Adaptive neural complementary sliding-mode control via functional-linked wavelet neural network

Chaos control can be applied in the vast areas of physics and engineering systems, but the parameters of chaotic system are inevitably perturbed by external inartificial factors and cannot be exactly known. This paper proposes an adaptive neural complementary sliding-mode control (ANCSC) system, which is composed of a neural controller and a robust compensator, for a chaotic system. The neural controller uses a functional-linked wavelet neural network (FWNN) to approximate an ideal complementary sliding-mode controller. Since the output weights of FWNN are equipped with a functional-linked type form, the FWNN offers good learning accuracy. The robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Without requiring preliminary offline learning, the parameter learning algorithm can online tune the controller parameters of the proposed ANCSC system to ensure system stable. Finally, it shows by the simulation results that favorable control performance can be achieved for a chaotic system by the proposed ANCSC scheme.     

Intelligent complementary sliding-mode control with dead-zone parameter modification

This paper proposes an intelligent complementary sliding-mode control (ICSMC) system which is composed of a computed controller and a robust controller. The computed controller includes a neural dynamics estimator and the robust compensator is designed to prove a finite L₂-gain property. The neural dynamics estimator uses a recurrent neural fuzzy inference network (RNFIN) to approximate the unknown system term in the sense of the Lyapunov function. In traditional neural network learning process, an over-trained neural network would force the parameters to drift and the system may become unstable eventually. To resolve this problem, a dead-zone parameter modification is proposed for the parameter tuning process to stop when tracking performance index is smaller than performance threshold. To investigate the capabilities of the proposed ICSMC approach, the ICSMC system is applied to a one-link robotic manipulator and a DC motor driver. The simulation and experimental results show that favorable control performance can be achieved in the sense of the L₂-gain robust control approach by the proposed ICSMC scheme.     

FPGA‐based adaptive dynamic sliding‐mode neural control for a brushless DC motor

In the adaptive neural control design, since the number of hidden neurons is finite for real‐time applications, the approximation errors introduced by the neural network cannot be inevitable. To ensure the stability of the adaptive neural control system, a switching compensator is designed to dispel the approximation error. However, it will lead to substantial chattering in the control effort. In this paper, an adaptive dynamic sliding‐mode neural control (ADSNC) system composed of a neural controller and a fuzzy compensator is proposed to tackle this problem. The neural controller, using a radial basis function neural network, is the main controller and the fuzzy compensator is designed to eliminate the approximation error introduced by the neural controller. Moreover, a proportional‐integral‐type adaptation learning algorithm is developed based on the Lyapunov function; thus not only the system stability can be guaranteed but also the convergence of the tracking error and controller parameters can speed up. Finally, the proposed ADSNC system is implemented based on a field programmable gate array chip for low‐cost and high‐performance industrial applications and is applied to control a brushless DC (BLDC) motor to show its effectiveness. The experimental results demonstrate the proposed ADSNC scheme can achieve favorable control performance without encountering chattering phenomena. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement

Although the PI or PID (PI/PID) controllers have many advantages, their control performance may be degraded when the controlled object is highly nonlinear and uncertain; the main problem is related to static nature of fixed-gain PI/PID controllers. This work aims to propose a wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller to solve the PI/PID controller problems. To create an adaptive nature for PI/PID controller and for online processing of the error signal, this work subtly employs a one to one offline trained self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in series connection with the fixed-proportional gain of conventional PI/PID controller. Offline training of the SRWNN-PU can be performed with any virtual training samples, independent of plant data, and it is thus possible to use a generalized SRWNN-PU for any systems. Employing a SRWNN-identifier (SRWNNI), the SRWNN-PU parameters are then updated online to process the error signal and minimize a control cost function in real-time operation. Although the proposed WNAP+ID is not limited to power system applications, it is used as supplementary damping controller of static synchronous series compensator (SSSC) of two SSSC-aided power systems to enhance the transient stability. The nonlinear time-domain simulation and system performance characteristics in terms of ITAE revealed that the WNAP+ID has more control proficiency in comparison to PID controller. As additional simulations, the features of the proposed controller are compared to those of the literature while some of its promising features like its fast noise-rejection ability and its high online adapting ability are also highlighted.     

基于Elman神经网络的浓相输送模糊控制系统

由于粉末物料的浓相输送系统存在严重的非线性和时变性,故要想建立其准确数学模型难度非常大,本文提出了使用模糊神经网络控制系统,并对于模糊控制规则由Elman神经网络联想记忆后提取,它不但可以获得最佳控制规则,而且响应速度快并能够进行在线进行规则的修正。经仿真实验,该控制器能够对粉末物料流量在一定范围内进行协调优化时实控制。     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

Recurrent wavelet neural backstepping controller design with a smooth compensator

Recurrent wavelet neural network (RWNN) has the advantages in its dynamic responses and information storing ability. This paper develops a recurrent wavelet neural backstepping control (RWNBC) scheme for multiple-input multiple-output (MIMO) mechanical systems. This proposed RWNBC comprises a neural controller and a smooth compensator. The neural controller using an RWNN is the principal tracking controller utilized to mimic an ideal backstepping control law; and the parameters of RWNN are online tuned by the derived adaptation laws from the Lyapunov stability theorem. The smooth compensator is designed to dispel the approximation error introduced by the neural controller, so that the asymptotic stability of the closed-loop system can be guaranteed. Finally, two MIMO mechanical systems, a mass-spring-damper system and a two-inverted pendulum system, are performed to verify the effectiveness of the proposed RWNBC scheme. From the simulation results, it is verified that the proposed RWNBC scheme can achieve favorable tracking performance without any chattering phenomenon.     

