全局稳定的机器人CMAC标称补偿跟踪控制研究

提出了一种新的小脑模型(Cerebellar Model Articulation Controller,CMAC)神经网络标称补偿控制器.采用二阶扩展B样条CMAC网络平滑逼近机器人标称模型,消除了常规神经网络控制对输入的严格假设.为了确保系统闭环的全局稳定性,采用Lyapunov直接法设计网络权值的更新律,并引入非线性反馈项完全抵消补偿的残留项.未知的CMAC逼近误差和系统随机干扰,通过一个简洁的鲁棒自适应律估计.最后,针对两自由度机器人的仿真实例验证了所提算法的有效性.     

基于CMAC的非参数化近似策略迭代增强学习

为解决在线近似策略迭代增强学习计算复杂度高、收敛速度慢的问题，引入CMAC结构作为值函数逼近器，提出一种基于CMAC的非参数化近似策略迭代增强学习（NPAPI-CMAC）算法。算法通过构建样本采集过程确定CMAC泛化参数，利用初始划分和拓展划分确定CMAC状态划分方式，利用量化编码结构构建样本数集合定义增强学习率，实现了增强学习结构和参数的完全自动构建。此外，该算法利用delta规则和最近邻思想在学习过程中自适应调整增强学习参数，利用贪心策略对动作投票器得到的结果进行选择。一级倒立摆平衡控制的仿真实验结果验证了算法的有效性、鲁棒性和快速收敛能力。     

多温区电加热炉自适应PID控制方法

多温区电加热炉是一种典型的多输入多输出系统(MIMO),存在着耦合性、不确定性和非线性的控制难点.针对此问题,提出了一种自适应PID控制方法.该方法先以解耦减小系统耦合性,再利用小脑模型关节控制器(CMAC)在线学习系统的未知不确定性及外部扰动,证明了CMAC神经网络在线逼近的收敛性和自适应控制方案的稳定性.实验结果表明,该控制方法有效地控制了各个温区的温度,提高了控制性能,具有实际应用意义.     

一类不确定系统的自适应滑模迭代学习控制

本文针对一类在有限时间内执行重复任务的不确定非线性系统状态跟踪问题,提出一种自适应滑模迭代学习控制方法,在存在初始偏移的情况下也能实现对参考轨迹的完全收敛.本文通过设计全饱和自适应迭代学习更新律,估计参数和非参数不确定性以及未知期望控制输入,并将估计值限制在指定界内,避免估计值的正向累加.文章设计的自适应滑模迭代学习控制方法对系统模型的信息需求少,在对系统非参数不确定性的上界估计时不需要Lipschitz界函数已知.本文给出严格的理论分析,证明闭环系统所有信号的一致有界性以及跟踪误差的一致收敛性,并通过仿真验证所提控制方法的有效性.     

不确定性机器人系统自适应鲁棒迭代学习控制

利用Lyapunov方法, 提出了一种不确定性机器人系统的自适应鲁棒迭代学习控制策略, 整个系统在迭代域里是全局渐近稳定的. 所考虑的机器人系统同时包含了结构和非结构不确定性. 在设计时, 系统的不确定性被分解成可重复性和非重复性两部分, 并考虑了系统的标称模型. 在所提出的控制策略中, 自适应策略用来估算做法确定性的界, 界的修正与迭代学习控制量一样的迭代域得以实现的. 计算机仿真表明本文提出的控制策略是有效的.     

环卫车辆轨迹跟踪系统的无模型自适应迭代学习控制

针对环卫车辆周期重复性工作特点,考虑模型时变以及未知扰动问题,提出一种基于无模型自适应迭代学习的环卫车辆轨迹跟踪控制方法.首先,针对环卫车辆建立了两轮移动机器人的运动学模型,然后,给出带时变参数和非线性不确定项的迭代域下全格式动态线性化数据模型,引入时间差分估计算法,设计基于最优性能指标的轨迹跟踪无模型自适应迭代学习控...     

