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。     

采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态 神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系 统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项 和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神 经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经 网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法 较基于静态神经网络的自适应方法具有更好的性能.     

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。     

基于神经网络的鲁棒自适应逆飞行控制

提出基于在线神经网络的超机动飞行自适应动态逆鲁棒控制方法.超机动飞行的基本控制律采用非线性动态逆方法设计,对于建模误差或者控制面损伤等因素导致的不确定性逆误差采用神经网络进行自适应补偿.通过动态逆控制律简化计算和飞机控制面故障自适应修复的仿真表明,神经网络通过在线补偿逆误差,能够有效降低非线性动态逆对模型准确性的要求,增强控制系统的鲁棒性.     

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。     

一种基于RBF 网络的非线性自适应逆控制系统

改进了原有的ε-滤波自适应逆控制系统，引入自适应扰动消除器和反馈补偿，构成一种新的自适应逆控制系统．反馈补偿能消除自适应逆控制系统中的直流零频漂移，自适应扰动消除器能最大限度地消除扰动．将神经网络引入自适应逆控制系统，采用基于径向基函数网络的非线性滤波器，对非线性系统进行建模、逆建模、控制器及自适应扰动消除器的设计．仿真结果验证了该方法的有效性．     

基于逆系统的赖氨酸发酵多变量解耦内模控制

针对赖氨酸发酵过程的时变、非线性和高耦合性,提出基于逆系统的赖氨酸发酵多变量解耦内模控制方法。根据动态递归模糊神经网络(DRFNN)的非线性辨识原理离线建立发酵过程的逆模型,将得到的逆模型串联在发酵系统之前,实现了发酵过程输入输出解耦线性化,从而得到伪线性系统;对复合后的伪线性系统采用内模控制。仿真结果表明,该方法能够适应赖氨酸发酵过程模型的不确定性和参数的时变性,具有较强的鲁棒性,且结构简单,易于实现。     

基于状态全反馈的动态迟滞非线性系统自适应控制

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。     

神经网络非线性多步预测逆控制方法研究*

提出了基于多步预测控制方法的多变量非线性神经网络逆控制方案。利用预测模型对系统动态特性进行预测,使用一个带有时延因子的前馈神经网络作为控制器,利用多步预测性能指标对其在线训练,实现神经网络逆系统;在多步预测过程中还对每一步的预测误差进行预测,以实现预测误差补偿。将所提出的控制算法用于锅炉这种大滞后非线性对象的控制,仿真实验证明,该控制策略具有良好的解耦和动态跟踪性能。     

高超声速飞行器的神经网络动态逆控制研究

针对通用的高超声速飞行器的纵向动力学设计一个神经网络动态逆补偿控制方法,并对其进行了分析;这种飞行器模型具有高度非线性、多变量、不稳定的特性,包括6个不确定参数;在4.5903km高度和15马赫的平衡巡航条件下的仿真研究,评价了飞行器对高度和空速的阶跃变化的响应;阶跃变化为速度30 m/s,高度40 m;通过仿真结果表明,采用神经网络补偿逆误差,弥补了非线性动态逆要求精确数学模型的缺点,而且可以简化动态逆控制律的设计,改善整个控制系统的性能。     

考虑量化输入和输出约束的互联系统自适应分散跟踪控制

本文考虑具有量化输入和输出约束的一类非线性互联系统的自适应分散跟踪控制设计.分别针对量化参数已知和未知两种情况,基于反推(Backstepping)设计法,利用神经网络逼近特性,设计自适应分散跟踪控制策略.通过定义新的未知常量和非线性光滑函数,设计自适应参数估计项来消除未知互联项对系统的影响.进一步考虑量化参数未知的情...     

基于神经网络与多模型的非线性自适应广义预测控制

针对一类不确定非线性离散时间动态系统, 提出了基于神经网络与多模型的非线性广义预测自适应控制方法. 该自适应控制方法由线性鲁棒广义预测自适应控制器, 神经网络非线性广义预测自适应控制器和切换机制三部分构成. 线性鲁棒广义预测自适应控制器保证闭环系统的输入输出信号有界, 神经网络非线性广义预测自适应控制器能够改善系统的性能. 切换策略通过对上述两种控制器的切换, 保证系统稳定的同时, 改善系统性能. 给出了所提自适应方法的稳定性和收敛性分析. 最后通过仿真实例验证了所提方法的有效性.     

RBFN-based decentralized adaptive control of a class of large-scale non-affine nonlinear systems

For a class of large-scale decentralized nonlinear systems with strong interconnections, a radial basis function neural network (RBFN) adaptive control scheme is proposed. The system is composed of a class of non-affine nonlinear subsystems, which are implicit function and smooth with respect to control input. Based on implicit function theorem, inverse function theorem and the design idea of pseudo-control, a novel control algorithm is proposed. Two neural networks are used to approximate unknown nonlinearities in the subsystem and unknown interconnection function, respectively. The stability is proved rigidly. The result of simulation validates the effectiveness of the proposed scheme.     