Adaptive hybrid control system using a recurrent RBFN-based self-evolving fuzzy-neural-network for PMSM servo drives

In this paper, an adaptive hybrid control system (AHCS) based on the computed torque control for permanent-magnet synchronous motor (PMSM) servo drive is proposed. The proposed AHCS incorporating an auxiliary controller based on the sliding-mode, a recurrent radial basis function network (RBFN)-based self-evolving fuzzy-neural-network (RRSEFNN) controller and a robust controller. The RRSEFNN combines the merits of the self-evolving fuzzy-neural-network, recurrent-neural-network and RBFN. Moreover, it performs the structure and parameter-learning concurrently. Furthermore, to relax the requirement of the lumped uncertainty, an adaptive RRSEFNN uncertainty estimator is used to adaptively estimate the non-linear uncertainties online, yielding a controller that tolerate a wider range of uncertainties. Additionally, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vector and higher order term in Taylor series. The online adaptive control laws are derived based on the Lyapunov stability analysis, so that the stability of the AHCS can be guaranteed. A computer simulation and an experimental system are developed to validate the effectiveness of the proposed AHCS. All control algorithms are implemented in a TMS320C31 DSP-based control computer. The simulation and experimental results confirm that the AHCS grants robust performance and precise dynamic response regardless of load disturbances and PMSM uncertainties.     

基于函数滑模控制器的机械手轨迹跟踪控制

提出一种基于函数滑模控制器(FSMC)的控制策略,用于不确定机械手的轨迹跟踪控制。首先,由动力学模型和滑模函数得到系统的不确定项;然后,利用RBF神经网络逼近系统不确定项,由于神经网络逼近存在误差,而且在初始阶段误差较大,设计函数滑模控制器和鲁棒补偿项对神经网络逼近误差进行补偿,以克服普通滑模控制器容易引起的抖振问题,同时提高系统的跟踪控制性能。基于李亚普诺夫理论证明了闭环系统的全局稳定性,仿真实验也验证了方法的有效性。     

采用遗传算法训练对角递归神经网络预测控制器

本文提出了一种基于广义预测控制的神经网络预测控制方案．预测控制器由对角递归 神经网络预测控制器和前向神经网络静态补偿器组成．两种神经网络均采用遗传算法进行训 练．仿真实验表明,对于带纯时延的非线性被控对象,采用遗传算法设计的对角递归神经网 络预测控制器具有令人满意的控制性能．     

Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach

This study proposes an indirect adaptive self-organizing RBF neural control (IASRNC) system which is composed of a feedback controller, a neural identifier and a smooth compensator. The neural identifier which contains a self-organizing RBF (SORBF) network with structure and parameter learning is designed to online estimate a system dynamics using the gradient descent method. The SORBF network can add new hidden neurons and prune insignificant hidden neurons online. The smooth compensator is designed to dispel the effect of minimum approximation error introduced by the neural identifier in the Lyapunov stability theorem. In general, how to determine the learning rate of parameter adaptation laws usually requires some trial-and-error tuning procedures. This paper proposes a dynamical learning rate approach based on a discrete-type Lyapunov function to speed up the convergence of tracking error. Finally, the proposed IASRNC system is applied to control two chaotic systems. Simulation results verify that the proposed IASRNC scheme can achieve a favorable tracking performance.     

An RBF neural network-based adaptive control for SISO linearisable nonlinear systems

An RBF neural network-based adaptive control is proposed for Single-Input and Single-Output (SISO) linearisable nonlinear systems in this paper. It is shown that a SISO nonlinear system is first linearised by using the differential geometric approach in the state space, and the linearised nonlinear system is then treated as a partially known system. The known dynamics are used to design a nominal feedback controller to stabilise the nominal system, and an adaptive RBF neural network-based compensator is then designed to compensate for the effects of uncertain dynamics. The main function of the RBF neural network in this work is to adaptively learn the upper bound of the system uncertainty, and the output of the neural network is then used to adaptively adjust the gain of the compensator so that the strong robustness with respect to unknown dynamics can be obtained, and the tracking error between the plant output and the desired reference signal can asymptotically converge to zero. A simulation example is performed in support of the proposed scheme.     

