基于状态全反馈的动态迟滞非线性系统

该文针对不平滑、多映射动态迟滞非线性系统,提出了一种基于神经网络自适应控制方案.在该方案中,通过利用神经网络来逼近模型误差,避免了目前常用逆模型补偿方案中,需求取复杂逆模型的问题.应用Lyapnov稳定定理,证明了整个闭环系统的跟踪误差及神经网络权值将收敛到零点一个有界邻域内.仿真结果表明,所提出的控制方案能够有效补偿迟滞非线性对系统的影响.     

基于神经网络的迟滞非线性补偿控制

提出了一种基于神经网络的迟滞非线性的补偿方法.首先构造一个Duhem逆算子来描述迟滞逆状态.然后利用神经网络来逼近此状态和输出之间的关系来得到神经网络迟滞逆模型,神经网络权值采用反馈误差学习方法来进行在线调整.系统的前馈控制器和反馈控制器分别为逆模型和PID控制器.该方法不需要建立迟滞的正模型,能够在线构造逆模型来实现迟滞补偿.最后通过仿真验证了该方法的有效性.     

迟滞非线性动态系统神经网络自适应控制

为了减轻非线性动态系统中未知迟滞(Hysteresis)的不良影响，该文提出了一类Backlash型迟滞模型。将有限数量不同宽度的Backlash(Matlab／Simulink)算子进行叠加，来仿真执行器中的迟滞非线性动态。用此模型，提出了基于径向基函数神经网络的自适应控制方案，以控制伴有未知迟滞的非线性动态系统。该方案采用了动态逆的思想及伪控制的概念。利用Lyapunov稳定理论，设计了两个鲁棒控制项，保证动态系统的稳定性、系统中所有信号有界和误差收敛到起点的领域内。通过Matlab／Simulink仿真实验，证明了所提出方案的有效性。     

压电微定位平台神经网络与专家模糊复合控制方法

为了消除压电微定位平台的迟滞非线性特性,实现高精度定位控制,采用具有两个隐含层的BP神经网络建立压电微定位平台的迟滞模型,以精确描述驱动电压与输出位移的迟滞关系;设计一种基于BP神经网络迟滞逆模型的前馈控制器,对迟滞非线性进行补偿,将迟滞非线性近似线性化.为进一步提高定位系统的精度,提出基于迟滞逆模型前馈补偿和专家模糊控制的复合控制方法.仿真结果表明,该复合控制方法可以将压电微定位平台的定位误差控制在0.091μm以内,从而有效地消除迟滞非线性对压电微定位平台定位精度的影响.     

基于ANN逆模型补偿的复合控制系统仿真

为对复杂非线性系统进行辨识建模和实施有效控制,分析了基于神经网络的非线性系统逆模型的辨识和控制原理,研究了基于神经网络的非线性系统逆模型补偿的复合控制方法。基于复合控制思想,时常规PID控制器＋前馈神经网络逆模型补偿的复合控制结构方案进行了仿真。仿真结果表明,基于神经网络的非线性系统逆模型补偿的复合控制结构方案是有效的、相对简单的网络结构,可提高逆模型的泛化能力和非线性系统的控制精度。     

基于迟滞算子的非平滑三明治系统自适应控制

针对一类具有非平滑的迟滞三明治系统, 提出一种基于神经网络的自适应控制方法. 首先利用神经网络做出了前端动态模块的逆系统实现前端动态模块的近似补偿, 这样将迟滞三明治系统转化成一般的迟滞非线性系统. 然后提出一个迟滞算子将迟滞的多映射转化成一一映射, 基于这个迟滞算子设计了神经网络自适应控制器, 通过Lyapunov方法证明了系统的稳定性并推导出神经网络的权值自适应调整律和控制律. 最后通过仿真验证了该方案的有效性.     

非线性系统的神经网络自适应逆控制

提出了非线性系统的神经网络自适应逆控制方法。设计中使用了2个神经网络，经离线训练的NN1实现非线性系统的逆，在线网络NN2用于补偿逆误差和系统的动态特性变化，对一非线性系统的仿真结果表明，神经网络自适应逆控制能够提高系统的动态性能，并且具有较好的鲁棒性。     

一种频率依赖性迟滞的神经网络内模控制

针对压电陶瓷等智能材料存在的依赖输入频率的迟滞非线性问题,采用BP神经网络对迟滞非线性进行辨识,并通过内模控制方案来对其进行控制.在迟滞的建模上,构建了一种静态迟滞非线性环节串联一个对输入频率敏感的线性动态环节组成的Hammerstein模型.在此基础上,得出Hammerstein模型的逆模型,并通过构造的正、逆模型进行内模控制.实验结果说明,提出的建模方法与内模控制方案是有效的.     

