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discrete-time optimal
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  discrete-time optimal
The analogous theory for the discrete-time optimal control leads to the equation and the rate of stability of solutions of this equation is also studied.
      
Control functions in discrete-time optimal control systems
      
Infinite-Horizon Discrete-Time Optimal Control Problems
      
Dynamic Programming Method for Constrained Discrete-Time Optimal Control
      
Efficient Sequential Quadratic Programming Implementations for Equality-Constrained Discrete-Time Optimal Control
      
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In this paper, a design methed on discrete-time Optimal control system with prescribed degree of stability is proposed first. and then the robustness problem of the optimal closed-loop system is studied, the stable bounds are derived for several closed- loop systems

本文首先提出具有指定稳定度的离散系统最优控制设计的一种方法,然后讨论了最优闭环系统的鲁棒性问题,得到了几个闭环系统鲁棒稳定的界。

A novel method for nonlinear discrete time optimal control problems is proposed based upon the neural network theory. The nonlinear dynamic system is approximated by a linear network model which is rapidly updated by using the system inputs and outputs during each sampling interval. The Linear Quadratic Regulator(LQR) theory is directly used to deduce the optimal control. Because no off line training is required, this new method...

A novel method for nonlinear discrete time optimal control problems is proposed based upon the neural network theory. The nonlinear dynamic system is approximated by a linear network model which is rapidly updated by using the system inputs and outputs during each sampling interval. The Linear Quadratic Regulator(LQR) theory is directly used to deduce the optimal control. Because no off line training is required, this new method can be applied to real time control situations. An illustrative numerical example shows that it is rather feasible and effective. This control scheme is a little more intelligent than any previous ones.

根据人工神经网络理论提出了一种处理最优控制问题的新思想,即由每一采样时刻系统的输入和响应,快速训练产生一模型网络,并由LQR理论直接构造最优反馈策略.和以往研究不同,这里不需要任何离线学习,因而避免了收集离线学习所需数据的困难,可以实现实时控制,充分体现了智能控制的特点

?In this paper, a novel recurrent neural network method of solving for discrete - time optimal control are presented. The novel modified neural network described here is composed of two sub - nets: Linear neural network (LNN) and diagaonal recurrent neural network (DRNN). The correction LNN using output value of DRNN approximate system model, and solve the solution of optimal control using LQR theory. The network can be used for on - line learning of the plant 's dynamaic characteristics.

本文提出了一种处理离散时间最优控制问题的一种新型神经网络及其算法。这种神经网络由两个子神经网络综合构成:线性神经网络(LNN)和递归神经网络(DRNN)。用DRNN的输出修正LNN模型,逼近原非线性系统,并由LOR理论直接构造最优反馈策略。该网络能用于在线学习对象的动态特性,实现实时控制。

 
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