基于动态换向策略的开关磁阻电机控制方法

针对开关磁阻电机(SRM)的自身结构造成的转矩脉动问题以及驱动系统的抗干扰能力,提出了一种基于改进的动态换向策略与改进小波神经网络(WNN)的控制器方案。基于直接瞬时转矩控制(DITC)系统设计了一种动态的换向策略,减少关断以后的相电流,增加开通相电流的被激励程度,进而抑制系统的转矩脉动。此外基于小波神经网络改进了速度控制器,并将系统的输出转矩引入,使得系统能更好地应对扰动,增强系统的鲁棒性。仿真结果表明,与传统直接瞬时转矩控制和基于小波神经网络的转矩控制相比,转矩脉动减少了22.12％到64.14％,负载突变后静态误差仅为0.02r/min到0.01r/min,可以有效减弱转矩脉动且系统的抗扰能力显著提高。     

利用人工神经网络SNC进行最优励磁控制

BP神经网络是一种多层结构的映射网络。由于它计算简单、存储量小，并具有分布并行处理特性，所以是目前应用最广的一种模型。本文设计了一种BP神经网络的监督学习控制器（SNC），在线性最优励磁控制的基础上，利用3层BP神经网络对柴油发电机的控制过程进行监督学习。通过对网络的训练，使其能达到实时控制的目的。仿真结果表明，所设计的SNC在系统运行方式较大的变化范围内，都能提供很好的控制性能。     

基于快速算法的模糊神经网络自适应控制

针对过程控制中被控对象常具有非线性、不确定性及参数时变等复杂因素，而难以建立精确的数学模型的情况，提出了一种基于快速学习算法的模糊神经网络自适应预测控制方案。该方案用神经网络作辨识器，模糊神经网络作控制器来实现非线性系统的自适应预测控制。为了克服传统的梯度下降法收敛速度慢、容易陷入局部极小值的缺点，该方案采用递推最小二乘法训练模糊神经网络。仿真结果表明，该方案可以实现模糊控制和神经网络的优势互补，对不确定非线性系统具有很好的控制效果。     

一种自组织小波神经网络定子电阻估计器

定子电阻的准确估计是改善直接转矩控制低速性能的关键技术.为了提高定子电阻的在线估计精度和速度,本文将小波分析、自组织算法和神经网络技术相结合,提出了一种自组织小波神经网络定子电阻估计器.该网络继承了小波分析优异的局部特性和神经网络的自学习能力,具有较高的估计精度.并采用自组织算法对小波元的数量进行了离线优化,大大简化了网络结构,提高了在线估计的实时性.     

基于模糊小波神经网络的提升机恒减速制动系统的研究

针对提升机恒减速制动系统采用常规PID控制方式、模糊控制方式存在控制效果差的问题,提出了一种基于模糊小波神经网络的提升机恒减速制动系统的设计方案。该系统采用小波基函数作为模糊隶属函数,利用神经网络的自学习能力和小波基良好的局部特性来增强模糊控制的自适应能力,并采用遗传算法对小波基函数的平移、伸缩因子以及控制器的连接权值进行训练,使网络参数达到全局最优。Matlab仿真结果表明,该系统具有良好的动态特性和较高的控制精度。     

基于自适应模糊神经网络控制器的网络控制系统

网络控制系统中存在着时延、丢包、网络干扰等问题。针对网络控制系统中存在恶化系统的控制性能,甚至导致系统不稳定的因素,提出了一种基于自适应模糊神经网络控制器的网络控制系统,它能根据系统的实际输出与期望输出误差,利用自适应模糊控制和神经网络自学习的原理进行控制参数的自行调整,以符合控制系统的实际要求,同时,分析了网络延时,丢包率及网络干扰因素对系统性能的影响。利用TrueTime工具箱建立了包含自适应模糊神经网络控制器的网络控制系统的仿真模型,并将其分别与基于常规PID控制器的网络控制系统和基于模糊参数PID控制器的网络控制系统进行了比较。实验结果表明,在相同的网络环境下,基于自适应模糊神经网络控制器的网络控制系统的控制效果比基于常规的PID控制器和基于模糊参数PID控制器的要好,且具有较好的抗干扰能力和鲁棒性能。     

A neural net-based time-delay compensation scheme and disturbance rejection for pneumatic systems

In practical electro-pneumatic or hydraulic servo mechanism, there is usually an unavoidable operating delay time in the solenoid valves. This may sometimes cause closed-loop instability if the time delay is not treated carefully in the control system designs. Furthermore, pneumatic actuators can offer high performance at low cost, but are often suffered from external disturbances depending on the operating conditions. This paper proposes a non-model-based design approach for pneumatic actuating systems. In the proposed control system, a gain scheduled fuzzy-PID controller ensures tracking performance an adaptable wavelet neuro compensator compensates for time-delay of the control valve, and a disturbance rejecter diminishes influence from load changes. A condition ensuring the closed-loop stability is derived. The proposed design is experimentally verified to show its effectiveness.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.