www.gusucode.com > nncontrol 工具箱 matlab 源码程序 > nncontrol/private/nnpredicthelp_main.m
% OVERVIEW % % The NN Predictive Control GUI is an interactive environment for % developing neural network predictive controllers. % % There are two steps in the controller design: % 1) Identification of a neural network plant model. % 2) Configuration of the controller parameters. % % Flip through the remaining Topics for a detailed description of how % to use these and other NN Predictive Control GUI features. % % MENUS % % The menus provide additional options for setting up and configuring % the controller. The menus available are as follows. % % 1) File: % a) Import Network: Import neural network plant weights % b) Export Network: Export plant weights % c) Save: Load all parameters into the Simulink controller block. % d) Save and Exit: Load all parameters into the Simulink controller block and close this menu. % e) Exit Without Saving: Close the NN Predictive Control GUI and all related windows. % % 2) Window: % Show and switch between all the open windows. % % 3) Help: % a) Main Help: Open the general NN Predictive Control GUI help text. % b) All other Help menus: Open tool specific help text. % % CONTROLLER STRUCTURE % % The neural network predictive controller is an optimization algorithm % that uses a neural network plant model to predict future plant behavior % over a specified time horizon. The optimization algorithm determines % the control signal that optimizes plant behavior over the time horizon. % % CONTROLLER PARAMETERS % % The controller has six parameters: % % 1)Cost horizon N2. The squared error between the plant output and the % reference signal is minimized over the specified cost horizon. % 2)Controller horizon. The squared controller increments are minimized % over the specified controller horizon. % 3)Control weighting factor. This term multiplies the squared controller % increments in the performance index. As this term is increased, the % control signal will become smoother. % 4)Minimization routine. You can select a line search routine to be used % in the optimization process. % 5)Search parameter alpha. This specifies how much the performance must % be reduced at each iteration of the optimization algorithm. It should % much less than 1. % 6)Iterations per sample time. Specified the number of iterations of the % optimization algorithm that will be performed at each sampling interval. % Copyright 1992-2013 The MathWorks, Inc.