基于人工神经网络的强化学习方法控制非线性液位系统matlab实现.rar

% REINFORCEMENT LEARNING CONTROL OF TWO TANK LIQUID LEVEL SYSTEM % Dr. Mathew Mithra Noel % School of Electrical Engineering % Vellore Institute of Technology clear all; clc; % Define final desired goal state global state_desired; state_desired= 7; % Flow to Tank 2 is not controlled and hence set to zero global Q2; Q2=0; % Discretize state space global h1; global h2; h1=linspace(0,10,15); h2=h1; global delta; delta= (h1(2)-h1(1))/2; % Discretize action space global action; Q1=linspace(0,20,10); N1 = length(h1); N2 = length(h2); % Initialize policy and value. pibest = zeros(N1,N2); gamma =0.99; % Set the initial guess for V(s) to be zero for each state s. V = zeros(N1,N2); policy = zeros(N1,N2); % Compute the optimal value function using the Value Iteration algorithm. for runs=1:1000 for m=1:N1 for n=1:N2 for p =1:length(Q1) % Take all possible actions. action = Q1(p); snext = [h1(m); h2(n)]+ 0.1*tank(0,[h1(m); h2(n)]); % Compute the closest discretized state. [r,s] = closest(snext); nextV(p)=V(r,s); end [Vbest,bestind] = max(nextV); % Improve value function estimate using Bellman's equation. V(m,n)= Reward([h1(m); h2(n)] ) + gamma*Vbest ; end end end % Compute the optimal policy from the optimal value function. for m=1:N1 for n=1:N2 % Take all possible actions. for p =1:length(Q1) action = Q1(p); snext = [h1(m); h2(n)]+ 0.1*tank(0,[h1(m); h2(n)]); % Compute the closest discretized state. [r,s] = closest(snext); nextV(p)=V(r,s); end [Vbest,bestind] = max(nextV); pibest(m,n) = Q1(bestind); end end N = 100; state=[1 0]; %Initial state states = zeros(N,2); states(1,:)= state ; Ts = 0.1; % Define time between control actions. % Simulate the system with the optimal control policy. for n=2:N [r,s] = closest(state); % Use linear regression to interpolate between control actions for % discretized states % A Feedfroward Neural Network can also be used as in the following paper: % Control of a nonlinear liquid level system using a new artificial neural network based reinforcement learning approach, % Applied Soft Computing, Volume 23, 2014, Pages 444-451, ISSN 1568-4946, https://doi.org/10.1016/j.asoc.2014.06.037. % (http://www.sciencedirect.com/science/article/pii/S1568494614003111) if r > 1 && s > 1 && r < N1 && s < N2 X = [h1(r) h2(s);h1(r-1) h2(s);h1(r+1) h2(s);h1(r) h2(s-1);h1(r) h2(s+1)]; Y = [pibest(r,s) pibest(r-1,s) pibest(r+1,s) pibest(r,s-1) pibest(r,s+1)]'; lin_model = fitlm(X,Y); action = predict(lin_model,state); else action = pibest(r,s); end %Simulate the system for one time step. [t,y]=ode45(@tank,[0 Ts],state); state = real(y(end,:)); states(n,:) = state; end % Plot time history of states with optimal policy. time = (1:length(states))*Ts; plot(time,states); xlabel('time (s)'); ylabel('state: liquid levels h_1 and h_2');