一类非线性系统LMF优化迭代神经网络控制器

提出一类非线性系统ＬＭＦ优化迭代神经网络控制器的设计方法。该方法在正向神经网络辨识模型的基础上，应用ＬＭＦ优化迭代方法进行控制器设计，理论证明，只要神经网络辨识模型的精度足够高，就会获得很好的控制精度。为补偿辨识和迭代学习误差，给出了通过引入反馈补偿控制器提高控制精度的方法，仿真结果证明了该方法的有效性。     

一类未知非线性系统的智能迭代学习控制

从自适应的角度设计迭代学习控制,将神经网络引入迭代学习控制中。学习控制与自适应控制相结合,使得对网络权值的学习和跟踪控制同时进行,克服 了经典迭代学习控制的一些缺陷。基于Lyapunov直接方法,证明了整个控制系统的稳定并实现了任意精度的跟踪。实例仿真结果说明了算法 的有效性及其所具有的优点。     

一类单输入单输出的典型非线性系统神经网络滑模控制

针对系统部分非线性未知的非线性函数输出跟踪问题，提出了一种基于滑模技术的神经网络滑模控制策略。在此采用已有的输入输出线性化方法消除非线性，且将神经网络辨识结果加入到控制器中。采用李亚普诺夫函数证明了跟踪误差渐进收敛至零，仿真结果也证明了这一点。     

基于神经网络的迭代优化预测控制

针对时变的非线性系统,提出一种基于神经网络的迭代优化预测控制。它将传统的预测控制策略与神经网络逼近任息非线性函数的能力结合,预测系统未来输出,然后用迭代学习方法优化预测控制器,即通过一阶泰勒展开的方法,把非线性优化问题转化为线性优化问题。不仅简化计算,同时避免用神经网络优化控制器时,由于调节参数过多、涮前速度慢而导致系统闭环稳定性和鲁棒性差的问题。仿真结果表明,该控制方案具有良好的控制品质,并适应对象参数的变化,具有较强的鲁棒性和自适应性。     

基于神经网络非线性系统辨识和控制的研究

本文提出了由静态的前馈网络和稳定的滤波器构成的非线性系统的辨识模型,在神经网络固有的逼近误差存在的情况下,从理论上讨论了神经网络应用于辨识控制过程中系统的稳定性问题,最后研究了在非线性系统的轨迹跟踪过程中增加滑动控制来偿神经网络的逼近误差,从而提高系统跟踪性能。     

基于模型辨识的非线性连续系统的迭代学习控制

基于非线性连续动态的模型辨识算法, 给出了非线性连续系统的一种非常有效的迭代学习控制方案. 该控制方案不要求非线性连续系统中具体的非线性关系, 并且容许系统初始误差的存在.     

非线性系统的神经网络辨识和控制方法

1引言 在Wiener开创控制论的伊始,就将控制、信息和神经科学作为一个共同的课题。后,控制学科、计算科学和神经生理学趋于分开发展。自从80年代初期以来,神经网络有了长的进步,在人工智能和     

基于模糊辨识的非线性连续系统的迭代学习控制

基于非线性连续动态的模型辨识算法，给出了非线性连续系统的一种非常有效的迭代学习控制方案。该控制方案不要求非线性连续系统中具体的非线性关系，并且容许系统初始误差的存在。     

基于神经网络的一类非线性系统的自适应控制

基于递归神经网络给出了仅含一个非线性环节的一类非线性系统的自适应控制方案。该方案采用递归神经网络辨识非线性系统中的未知非线性环节。沿用广义最小方差自校正控制方法,可以解决非线性环节未知和工作点变化时传统方法无法控制的自适应控制问题。理论分析和仿真结果表明,该方法具有很好的控制效果。     

时变参数化非线性系统自适应迭代学习控制器设计

针对一类时变参数化非线性系统的控制问题进行深入研究,提出一种新的迭代神经网络估计器,并证明了其逼近引理,实现了对时变不确定性的逼近.在用迭代神经网络对时变不确定性进行估计的同时,以Lyapunov稳定性理论为基础,综合运用Backstepping和自适应控制技术,设计了自适应迭代学习控制器,并进行了稳定性分析,得到了稳定性定理,解决了这类时变非线性系统的控制问题.最后的仿真实验验证了所提出设计方法的正确性.     

