MATLAB DDPG PID
时间: 2024-01-27 11:53:59 浏览: 157
这是一个IT类问题。
DDPG(Deep Deterministic Policy Gradient)是一种深度强化学习算法,用于解决连续动作空间的问题。PID(Proportional-Integral-Derivative)则是一种传统的控制算法,常用于控制系统中。
在MATLAB中,可以使用DDPG算法来优化PID控制器的参数,从而实现更好的控制效果。具体实现方法可以参考MATLAB官方文档和相关论文。
相关问题
用于MATLAB的DDPG代码整定PID参数
DDPG(Deep Deterministic Policy Gradient)是一种深度强化学习算法,可以用于连续控制问题的解决。在MATLAB中,可以使用Deep Learning Toolbox中的函数以及Reinforcement Learning Toolbox中的函数来实现DDPG算法。
下面是一个使用DDPG算法来优化PID控制器参数的示例代码:
```matlab
%% 初始化环境
mdl = 'pendulum';
open_system(mdl);
Ts = 0.05;
Tf = 10;
obsInfo = rlNumericSpec([3 1],'LowerLimit',[-pi/2; -8; -Inf],'UpperLimit',[pi/2; 8; Inf]);
obsInfo.Name = 'observations';
obsInfo.Description = 'theta;thetadot;thetaerror';
actInfo = rlNumericSpec([1 1],'LowerLimit',-10,'UpperLimit',10);
actInfo.Name = 'torque';
env = rlSimulinkEnv(mdl,mdl,obsInfo,actInfo);
%% 确定深度神经网络的结构
statePath = [
imageInputLayer([3 1 1],'Normalization','none','Name','observation')
fullyConnectedLayer(64,'Name','CriticStateFC1')
reluLayer('Name','CriticRelu1')
fullyConnectedLayer(64,'Name','CriticStateFC2')];
actionPath = [
imageInputLayer([1 1 1],'Normalization','none','Name','action')
fullyConnectedLayer(64,'Name','CriticActionFC1','BiasLearnRateFactor',0)];
commonPath = [
additionLayer(2,'Name','add')
reluLayer('Name','CriticCommonRelu')
fullyConnectedLayer(1,'Name','output')];
criticNetwork = layerGraph(statePath);
criticNetwork = addLayers(criticNetwork,actionPath);
criticNetwork = addLayers(criticNetwork,commonPath);
criticNetwork = connectLayers(criticNetwork,'CriticStateFC2','add/in1');
criticNetwork = connectLayers(criticNetwork,'CriticActionFC1','add/in2');
%% 建立深度决策网络
actorNetwork = [
imageInputLayer([3 1 1],'Normalization','none','Name','observation')
fullyConnectedLayer(64,'Name','ActorFC1')
reluLayer('Name','ActorRelu1')
fullyConnectedLayer(64,'Name','ActorFC2')
reluLayer('Name','ActorRelu2')
fullyConnectedLayer(1,'Name','ActorFC3')
tanhLayer('Name','ActorTanh1')
scalingLayer('Name','ActorScaling1','Scale',2)];
%% 设置DDPG代理
agentOpts = rlDDPGAgentOptions;
agentOpts.SampleTime = Ts;
agentOpts.DiscountFactor = 0.99;
agentOpts.MiniBatchSize = 256;
agentOpts.ExperienceBufferLength = 1e6;
agentOpts.TargetSmoothFactor = 1e-3;
agentOpts.NoiseOptions.Variance = 0.2;
agentOpts.NoiseOptions.VarianceDecayRate = 1e-5;
agentOpts.SaveExperienceBufferWithAgent = true;
agentOpts.ResetExperienceBufferBeforeTraining = false;
agentOpts.UseParallel = false;
agentOpts.UseGPU = false;
agent = rlDDPGAgent(actorNetwork,criticNetwork,agentOpts);
%% 训练代理
trainOpts = rlTrainingOptions;
trainOpts.MaxEpisodes = 100;
trainOpts.MaxStepsPerEpisode = ceil(Tf/Ts);
trainOpts.StopTrainingCriteria = 'AverageReward';
trainOpts.StopTrainingValue = -400;
trainOpts.ScoreAveragingWindowLength = 30;
trainOpts.SaveAgentCriteria = 'EpisodeReward';
trainOpts.SaveAgentValue = -350;
trainOpts.Plots = 'training-progress';
trainingStats = train(agent,env,trainOpts);
```
在这个示例中,我们使用了一个具有64个神经元的深度神经网络来近似Q函数,另一个具有64个神经元的深度神经网络来确定actor的策略。我们还设置了一些DDPG代理的选项,如样本时间、折扣因子、噪声参数等。
在训练代理之后,我们可以使用以下代码来测试代理的性能,并使用优化的PID参数控制摆杆系统:
```matlab
agentOpts.NoiseOptions.Variance = 0;
simOpts = rlSimulationOptions('MaxSteps',1000);
experience = sim(env,agent,simOpts);
```
这段代码将关闭DDPG代理的噪声,并使用优化的PID参数控制摆杆系统。
写出在MATLAB中,使用DDPG优化PID参数的代码
### 回答1:
我们可以使用MATLAB中的rlDDPGTuning函数来优化PID参数。代码如下:[KP,KI,KD] = rlDDPGTuning(@PID_controller,pid); 其中,@PID_controller是控制器的函数句柄,pid是初始PID参数的向量。
### 回答2:
在MATLAB中使用DDPG算法来优化PID参数的代码如下:
``` matlab
% DDPG优化PID参数的代码
% 步骤1:定义DDPG网络结构
% 定义Actor网络结构
actor_network = [
imageInputLayer([state_dimension, 1, 1],'Normalization','none','Name','state')
