含未知信息的轮式移动机器人编队确定学习控制

本文研究含未知信息的轮式移动机器人(wheeled mobile robots,WMR)的编队控制问题.首先,基于领航–跟随法和虚拟结构法,将WMR编队控制问题转化为跟随机器人对参考虚拟机器人的跟踪控制问题.然后,利用径向基函数神经网络(radial basis function neural networks,RBF NN)对WMR的未知系统动态进行学习,以及根据李雅普诺夫稳定性理论设计了稳定的自适应RBF NN控制器和RBF NN权值估计的学习率.依据确定学习理论,闭环系统内部信号在对回归轨迹实现跟踪控制的过程中满足部分持续激励(persistent excitation,PE)条件.随着PE条件的满足,RBF NN权值估计收敛到其理想权值,实现了对未知闭环系统动态的准确学习.最后,利用学习结果设计了RBF NN学习控制器,保证了控制系统的稳定与收敛,实现了闭环稳定性和改进了控制性能,并通过仿真验证了所提控制方法的正确性和有效性.     

基于确定学习的机器人任务空间自适应神经网络控制

针对产生回归轨迹的连续非线性动态系统, 确定学习可实现未知闭环系统动态的局部准确逼近. 基于确定学习理论, 本文使用径向基函数(Radial basis function, RBF)神经网络为机器人任务空间跟踪控制设计了一种新的自适应神经网络控制算法, 不仅实现了闭环系统所有信号的最终一致有界, 而且在稳定的控制过程中, 沿着回归跟踪轨迹实现了部分神经网络权值收敛到最优值以及未知闭环系统动态的局部准确逼近. 学过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, 可以用来改进系统的控制性能, 也可以应用到后续相同或相似的控制任务中, 节约时间和能量. 最后, 用仿真说明了所设计控制算法的正确性和有效性.     

AUV深度的神经网络辨识和学习控制仿真研究

将自主水下航行器（AUV）的深度控制问题转换为对非线性严格反馈系统的分析,提出了一种结合反步法和确定学习理论的自适应学习控制方法。通过反步法设计了一种输入状态稳定（ISS）神经网络控制器,其中引入小增益定理,避免了控制器设计中存在的奇异值问题,并在满足持续激励（PE）条件下,利用神经网络辨识实现了对系统未知动态的局部准确逼近和部分神经网络权值的收敛,保证了闭环系统的稳定。将从动态模式中学到的知识静态保存,提取动态特征设计学习控制器,仿真结果表明,该控制器避免了执行同样任务时的重复训练,改善了系统控制性能,验证了所提控制方法的有效性。     

基于外环速度补偿的封闭机器人确定学习控制

针对未开放力矩控制接口的一类封闭机器人系统,提出一种基于外环速度补偿的确定学习控制方案.该控制方案考虑机器人受到未知动力学影响,且具有未知内环比例积分(Proportional-integral, PI)速度控制器.首先,利用宽度径向基函数(Radial basis function, RBF)神经网络对封闭机器人的内部未知动态进行逼近,设计外环自适应神经网络速度控制指令.在实现封闭机器人稳定控制的基础上,结合确定学习理论证明了宽度RBF神经网络的学习能力,提出基于确定学习的高精度速度控制指令.该控制方案能够保证被控封闭机器人系统的所有信号最终一致有界且跟踪误差收敛于零的小邻域内.在所提控制方案中,通过引入外环补偿控制思想和宽度神经网络动态增量节点方式,减小了设备计算负荷,提高了速度控制下机器人的运动性能,解决了市场上封闭机器人系统难以设计力矩控制的难题,实现了不同工作任务下的高精度控制.最后数值系统仿真结果和UR5机器人实验结果验证了该方案的有效性.     

欠驱动AUV模糊神经网络L2增益鲁棒跟踪控制

提出基于模糊神经网络欠驱动水下自主机器人(AUV)的L2增益鲁棒跟踪控制方法,该方法通过在线学习逼近动力学模型的不确定项.控制器克服了由于缺少横向推力对跟踪误差的影响,在考虑未知海流干扰情况下,实现了系统对模糊神经网络逼近误差的L2增益小于γ.利用Lyapunov稳定性理论证明了闭环控制系统误差信号一致最终有界.最后,通过精确模型参数和参数扰动仿真实验验证了该控制方法具有很好的跟踪效果和较强的鲁棒性.     

全局稳定的分散自适应神经网络反推跟踪控制

针对一类不确定大规模系统,研究其全局稳定的分散自适应神经网络反推跟踪控制问题.在假设不匹配的未知关联项满足部分已知的非线性Lipschitz条件下,采用神经网络作为前馈补偿器,逼近参考信号作为输入的未知关联函数;设计者可根据参考信号的界预先确定神经网络逼近域,同时保证了闭环系统的全局稳定性.仿真实例验证了控制算法的有效性.     

基于反步自适应神经网络的船舶航迹控制

针对欠驱动船舶在稳定航速条件下轨迹跟踪问题,提出了一种基于自适应神经网络与反步法相结合的控制算法.该算法将实际的欠驱动船舶视为模型完全未知的非线性系统,利用神经网络的函数逼近特性实现控制器中非线性部分的在线估计,采用同时调整输入层-隐层、隐层-输出层间的权值阵的方法进行神经网络权值调整.通过选取积分型Lyapunov函数证明了闭环系统的稳定性.仿真实验表明该控制策略具有良好的跟踪特性,可以实现对期望航迹的精确跟踪.     