对迟滞三明治系统基于Duhem算子的自适应控制

对具有迟滞非线性的三明治系统,设计了基于Duhem算子的神经网络自适应控制器.首先对前端动态子系统进行近似补偿;然后用Duhem算子描述所提出的迟滞状态,用神经网络逼近迟滞状态与迟滞输出的关系,实现对迟滞非线性的建模.基于该迟滞模型并采用伪控制技术设计神经网络自适应控制器,通过Lyapunov方法证明了系统的稳定性,并推导出神经网络的权值自适应调整律和控制律.最后通过仿真验证了该方案的有效性.     

基于动态神经网络的非线性内模控制

针对一类不确定仿射非线性系统，提出一种基于动态神经网络的非线性内模控制方法。利用该网络模型存在相对阶时可以解析求得逆模型的特点，避免了普通神经网络内模控制方案中求逆的困难。并在有建模误差的情况下，通过将非线性对象输入输出线性化，分析了闭环系统的鲁棒稳定性和稳态性能。仿真试验表明该方法是可行和有效的。     

An efficient CMAC neural network for stock index forecasting

Stock index forecasting is one of the major activities of financial firms and private investors in making investment decisions. Although many techniques have been developed for predicting stock index, building an efficient stock index forecasting model is still an attractive issue since even the smallest improvement in prediction accuracy can have a positive impact on investments. In this paper, an efficient cerebellar model articulation controller neural network (CAMC NN) is proposed for stock index forecasting. The traditional CAMC NN scheme has been successfully used in robot control due to its advantages of fast learning, reasonable generalization capability and robust noise resistance. But, few studies have been reported in using a CMAC NN scheme for forecasting problems. To improve the forecasting performance, this paper presents an efficient CMAC NN scheme. The proposed CMAC NN scheme employs a high quantization resolution and a large generalization size to reduce generalization error, and uses an efficient and fast hash coding to accelerate many-to-few mappings. The forecasting results and robustness evaluation of the proposed CMAC NN scheme were compared with those of a support vector regression (SVR) and a back-propagation neural network (BPNN). Experimental results from Nikkei 225 and Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) closing indexes show that the performance of the proposed CMAC NN scheme was superior to the SVR and BPNN models.     

Multiple Lyapunov functions‐based adaptive neural network tracking control of uncertain switched nonlinear systems

In this paper, the problem of adaptive neural network (NN) tracking control of a class of switched strict‐feedback uncertain nonlinear systems is investigated by state‐feedback, in which the solvability of the problem of adaptive NN tracking control for individual subsystems is unnecessary. A multiple Lyapunov functions (MLFs)–based adaptive NN tracking control scheme is established by exploiting backstepping and the generalized MLFs approach. Moreover, based on the proposed scheme, adaptive NN controllers of all subsystems and a state‐dependent switching law simultaneously are constructed, which guarantee that all signals of the resulting closed‐loop system are semiglobally uniformly ultimately bounded, and the tracking error converges to a small neighborhood of the origin. The scheme provided permits removal of a technical condition in which the adaptive NN tracking control problem for individual subsystems is solvable. Finally, the effectiveness of the design scheme proposed is shown by using two examples.     

An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach

In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations.     

Robust Model Reference Adaptive Intelligent Control

In this paper a Neural Network based Model Reference Adaptive Control scheme (NNMRAC) is proposed. In this scheme, the controller is designed by using parallel combination of the conventional Model Reference Adaptive Control (MRAC) scheme and Neural Network (NN) controller. In the conventional MRAC scheme, the controller is designed to realize plant output converging to reference model output based on the plant which is linear. This scheme is used to control linear plant effectively with unknown parameters. However, it is difficult for a nonlinear system to control the plant output in real time applications. In order to overcome the above limitations, the NN-MRAC scheme is proposed to improve the system performances. The control input of the plant is given by the sum of the MRAC output and NN controller output. The NN controller is used to compensate the nonlinearities and disturbances of the plant that are not taken into consideration in the conventional MRAC. The simulation results clearly show that the proposed NN-MRAC scheme have better steady state and transient performances than those of the current adaptive control schemes. Thus, the proposed NN-MRAC scheme named as Robust Model Reference Adaptive Intelligent Control (RMRAIC) is found to be extremely effective, efficient and useful in the field of control system.     