基于神经网络与多模型的非线性自适应广义预测解耦控制

针对一类非线性多变量离散时间动态系统,提出了基于神经网络与多模型的非线性自适应广义预测解耦控制方法.该控制方法由线性鲁棒广义预测解耦控制器和神经网络非线性广义预测解耦控制器以及切换机构组成.线性鲁棒广义预测解耦控制器用于保证闭环系统输入输出信号有界,神经网络非线性广义预测解耦控制器能够改善系统性能.切换策略通过对上述两种控制器的切换,保证系统稳定的同时,改善系统性能.同时本文给出了所提自适应解耦控制方法的稳定性和收敛性分析.最后,通过仿真实例验证了该方法的有效性.     

Non-affine nonlinear adaptive control of decentralized large-scale systems using neural networks

This paper introduces a new decentralized adaptive neural network controller for a class of large-scale nonlinear systems with unknown non-affine subsystems and unknown interconnections represented by nonlinear functions. A radial basis function neural network is used to represent the controller’s structure. The stability of the closed loop system is guaranteed through Lyapunov stability analysis. The effectiveness of the proposed decentralized adaptive controller is illustrated by considering two nonlinear systems: a two-inverted pendulum and a turbo generator. The simulation results verify the merits of the proposed controller.     

Adaptive neural network asymptotical tracking control for an uncertain nonlinear system with input quantisation

In this paper, the problem of adaptive neural network asymptotical tracking is investigated for a class of nonlinear system with unknown function, external disturbances and input quantisation. Based on neural network technique, an adaptive asymptotical tracking controller is provided for an uncertain nonlinear system via backstepping method. In order to reduce complexity of the control algorithm in the backstepping design process, a sliding mode differentiator is employed to estimate the virtual control law and only two parameters need to be estimated via adaptive control technique. The stability of the closed-loop system is analysed by using Lyapunov function method and zero-tracking error performance is obtained in the presence of unknown nonlinear function, external disturbances and input quantisation. Finally, an application example is employed to demonstrate the effectiveness of the proposed scheme.     

Real‐Time Results for High Order Neural Identification and Block Control Transformation Form Using High Order Sliding Modes

In this paper, real‐time results for a novel continuous‐time adaptive tracking controller algorithm for nonlinear multiple input multiple output systems are presented. The control algorithm includes the combination of a recurrent high order neural network with block control transformation using a high order sliding modes technique as control law. A neural network is used to identify the dynamic plant behavior where a filtered error algorithm is used to train the neural identifier. A decentralized high order sliding mode, named the twisting algorithm, is used to design chattering‐reduced independent controllers to solve the trajectory tracking problem for a robot arm with three degrees of freedom. Stability analyses are given via a Lyapunov approach.     

Robust neural network tracking controller using simultaneous perturbation stochastic approximation

This paper considers the design of robust neural network tracking controllers for nonlinear systems. The neural network is used in the closed-loop system to estimate the nonlinear system function. We introduce the conic sector theory to establish a robust neural control system, with guaranteed boundedness for both the input/output (I/O) signals and the weights of the neural network. The neural network is trained by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard backpropagation (BP) algorithm. The proposed neural control system guarantees closed-loop stability of the estimation system, and a good tracking performance. The performance improvement of the proposed system over existing systems can be quantified in terms of preventing weight shifts, fast convergence, and robustness against system disturbance.     

Decentralized fault tolerant control of a class of nonlinear interconnected systems

In this paper, a novel decentralized fault tolerant controller (DFTC) is proposed for interconnected nonlinear continuous-time systems by using local subsystem state vector alone in contrast with traditional distributed fault tolerant controllers or fault accommodation schemes where the measured or the estimated state vector of the overall system is needed. The proposed decentralized controller uses local state and input vectors and minimizes the fault effects on all the subsystems. The DFTC in each subsystem includes a traditional controller term and a neural network based online approximator term which is used to deal with the unknown parts of the system dynamics, such as fault and interconnection terms. The stability of the overall system with the proposed DFTC is investigated by using Lyapunov approach and the boundedness of all signals is guaranteed in the presence of a fault. Therefore, the proposed controller enables the system to continue its normal operation after the occurrence of a fault, as long as it does not cause failure or break down of a component. Although the decentralized fault tolerant controller is designed mainly for large-scale systems where continuous transmissions between subsystems is not possible, it can also be applied to small-scale systems where sensor measurements are available for use in all subsystems. Finally the proposed methods are verified and compared in simulation environment.     

Decentralized adaptive controller design of large-scale uncertain robotic systems

In this paper, we develop a decentralized neural network control design for robotic systems. Using this design, it is not necessary to derive the robotic dynamical system (robotic model) for the control of each of the robotic components, as in traditional robot control. The advantage of the proposed neural network controller is that, under a mild assumption, unknown nonlinear dynamics such as inertia matrix and Coriolis/centripetal matrix and friction, as well as interconnections with arbitrary nonlinear bounds can be accommodated with on-line learning.