Modelling and multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks

In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.     

控制方向未知的二阶时变非线性系统自适应迭代学习控制

对一类二阶严格反馈时变非线性系统的自适应迭代学习控制问题进行了研究.系统中含有非周期时变参数化不确定性且控制方向未知.首先,提出了一种神经网络估计器,实现了对未知非周期时变非线性函数的逼近.随后,用Nussbaum函数对未知控制方向进行了自适应估计,并综合应用baCkstcpping技术和自适应迭代学习控制技术设计了控制器.所设计的控制器能保证系统所有状态量在Lpe-范数意义下有界,且系统的输出量在LT2-范数意义下收敛到期望轨迹.最后的仿真研究证明了控制器设计方法的有效性.     

基于迭代学习和神经网络的船用起重机控制

作为一种重要的海上作业装备,船用起重机被广泛应用于海洋工程的各类场景中.然而,船用起重机是一类复杂的非线性欠驱动系统,存在摩擦、未建模动态等干扰,为控制器设计带来了巨大挑战.更糟糕的是,船用起重机还面临海浪、大风等未知干扰的影响,使得实际控制更加困难.如何稳定高效地控制该类系统,目前仍处于初步探索阶段.为了解决上述问题,本文提出了一种基于迭代学习和神经网络的控制方法.具体来说,首先将未知干扰分为周期与非周期两部分.对于周期干扰,利用周期估计器解决了对未知周期的估计问题,在此基础上通过迭代学习对干扰进行补偿;对于非周期干扰,使用双层神经网络进行逼近和补偿,并设计了权重的更新律;在补偿未知干扰后,基于反馈线性化设计了控制输入.通过Lyapunov分析方法,可以证明期望平衡点是全局有界的.最后,在所搭建的船吊实验平台上进行了大量实验,充分验证了所设计控制方法的有效性与鲁棒性.     

Batch Process Modelling and Optimal Control Based on Neural Network Model

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.     

Batch Process Modelling and Optimal Control Based on Neural Network Models

This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.     

Respecifying the weighting matrix of a quadratic objective function

A method based on a rank-one updating formula well known in function optimisation is described for respecifying the weighting matrix of the quadratic criterion function. The criterion function is thus altered systematically until an optimal solution acceptable to the policy-maker is generated. An iterative procedure is developed for this purpose.     

Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN₁) is synthesised offline. However, another linear-in-weight neural network (called as NN₂) is trained online and augmented to NN₁ in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN₃) online. Training of NN₃ is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN₂ successfully to capture the required optimal relationship. The overall architecture is named as ‘Dynamically Re-optimised single network adaptive critic (DR-SNAC)’. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.     

基于遗传算法学习的复合神经网络自适应温度控制系统

针对一类温度控制系统中存在的非线性和参数不确定等问题,提出一种复合神经网络自适应控制结构.在控制系统中构造了神经网络正模型来再现被控对象的动态特性,用神经网络控制器实现优化控制律的非线性映射.文中选用了被控对象80组历史数据作为样本集,并利用遗传算法的全局搜索能力及高效率来训练多层前向神经网络的权系数.最后用升降温工艺曲线作为输入对温度控制系统进行仿真.仿真结果表明,应用遗传算法能够提高神经网络的学习效率.保证神经网络全局快速收敛,从而克服了传统的误差反传学习算法的一些缺点.证明了采用这种神经网络自适应控制结构.使神经网络控制器的输出可以适应对象参数和环境的变化.使温度控制系统具有很好的学习和自适应控制能力,取得了良好的控制效果.