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

电驱动刚性机器人的鲁棒神经网络复合控制

采用逐步逆向的设计思想，提出一种新的电驱动刚性机器人轨迹跟踪的鲁棒神经网络复合控制策略，该策略不仅能有效地消除模型不确定性的影响，而且可避免复杂的求导运算以及对关节角加速度可测的要求。给出了控制器的具体组成和神经网络连接权的在线学习算法，理论表明该算法能保证跟踪误差及神经网络连接权估计最终一致有界，仿真结果也验证了算法的有效性。     

Comments on "Adaptive control and identification using one neural network for a class of plants with uncertainties"

In the above paper, Tsuji et al. (see ibid. vol.28 (1998)) proposed an interesting scheme for adaptive control that uses only one neural network (NN). Moreover, the stability of the parametric and identification errors was analyzed and a sufficient condition for it was presented. However, since the selected signals for the NN input were not the natural choice, their scheme presents some problems. This study intends to circumvent these problems by modifying the scheme proposed in the work of Tsuji et al., and to highlight some advantages of the modified scheme.     

Output Feedback Control of a Quadrotor UAV Using Neural Networks

In this paper, a new nonlinear controller for a quadrotor unmanned aerial vehicle (UAV) is proposed using neural networks (NNs) and output feedback. The assumption on the availability of UAV dynamics is not always practical, especially in an outdoor environment. Therefore, in this work, an NN is introduced to learn the complete dynamics of the UAV online, including uncertain nonlinear terms like aerodynamic friction and blade flapping. Although a quadrotor UAV is underactuated, a novel NN virtual control input scheme is proposed which allows all six degrees of freedom (DOF) of the UAV to be controlled using only four control inputs. Furthermore, an NN observer is introduced to estimate the translational and angular velocities of the UAV, and an output feedback control law is developed in which only the position and the attitude of the UAV are considered measurable. It is shown using Lyapunov theory that the position, orientation, and velocity tracking errors, the virtual control and observer estimation errors, and the NN weight estimation errors for each NN are all semiglobally uniformly ultimately bounded (SGUUB) in the presence of bounded disturbances and NN functional reconstruction errors while simultaneously relaxing the separation principle. The effectiveness of proposed output feedback control scheme is then demonstrated in the presence of unknown nonlinear dynamics and disturbances, and simulation results are included to demonstrate the theoretical conjecture.      

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

Trajectory Tracking Control of Omnidirectional Wheeled Mobile Manipulators: Robust Neural Network-Based Sliding Mode Approach

This paper addresses the robust trajectory tracking problem for a redundantly actuated omnidirectional mobile manipulator in the presence of uncertainties and disturbances. The development of control algorithms is based on sliding mode control (SMC) technique. First, a dynamic model is derived based on the practical omnidirectional mobile manipulator system. Then, a SMC scheme, based on the fixed large upper boundedness of the system dynamics (FLUBSMC), is designed to ensure trajectory tracking of the closed-loop system. However, the FLUBSMC scheme has inherent deficiency, which needs computing the upper boundedness of the system dynamics, and may cause high noise amplification and high control cost, particularly for the complex dynamics of the omnidirectional mobile manipulator system. Therefore, a robust neural network (NN)-based sliding mode controller (NNSMC), which uses an NN to identify the unstructured system dynamics directly, is further proposed to overcome the disadvantages of FLUBSMC and reduce the online computing burden of conventional NN adaptive controllers. Using learning ability of NN, NNSMC can coordinately control the omnidirectional mobile platform and the mounted manipulator with different dynamics effectively. The stability of the closed-loop system, the convergence of the NN weight-updating process, and the boundedness of the NN weight estimation errors are all strictly guaranteed. Then, in order to accelerate the NN learning efficiency, a partitioned NN structure is applied. Finally, simulation examples are given to demonstrate the proposed NNSMC approach can guarantee the whole system's convergence to the desired manifold with prescribed performance.