Robust Adaptive Neural Control Research Articles

Robust adaptive neural tracking control for a class of electrically driven robots with time delays

This article addresses the problem of designing the robust tracking control for a class of uncertain electrically driven robots with time delays. The unknown time-delay uncertainty is assumed to be bounded by a function of all the state variables. By suitably choosing the Lyapunov–Krasovskii functionals, a novel adaptive/robust neural tracking control scheme is developed for the first time such that all the states and signals of the closed-loop time-delay robot system are bounded and the tracking error is shown to be uniformly ultimately bounded. By suitably designing the embedded current signal, the effect of time-delay uncertainty in the mechanical dynamics does not require to be incorporated into the current tracking error dynamics, and so the Lyapunov–Krasovskii functionals can be easily constructed in the stability analysis. Compared with the previous investigations of controlling robots the control scheme developed here can be extended to handle a broader class of electrically driven robots perturbed simultaneously by plant uncertainties, time-varying perturbations, and time-delay uncertainties. Finally, simulation examples are made to demonstrate the effectiveness of the proposed control algorithm.

A robust adaptive neural networks controller for maritime dynamic positioning system

This paper aims to develop a neural controller using the vectorial backstepping technique for dynamically positioned surface ships with uncertainties and unknown disturbances. The radial basis function networks are employed to compensate for the uncertainties of ship dynamics and disturbances in controller design. The advantage of the proposed control scheme is that there is no requirement of any priori knowledge about dynamics of ships and disturbances. It is shown that our proposed control law can regulate the position and heading of ships to the desired targets with arbitrarily small positioning error. Theoretical results on stability analysis indicate that our proposed controller guarantees uniformly ultimate boundedness of all signals of the closed-loop system. Simulation studies with comparisons on a supply ship are carried out, and results demonstrate the effectiveness of the proposed control scheme.

Dynamic Surface Neural Control for a Class of Nonlinear Time-Varying Delay Systems Preceded by Unknown Hysteresis

In this paper, a new robust adaptive neural dynamic surface control is proposed for a class of time-varying delay nonlinear systems preceded by backlash-like hysteresis. Compared with the present schemes of dealing with time-varying delay and hysteresis input, the main advantages of the proposed scheme are that the prespecified transient and steady state performance of tracking error can be guaranteed for the first time when using DSC to deal with the time-varing delays; the computational burden can be greatly reduced and the explosion of complexity problem inherent in backstepping control can be eliminated. It is proved that the new scheme can guarantee all the closed-loop signals semi-globally uniformly ultimate bounded. Simulation results are presented to demonstrate the validity of the proposed scheme.

Robust adaptive neural control for a class of uncertain non-linear time-delay systems with unknown dead-zone non-linearity

A robust adaptive neural network controller is proposed for a class of uncertain non-linear time-delay systems in strict feedback form with both completely unknown control gains and unknown non-symmetric dead-zone non-linearity based on backstepping design. The proposed design approach does not require a priori knowledge of the signs of the unknown control gains. The unknown time delays are compensated for constructing appropriate Lyapunov–Krasovskii functionals. By utilising integral Lyapunov design and sliding-mode control strategy, the controller singularity problem and the effect of dead-zone input non-linearity are avoided perfectly. From Lyapunov stability theorem, it is proved that the proposed design approach is able to guarantee semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system, and the tracking error of the system is proven to be converged to a small neighbourhood of the origin. The simulation results demonstrate the effectiveness of the proposed approach.

Robust adaptive fuzzy neural tracking control for a class of unknown chaotic systems

In this paper, an adaptive fuzzy neural controller (AFNC) for a class of unknown chaotic systems is proposed. The proposed AFNC is comprised of a fuzzy neural controller and a robust controller. The fuzzy neural controller including a fuzzy neural network identifier (FNNI) is the principal controller. The FNNI is used for online estimation of the controlled system dynamics by tuning the parameters of fuzzy neural network (FNN). The Gaussian function, a specific example of radial basis function, is adopted here as a membership function. So, the tuning parameters include the weighting factors in the consequent part and the means and variances of the Gaussian membership functions in the antecedent part of fuzzy implications. To tune the parameters online, the back-propagation (BP) algorithm is developed. The robust controller is used to guarantee the stability and to control the performance of the closed-loop adaptive system, which is achieved always. Finally, simulation results show that the AFNC can achieve favourable tracking performances.

A novel decentralised adaptive NN tracking control for double inverted pendulums

Adaptive trajectory-tracking control of double inverted pendulums (DIPs) connected by a spring is considered in this paper. By incorporating ‘dynamic surface control (DSC)’ approach and ‘minimal learning parameters (MLP)’ algorithm, a systematic procedure for the synthesis of a novel decentralised robust adaptive neural control scheme is developed. Two main advantages of the developed scheme are that: The computational burden has thus been greatly reduced. In addition, the stability in the sense of semi-globally uniform ultimate boundedness (SGUUB) of the closed-loop system is established via Lyapunov stability analysis, and the tracking error can be made arbitrarily small. Simulation results for the trajectory tracking of the DIPs are presented to demonstrate the effectiveness and good transient performance of the proposed scheme.

Robust adaptive neural tracking control for a class of switched affine nonlinear systems

In this paper, the adaptive tracking control problem for a class of switched affine nonlinear systems is investigated. We employ RBF neural networks (RBF NNs) to approximate unknown nonlinear functions. Due to the existence of approximation errors of the neural networks and external disturbance, we, respectively, utilize sliding mode method and H ∞ method as the robust controller to enhance system robustness and maintain boundedness. In addition, admissible switching laws are constructed and the weights of RBF NNs updated laws are chosen by switched Lyapunov function approach. With the two proposed methods, we can both prove that the resulting closed-loop switched system is robustly stable and uniformly ultimately bounded (UUB), and the output tracking errors converge to 0. Finally, we give a simulation example to demonstrate the proposed methods and do a comparative analysis.

DSC approach to robust adaptive NN tracking control for a class of MIMO systems

A robust adaptive neural tracking controller is proposed for a class of MIMO non-linear systems with strongly coupled interconnections. With the help of RBF neural networks (NN) as approximator, a unified and systematic procedure is developed by fusion of 'dynamic surface control (DSC)' with 'minimal learning parameters' algorithm. As a result, both problems of 'explosion of complexity' and 'curse of dimension' are solved synchronously, especially, the number of updated parameters for each subsystem is reduced to two, which can reduce the computational burden to an extreme extent, consequently ease the implementation of the algorithm. Additionally, the possible controller singularity problem can be removed and the stability of the closed-loop system is guaranteed. Simulation results validate the effectiveness of the proposed scheme.

Robust wavelet-based adaptive neural controller design with a fuzzy compensator

In this paper, a robust wavelet-based adaptive neural control (RWANC) with a PI type learning algorithm is proposed. The proposed RWANC system is composed of a wavelet neural controller and a fuzzy compensation controller. The wavelet neural control is utilized to approximate an ideal controller and the fuzzy compensation controller with a fuzzy logic system in it is used to remove the chattering phenomena of conventional sliding-mode control completely. In the RWANC, the learning algorithm is derived based on the Lyapunov function, thus the closed-loop system's stability can be guaranteed. The chaotic system control has become an emerging topic in engineering community since the uncontrolled system displays complex, noisy-like and unpredictable behavior. Therefore, the proposed RWANC approach is applied to a second-order chaotic nonlinear system to investigate the effectiveness. Through the simulation results, the proposed RWANC scheme can achieve favorable tracking performance and the convergence of the tracking error and control parameters can be accelerated by the developed PI adaptation learning algorithm.

Robust adaptive neural network synchronization controller design for a class of time delay uncertain chaotic systems

In this paper, a robust adaptive neural network synchronization controller is proposed for two chaotic systems with input time delay and uncertainty. The studied chaotic system may possess a wide class of nonlinear time-delayed input uncertainty. The radial basis function (RBF) neural network is used to approximate the unknown continuous bounded function item of the time delay uncertainty via appropriate weight value updated law. With the output of RBF neural network, a robust adaptive synchronization control scheme is presented for the time delay uncertain chaotic system. Finally, a simulation example is used to illustrate the effectiveness of the proposed synchronization control scheme.

Robust Adaptive Neural Control of a Class of MIMO Nonlinear Systems

In this paper we present a robust adaptive control for a class of uncertain continuous time multiple input multiple output (MIMO) nonlinear systems. Multiple multi-layer neural networks are employed to approximate the uncertainty of the nonlinear functions, and robustifying control terms are used to compensate for approximation errors. All parameter adaptive laws and robustifying control terms are derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability of the closed-loop system is guaranteed, and the tracking error asymptotically converges to zero. Simulations performed on a two-link robot manipulator illustrate the approach and its performance.

Robust adaptive neural fuzzy control for autonomous redundant non-holonomic mobile modular manipulators

This paper discusses the trajectory-following issues for autonomous redundant non-holonomic mobile modular manipulators. An integrated dynamic modelling method is proposed. A Robust Adaptive Neural Fuzzy Controller (RANFC) is presented to control the end-effector to follow desired spacial trajectories. The proposed algorithm provides a new solution to stabilise redundant robotic self-motions. The RANFC does not need precise a priori dynamic parameters and can suppress bounded external disturbance. Furthermore, the RANFC does not need any off-line training phases and can incorporate human expert knowledge easily. Simulation results for a real mobile modular manipulator validate the proposed algorithm.

Robust adaptive single neural control for a class of uncertain nonlinear systems with input nonlinearity

This paper presents an adaptive single neural controller for a class of uncertain nonlinear systems subject to a nonlinear input. A new type of neuron called auto-tuning neuron with three adjustable parameters will be introduced to construct a single neural controller. From the concept of the sliding mode control, a simple adaptation law, minimizing the value of a designed sliding condition based on a modified MIT rule, is developed for online updating these parameters in the auto-tuning neuron, even if the nonlinear plant considered is with the uncertainty, external noisy perturbation, and nonlinear input. Lastly, a controlled well-known Duffing–Holmes chaotic system is illustrated to show the effectiveness of the proposed neural controller.

ROBUST ADAPTIVE NEURAL FUZZY CONTROLLER WITH MODEL UNCERTAINTY ESTIMATOR FOR MANIPULATORS

In this paper, a new robust adaptive neural fuzzy controller (RANFC) for manipulators is proposed. The proposed controller uses the fuzzy logic inverse dynamic model output (approximated torques/fo...

Robust adaptive single neural control for a class of uncertain nonlinear systems with input nonlinearity

This paper presents an adaptive single neural controller for a class of uncertain nonlinear systems subject to a nonlinear input. A new type of neuron called auto-tuning neuron with three adjustable parameters will be introduced to construct a single neural controller. From the concept of the sliding mode control , a simple adaptation law, minimizing the value of a designed sliding condition based on a modified MIT rule, is developed for online updating these parameters in the auto-tuning neuron, even if the nonlinear plant considered is with the uncertainty, external noisy perturbation, and nonlinear input. Lastly, a controlled well-known Duffing–Holmes chaotic system is illustrated to show the effectiveness of the proposed neural controller.

Online adaptive fuzzy neural identification and control of a class of mimo nonlinear systems

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for identification and control of a class of uncertain multiple-input-multiple-output (MIMO) nonlinear systems. The proposed controller has the following salient features: 1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically; 2) online learning ability of uncertain MIMO nonlinear systems; 3) fast learning speed; 4) fast convergence of tracking errors; 5) adaptive control, where structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; 6) robust control, where global stability of the system is established using the Lyapunov approach. Simulation studies on an inverted pendulum and a two-link robot manipulator show that the performance of the proposed controller is superior.

Robust adaptive control of robot manipulators using generalized fuzzy neural networks

This paper presents a robust adaptive fuzzy neural controller (AFNC) suitable for motion control of multilink robot manipulators. The proposed controller has the following salient features: (1) self-organizing fuzzy neural structure, i.e., fuzzy control rules can be generated or deleted automatically according to their significance to the control system and the complexity of the mapped system and no predefined fuzzy rules are required; (2) fast online learning ability, i.e., no prescribed training models are needed for online learning and weights of the fuzzy neural controller are modified without any iterations; (3) fast convergence of tracking errors, i.e., manipulator joints can track the desired trajectories very quickly; (4) adaptive control, i.e., structure and parameters of the AFNC can be self-adaptive in the presence of disturbances to maintain high control performance; and (5) robust control, where asymptotic stability of the control system is established using the Lyapunov theorem. Experimental evaluation conducted on an industrial selectively compliant assembly robot arm demonstrates that excellent tracking performance can be achieved under time-varying conditions.

Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems

This paper presents a robust adaptive neural control design for a class of perturbed strict feedback nonlinear system with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks, while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions." With the utilization of iterative Lyapunov design and neural networks, the proposed design procedure expands the class of nonlinear systems for which robust adaptive control approaches have been studied. The design method does not require a priori knowledge of the signs of the unknown virtual control coefficients. Leakage terms are incorporated into the adaptive laws to prevent parameter drifts due to the inherent neural-network approximation errors. It is proved that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals.. The control performance can be guaranteed by an appropriate choice of the design parameters. Simulation studies are included to illustrate the effectiveness of the proposed approach.

UNCERTAIN CHAOTIC SYSTEM CONTROL VIA ADAPTIVE NEURAL DESIGN

Though chaotic behaviors are exhibited in many simple nonlinear models, physical chaotic systems are much more complex and contain many types of uncertainties. This paper presents a robust adaptive neural control scheme for a class of uncertain chaotic systems in the disturbed strict-feedback form, with both unknown nonlinearities and uncertain disturbances. To cope with the two types of uncertainties, we combine backstepping methodology with adaptive neural design and nonlinear damping techniques. A smooth singularity-free adaptive neural controller is presented, where nonlinear damping terms are used to counteract the disturbances. The differentiability problem in controlling the disturbed strict-feedback system is solved without employing norm operation, which is usually used in robust control design. The proposed controllers can be applied to a large class of uncertain chaotic systems in practical situations. Simulation studies are conducted to verify the effectiveness of the scheme.

ONLINE ADAPTIVE FUZZY NEURAL IDENTIFICATION AND CONTROL OF A CLASS OF MIMO NONLINEAR SYSTEMS

This paper presents a robust Adaptive Fuzzy Neural Controller (AFNC) suitable for identification and control of a class of uncertain MIMO nonlinear systems. The proposed controller has the following salient features: (1) Self-organizing fuzzy neural structure, i.e. fuzzy control rules can be generated or deleted automatically; (2) Online learning ability of uncertain MIMO nonlinear systems; (3) Fast learning speed; (4) Adaptive control; (5) Robust control, where global stability of the system is established using the Lyapunov approach. Simulation example is included to confirm the validity and performance of the proposed control algorithm.