fullyConnectedLayer(400,'Name','fc1')
reluLayer('Name','relu1')
fullyConnectedLayer(300,'Name','fc2')
reluLayer('Name','relu2')
fullyConnectedLayer(action_dimension,'Name','output')];
% 定义Critic网络结构
critic_network = [
imageInputLayer([state_dimension, 1, 1],'Normalization','none','Name','state')
fullyConnectedLayer(400,'Name','fc1')
reluLayer('Name','relu1')
fullyConnectedLayer(300,'Name','fc2')
reluLayer('Name','relu2')
fullyConnectedLayer(1,'Name','output')];
% 步骤2:定义DDPG算法参数
agentOpts = rlDDPGAgentOptions;
agentOpts.SampleTime = step_size;
agentOpts.DiscountFactor = discount_factor;
agentOpts.TargetSmoothFactor = target_smoothing_factor;
% 步骤3:创建DDPG代理
agent = rlDDPGAgent(actor_network, critic_network, agentOpts);
% 步骤4:定义环境
env = rlSimulinkEnv(system_name, block_names, signals);
% 步骤5:优化PID参数
% 训练步数
num_episodes = 100;
for episode = 1:num_episodes
% 重置环境
obs = resetObservation(env);
done = false;
while ~done
% 选择动作
action = selectAction(agent, obs);
% 执行动作并观察下一个状态、回报和终止信号
[nextObs, reward, done] = step(env, action);
% 存储经验
experience = rlExperience(obs, action, reward, nextObs, done);
% 每步学习
agent = learn(agent, experience);
% 更新当前状态
obs = nextObs;
end
end
```
以上代码演示了使用DDPG算法优化PID参数的过程,其中定义了Actor和Critic网络结构,设置了DDPG算法参数,创建了DDPG代理,定义了环境,并执行了训练循环来逐步优化PID参数。需要注意的是,上述代码中的`state_dimension`、`action_dimension`、`step_size`、`discount_factor`、`target_smoothing_factor`、`system_name`和`block_names`等参数需要根据实际情况进行设置。
### 回答3:
在MATLAB中使用DDPG(Deep Deterministic Policy Gradient)算法优化PID参数的代码如下:
```matlab
% 系统模型和初始PID参数设置
sys = tf([1],[1,1]);
Kp = 1;
Ki = 0.5;
Kd = 0.1;
pid = pid(Kp, Ki, Kd);
% 状态和行为空间定义
obsDim = 1; % 状态空间维度
actionDim = 3; % 行为空间维度
obsUB = 10; % 状态上界
obsLB = -10; % 状态下界
actionUB = 1; % 行为上界
actionLB = -1; % 行为下界
% 设置DDPG超参数
actorOpts = rlRepresentationOptions('Observation',{'Continuous'},...
'Action',{'Continuous'},'ActionSampleType','Gaussian');
criticOpts = rlRepresentationOptions('Observation',{'Continuous'},...
'Action',{'Continuous'});
actor = rlDeterministicActorRepresentation(actorOpts,obsDim,actionDim);
critic = rlQValueRepresentation(criticOpts,obsDim,actionDim);
agentOpts = rlDDPGAgentOptions('SampleTime',0.01,...
'TargetSmoothFactor',1e-3,'DiscountFactor',0.99);
agent = rlDDPGAgent(actor,critic,agentOpts);
% 创建环境
env = rlSimulinkEnv(sys,'ResetFcn',@(in)setParams(in,Kp,Ki,Kd),'StopFcn',@(in,~,logs)stopSim(in,false,logs));
env.ResetFcn = @(in)setParams(in,Kp,Ki,Kd);
% 训练
trainOpts = rlTrainingOptions('MaxEpisodes',1000,'MaxStepsPerEpisode',200,...
'Verbose',false,'Plots','training-progress');
trainOpts.ScoreAveragingWindowLength = 30;
trainOpts.StopTrainingCriteria = "AverageReward";
trainOpts.StopTrainingValue = inf;
% RL网络训练
doTraining = true;
while doTraining
% 训练DDPG智能体
trainingStats = train(agent,env,trainOpts);
% 检查训练终止的条件
if trainingStats.AverageReward > -50
doTraining = false;
else
% 更新PID参数
action = predict(actor,obs);
Kp = Kp + action(1);
Ki = Ki + action(2);
Kd = Kd + action(3);
pid = pid(Kp, Ki, Kd);
env.ResetFcn = @(in)setParams(in,Kp,Ki,Kd);
end
end
% 设置新的PID控制器参数
function setParams(in,Kp,Ki,Kd)
in.mass = Kp;
in.damping = Ki;
in.spring = Kd;
end
% 检查训练是否终止并停止仿真
function stopSim(in,isdone,logs)
if isdone
Kp = 0.1;
Ki = 0.1;
Kd = 0.1;
pid = pid(Kp, Ki, Kd);
setParams(in,Kp,Ki,Kd);
logs.States{end,:) = in;
logs.Rewards{end} = -50;
end
stop(in);
end
```
该代码中,首先定义了系统模型、初始PID参数和状态、行为空间的设置。然后设置了DDPG算法的超参数,并创建了DDPG智能体对象。接着创建了Simulink环境,用于模拟系统。利用训练选项和训练函数对DDPG智能体进行训练,同时在每次训练迭代中根据智能体的输出更新PID参数。最后定义了设置PID参数的函数和停止仿真的函数。
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