基于不确定T-S模型的模糊滑模自适应控制

提出一种不确定T-S模型的模糊滑模自适应控制方法。通过变换将该模型转换成3个组成部分：线性标称系统,已知非线性部分和未知不确定部分。针对它们设计3个控制器,其作用分别为：强迫系统沿着滑模面运动,消除已知扰动对线性标称系统的影响,克服不确定扰动(采用模糊滑模自适应控制,无需知道不确定的界限)。该方法无需求正定矩阵就能保证系统全局稳定。     

一种自适应CMAC在交流励磁水轮发电系统中仿真研究

在分析常规CMAC结构的基础上,针对一类非线性、参数时变和不确定的控制系统,提出了一种自适应CMAC神经网络的控制器．该控制器以系统动态误差和给定信号量作为CMAC的激励信号,并与自适应线性神经元网络相结合构成系统的复合控制．为了验证其有效性,将其应用到交流励磁水轮发电机系统的多变量非线性控制中,并与常规的PID控制效果进行了比较．仿真结果表明,该控制器具有较强鲁棒性和自适应能力,控制品质优良。     

小型无人直升机反步自适应控制

针对具有强非线性、高度耦合以及参数不确定性特点的小型无人直升机系统,提出一种基于小脑模型关节控制器（Cerebellar Model Articulation Control,CMAC）神经网络的自适应反步控制方法,该方法采用小脑模型关节控制器神经网络在线学习系统不确定性以及反步控制中各阶虚拟控制量的导数信息,设计鲁棒控制项克服CMAC神经网络在线学习系统不确定性的误差,控制律由反步法回归递推得到。仿真结果表明,在模型参数不确定和存在较大误差的情况下,所设计的控制律具有理想的姿态跟踪性能以及良好的鲁棒性。     

航天器位姿运动一体化直接自适应容错控制研究

针对航天器近距离操作过程中追踪航天器位姿控制系统执行器故障问题,提出了一种直接自适应容错控制方法,保证了追踪航天器在发生执行器故障下的自身稳定性和对目标航天器位姿状态的渐近跟踪性能.基于对偶四元数的航天器位姿一体化控制系统模型,首先,假设故障已知,设计标称控制信号;然后,设计自适应更新律对标称控制信号中的未知参数进行估计,构成自适应控制信号;最后,利用多Lyapunov函数对多故障模式下的系统性能进行分析.仿真结果表明了所提方法的有效性.     

Adaptive CMAC-based supervisory control for uncertain nonlinear systems.

An adaptive cerebellar-model-articulation-controller (CMAC)-based supervisory control system is developed for uncertain nonlinear systems. This adaptive CMAC-based supervisory control system consists of an adaptive CMAC and a supervisory controller. In the adaptive CMAC, a CMAC is used to mimic an ideal control law and a compensated controller is designed to recover the residual of the approximation error. The supervisory controller is appended to the adaptive CMAC to force the system states within a predefined constraint set. In this design, if the adaptive CMAC can maintain the system states within the constraint set, the supervisory controller will be idle. Otherwise, the supervisory controller starts working to pull the states back to the constraint set. In addition, the adaptive laws of the control system are derived in the sense of Lyapunov function, so that the stability of the system can be guaranteed. Furthermore, to relax the requirement of approximation error bound, an estimation law is derived to estimate the error bound. Finally, the proposed control system is applied to control a robotic manipulator, a chaotic circuit and a linear piezoelectric ceramic motor (LPCM). Simulation and experimental results demonstrate the effectiveness of the proposed control scheme for uncertain nonlinear systems.     

Self-Organizing CMAC Control for a Class of MIMO Uncertain Nonlinear Systems

This paper presents a self-organizing control system based on cerebellar model articulation controller (CMAC) for a class of multiple-input-multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding-mode control (SMC), so the input space dimension of CMAC can be simplified. The structure of CMAC will be self-organized; that is, the layers of CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The control system consists of a self-organizing CMAC (SOCM) and a robust controller. SOCM containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient-descent method is used to online tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. A simulation study of inverted double pendulums system and an experimental result of linear ultrasonic motor motion control show that favorable tracking performance can be achieved by using the proposed control system.     

Adaptive supervisory WCMAC neural network controller (SWC) for nonlinear systems

This paper proposes a wavelet-based cerebellar model arithmetic controller neural network (called WCMAC) and develops an adaptive supervisory WCMAC control (SWC) scheme for nonlinear uncertain systems. The WCMAC is modified from the traditional CMAC for obtaining high approximation accuracy and convergent rate using the advantages of wavelet functions and fuzzy TSK-model. For nonlinear uncertain systems, a PD-type WCMAC controller with filter is constructed to approximate an ideal control signal. The corresponding adaptive supervisory controller is used to recover the residual of approximation error. Finally, the adaptive SWC scheme is applied to chaotic system identification and control including Mackey–Glass time-series prediction, control of inverted pendulum system, and control of Chua circuit system. These demonstrate the effectiveness of our adaptive SWC approach for nonlinear uncertain systems.     