基于RBF神经网络的一类不确定非线性系统自适应H∞控制

基于RBF神经网络提出了一种H∞自适应控制方法.控制器由等效控制器和H∞控制器两部分组成.用RBF神经网络逼近非线性函数,并把逼近误差引入到网络权值的自适应律中用以改善系统的动态性能.H∞控制器用于减弱外部干扰及神经网络的逼近误差对跟踪的影响.所设计的控制器不仅保证了闭环系统的稳定性,而且使外部干扰及神经网络的逼近误差对跟踪的影响减小到给定的性能指标.最后给出的算例验证了该方法的有效性.     

随机时滞系统的神经网络输出反馈动态面控制

针对一类具有未知控制方向的随机时滞系统设计自适应神经输出反馈控制器.首先,利用状态观测器估计不可测量的系统状态;其次,选择合适的Lyapunov-Krasovskii函数消除未知延迟项对系统的影响,利用Nussbaum-type函数处理系统的未知控制方向问题,通过神经网络逼近未知的非线性函数,以及用动态表面控制(DSC)解决控制器设计中出现的复杂性问题;最后,通过Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,可以保证闭环系统中所有信号在二阶或四阶矩意义下一致最终有界,跟踪误差能收敛到零值小的领域内.仿真实例验证了所提出方法的有效性.     

带有干扰观测器的线控转向系统复合自适应神经网络控制

考虑车辆线控转向(SbW)系统存在不确定动态特性以及外界干扰影响.本文提出一种带有干扰观测器的复合自适应神经网络实现SbW系统的精确建模与稳定控制.首先,利用神经网络在线逼近系统不确定动态,避免控制器设计中使用到系统模型的先验知识.然后,结合系统的跟踪误差与建模误差提出一种新的复合自适应学习率来更新神经网络的权值,从而加快跟踪误差的收敛速度.最后通过设计干扰观测器补偿系统受到摩擦力矩、回正力矩与神经网络逼近误差的影响,提高了系统的抗干扰能力.李雅普诺夫稳定性理论证明了闭环系统的跟踪误差信号一致最终有界.数值仿真与硬件在环实验结果验证了该控制方法的有效性和优越性.     

Adaptive neural control and learning of affine nonlinear systems

This paper presents deterministic learning from adaptive neural network control of affine nonlinear systems with completely unknown system dynamics. Thanks to the learning capability of radial basis function, neural network (NN), stable adaptive NN controller is designed for the unknown affine nonlinear systems. The designed adaptive NN controller is rigorously shown that learning of the unknown closed-loop system dynamics can be achieved during the stable control process because partial persistent excitation condition of some internal signals in the closed-loop system is satisfied. Subsequently, neural learning controller using the knowledge obtained from deterministic learning is constructed to achieve closed-loop stability and improve control performance. Numerical simulation is provided to show the effectiveness of the proposed control scheme.     

Learning from neural control

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.     

Adaptive neural controller for space robot system with an attitude controlled base

In this paper, an adaptive neural network-based controller is proposed for a space robot system with an attitude controlled base without joint acceleration measurements and in the presence of parametric uncertainties and external disturbances. Based on the dynamic model, a neural network-based controller is proposed that achieves the required tracking effectively. A feedforward neural network is employed to learn the existing unknown dynamics of robot system. The uniform ultimate boundedness of all signals in the closed-loop system is guaranteed by the Lyapunov approach. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary off learning. Finally, simulation study has been performed to evaluate the controller performance.     

Learning from neural control of nonlinear systems in normal form

A deterministic learning theory was recently proposed which states that an appropriately designed adaptive neural controller can learn the system internal dynamics while attempting to control a class of simple nonlinear systems. In this paper, we investigate deterministic learning from adaptive neural control (ANC) of a class of nonlinear systems in normal form with unknown affine terms. The existence of the unknown affine terms makes it difficult to achieve learning by using previous methods. To overcome the difficulties, firstly, an extension of a recent result is presented on stability analysis of linear time-varying (LTV) systems. Then, with a state transformation, the closed-loop control system is transformed into a LTV form for which exponential stability can be guaranteed when a partial persistent excitation (PE) condition is satisfied. Accurate approximation of the closed-loop control system dynamics is achieved in a local region along a recurrent orbit of closed-loop signals. Consequently, learning of control system dynamics (i.e. closed-loop identification) from adaptive neural control of nonlinear systems with unknown affine terms is implemented.     

一种稳定的机器人神经网络控制器研究

研究一种稳定的机器人神经网络（ＮＮ）控制器，提出了由神经网络控制器和监督控制器构成的控制方案，给出了控制器的设计方法及ＮＮ学习自适应律，并基于Ｌｙａｐｕｎｏｖ方法证明了控制系统的稳定性和ＮＮ参数收敛性，仿真结果表明该控制方案具有良好的鲁棒性和参数收敛性，从而证明控制器的有效性。     

Neural-network control of mobile manipulators

In this paper, a neural network (NN)-based methodology is developed for the motion control of mobile manipulators subject to kinematic constraints. The dynamics of the mobile manipulator is assumed to be completely unknown, and is identified online by the NN estimators. No preliminary learning stage of NN weights is required. The controller is capable of disturbance-rejection in the presence of unmodeled bounded disturbances. The tracking stability of the closed-loop system, the convergence of the NN weight-updating process and boundedness of NN weight estimation errors are all guaranteed. Experimental tests on a 4-DOF manipulator arm illustrate that the proposed controller significantly improves the performance in comparison with conventional robust control.     

Neural network-based adaptive controller design of roboticmanipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies