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discrete time optimal control
相关语句
  离散时间最优控制
     A NOVEL METHOD BASED ON NEURAL NETWORKS FOR DISCRETE TIME OPTIMAL CONTROL PROBLEMS
     离散时间最优控制问题的一种神经网络新解法
短句来源
  “discrete time optimal control”译为未确定词的双语例句
     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.
     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
短句来源
     This paper studies the discrete time optimal control of a flexible cantilever beam with time delay in control.
     对考虑时滞影响的柔性悬臂梁的离散最优控制进行研究。
短句来源
  相似匹配句对
     Robustness of Discrete-Time Optimal Control Systems
     离散最优控制系统的鲁棒性
短句来源
     Optimal Control for Discrete Time Singular System
     离散广义线性系统的最优控制
短句来源
     The Time-optimal Control of Servomechanisms
     随动系统的最速控制
短句来源
     Time-optimal Control by Microcomputer
     应用微型机实现时间最优控制的方法
短句来源
     The Discrete-time Model and Optimal Control in Canal Operation
     渠道运行离散模型及最优控制
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  discrete time optimal control
This work presents a multi-objective differential dynamic programming approach to constrained discrete-time optimal control.
      
This cubic iteration time can be guaranteed using a Riccati-based block factorization technique, which is standard in discrete-time optimal control.
      
A multi-objective dynamic programming approach to constrained discrete-time optimal control Driessen, Brian J.
      


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,...

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...

?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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