确定学习与基于数据的建模及控制

确定学习运用自适应控制和动力学系统的概念与方法, 研究未知动态环境下的知识获取、表达、存储和利用等问题. 针对产生周期或回归轨迹的连续 非线性动态系统, 确定学习可以对其未知系统动态进行局部准确建模, 其基本要 素包括: 1)使用径向基函数(Radial basis function, RBF)神经网络; 2)对于周期(或回归)状态轨迹 满足部分持续激励条件; 3)在周期(或回归)轨迹的邻域内实现对非线性系统动态的局部准确神经网络逼近(局部准确建模); 4)所学的知识以时不变且空间分布的方式表达、以常值神经网络权值的方式存储, 并可在动态环境下用于动态模式的快速识别或者闭环神经网络控制. 本文针对离散动态系统, 扩展了确定学习理论, 提出一个根据时态数据序列对离散动态系统进行建模与控制的框架. 首先, 运用确定学习原理和离散系统的自适应辨识方法, 实现对产生时态数据的离散非线性系统的未知动态进行局部准确的神经网络建模, 并利用此建模结果对时态数据序列进行时不变表达. 其次, 提出时态数据序列的基于动力学的相似性定义, 以及对离散动态系统产生的时态数据序列(亦可称为动态模式)进行快速识别方法. 最后, 针对离散非线性控制系统, 实现了基于时态数据序列对控制系统动态的闭环辨识(局部准确建模). 所学关于闭环动态的知识可用于基于模式的智能控制. 本文表明确定学习可以为时态数据挖掘的研究提供新的途径, 并为基于数据的建模与控制等问题提供新的研究思路.     

Comments on "Integral variable structure control of nonlinear system using a CMAC neural network learning Approach"

In the above paper, an adaptive integral variable structure control scheme using CMAC neural networks was proposed for a class of uncertain nonlinear systems. The author of the above paper claimed that the proposed controller ensures convergence of the tracking error to zero (see property 2 of Theorem 1). In this note, we point out that the proof of the property 2 is incorrect and therefore this property is untenable.     

Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm

The cerebellar model articulation controller (CMAC) has the advantages such as fast learning property, good generalization capability and information storing ability. Based on these advantages, this paper proposes an adaptive CMAC neural control (ACNC) system with a PI-type learning algorithm and applies it to control the chaotic systems. The ACNC system is composed of an adaptive CMAC and a compensation controller. Adaptive CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between adaptive CMAC and ideal controller. Based on the Lyapunov stability theorems, the designed ACNC feedback control system is guaranteed to be uniformly ultimately bounded. Finally, the ACNC system is applied to control two chaotic systems, a Genesio chaotic system and a Duffing–Holmes chaotic system. Simulation results verify that the proposed ACNC system with a PI-type learning algorithm can achieve better control performance than other control methods.     

Adaptive fault-tolerant control of non-linear systems: an improved CMAC-based fault learning approach

The conventional cerebellar model articulation controllers (CMAC) learning scheme equally distributes the correcting errors into all addressed hypercubes, regardless of the credibility of those hypercubes. This paper presents the adaptive fault-tolerant control scheme of non-linear systems using a fuzzy credit assignment CMAC neural network online fault learning approach. The credit assignment concept is introduced into fuzzy CMAC weight adjusting to use the learned times of addressed hypercubes as the credibility of CMAC. The correcting errors are proportional to the inversion of learned times of addressed hypercubes. With this fault learning model, the learning speed of fault can be improved. After the unknown fault is estimated, online, by using the fuzzy credit assignment CMAC, the effective control law reconfiguration strategy based on the sliding mode control technique is used to compensate for the effect of the fault. The proposed fault-tolerant controller adjusts its control signal by adding a corrective sliding mode control signal to confine the system performance within a boundary layer. The numerical simulations demonstrate the effectiveness of the proposed CMAC algorithm and fault-tolerant controller.     

Application of CMAC neural network to the control of induction motor drives

In this paper, a cerebellar-model-articulation-controller (CMAC) neural network (NN) based control system is developed for a speed-sensorless induction motor that is driven by a space-vector pulse-width modulation (SVPWM) inverter. By analyzing the CMAC NN structure and motor model in the stationary reference frame, the motor speed can be estimated through CMAC NN. The gradient-type learning algorithm is used to train the CMAC NN online in order to provide a real-time adaptive identification of the motor speed. The CMAC NN can be viewed as a speed estimator that produces the estimated speed to the speed control loop to accomplish the speed-sensorless vector control drive. The effectiveness of the proposed CMAC speed estimator is verified by experimental results in various conditions, and the performance of the proposed control system is compared with a new neural algorithm. Accurate tracking response and superior dynamic performance can be obtained due to the powerful online learning capability of the CMAC NN.