknown control coefficients and perturbations. By using Lyapunov–Krasovskii functional, backstepping and tuning function technique, a novel adaptive neural network (NN) output-feedback controller is constructed with fewer learning parameters. The designed controller guarantees that all the signals in the closed-loop system are 4-moment (or mean square) semi-globally uniformly ultimately bounded (SGUUB). Finally, a simulation example is shown to demonstrate the effectiveness of the proposed control scheme.

Learning from adaptive neural network output feedback control of a unicycle-type mobile robot

This paper studies learning from adaptive neural network (NN) output feedback control of nonholonomic unicycle-type mobile robots. The major difficulties are caused by the unknown robot system dynamics and the unmeasurable states. To overcome these difficulties, a new adaptive control scheme is proposed including designing a new adaptive NN output feedback controller and two high-gain observers. It is shown that the stability of the closed-loop robot system and the convergence of tracking errors are guaranteed. The unknown robot system dynamics can be approximated by radial basis function NNs. When repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve guaranteed stability and better control performance, thereby avoiding the tremendous repeated training process of NNs.

Observer-based adaptive neural dynamic surface control for a class of non-strict-feedback stochastic nonlinear systems

The problem of adaptive output feedback stabilisation is addressed for a more general class of non-strict-feedback stochastic nonlinear systems in this paper. The neural network (NN) approximation and the variable separation technique are utilised to deal with the unknown subsystem functions with the whole states. Based on the design of a simple input-driven observer, an adaptive NN output feedback controller which contains only one parameter to be updated is developed for such systems by using the dynamic surface control method. The proposed control scheme ensures that all signals in the closed-loop systems are bounded in probability and the error signals remain semi-globally uniformly ultimately bounded in fourth moment (or mean square). Two simulation examples are given to illustrate the effectiveness of the proposed control design.

Learning from NN output feedback control of robot manipulators

In this paper, based on recently developed deterministic learning (DL) theory, we investigate the learning issue in neural network (NN) output feedback control of robot manipulators with unknown system dynamics and disturbance. Our objective is to learn the unknown closed-loop robot system dynamics while tracking to a periodic or periodic-like reference orbit with only joint angle measurements. Firstly, a high-gain observer (HGO) is used to estimate the joint velocities. An adaptive NN output feedback controller is then designed to guarantee the stability of the closed-loop robot system and the tracking performance when tracking a periodic or periodic-like reference orbit. Based on DL theory, when a partial persistence of excitation (PE) condition of the regression subvector is satisfied, part of the neural weights of the employed radial basis function (RBF) NN will converge to their optimal values. The unknown dynamics of robot manipulators can be learned by NN in a local region along the estimated state trajectory and the learned knowledge is stored in constant RBF networks. Secondly, the peaking phenomenon generated by the use of HGO and its adverse effect on learning are analyzed. If the gain of HGO is not chosen too high, the peaking phenomenon will be weakened and the accuracy of the estimated system states can still be guaranteed for learning from robot manipulators control. Thirdly, when repeating same or similar control tasks, the learned knowledge can be recalled and reused to achieve the guaranteed stability and better control performance with little effort. Finally, simulation studies are included to demonstrate the effectiveness of the proposed method.

Adaptive NN Control for a Class of Ctochastic Nonlinear Systems with Unmodeled Dynamics

Abstract This paper addresses the problem of adaptive neural networks output feedback control for a class of stochastic nonlinear system with unmodeled dynamics. Only a neural network (NN) is employed to compensate for all unknown nonlinear functions, so that the designed controller is simpler than the existing results and reduces the computation loads. With the concept of input-to-state practical stability (ISpS) and nonlinear small-gain theorem extended to the stochastic case, together with the RBF NN technique, an adaptive NN output feedback controller is proposed. It is shown that the solutions of the closed-loop system are bounded in probability.

Observer-based adaptive neural network control for nonlinear stochastic systems with time delay.

This paper considers the problem of observer-based adaptive neural network (NN) control for a class of single-input single-output strict-feedback nonlinear stochastic systems with unknown time delays. Dynamic surface control is used to avoid the so-called explosion of complexity in the backstepping design process. Radial basis function NNs are directly utilized to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. The proposed adaptive NN output feedback controller can guarantee all the signals in the closed-loop system to be mean square semi-globally uniformly ultimately bounded. Simulation results are provided to demonstrate the effectiveness of the proposed methods.

Adaptive neural network output feedback control of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive neural network (NN) backstepping output feedback control approach is proposed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, unmodeled dynamics, dynamical uncertainties and without requiring the measurements of the states. The NNs are used to approximate the unknown nonlinear functions, and a filter observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive NN output feedback control approach is constructed. It is mathematically proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are semi-globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. The simulation example and comparison results further justify the effectiveness of the proposed approach.