Reinforcement learning based adaptive PID controller design for control of linear/nonlinear unstable processes

PID控制器 控制理论(社会学) 强化学习 非线性系统 计算机科学 控制器(灌溉) 控制工程 自适应控制 控制(管理) 人工智能 温度控制 工程类 物理 农学 量子力学 生物
作者
T. Shuprajhaa,Shivakanth Sujit,K. Srinivasan
出处
期刊:Applied Soft Computing [Elsevier BV]
卷期号:128: 109450-109450 被引量:60
标识
DOI:10.1016/j.asoc.2022.109450
摘要

Control of unstable process is challenging owing to its dynamic nature, output multiplicities and stability issues. This research work focuses to develop a generic data driven modified Proximal Policy Optimization (m-PPO) reinforcement learning based adaptive PID controller (RL-PID) for the control of open loop unstable processes. The RL agent acting as the supervisor explores and identifies optimal gains for the PID controller to ensure desired servo and regulatory performance. Adaptive modifications in terms of inclusion of action repeat, modified reward function and early stopping criterion are incorporated to the m-PPO algorithm to handle the unbounded output nature of unstable processes. Effect of m-PPO algorithm is proven in terms of reward earned by the RL agent. Servo and regulatory performance of the proposed RL-PID controller is compared with that of classical PID controller, Deep Discriminant Policy Gradient based PID controller and Advantage Actor Critic based PID controller on various linear, non linear, multivariable unstable systems including unstable jacketed CSTR process and Unmanned Aerial Vehicle in simulation environment. Validation of the proposed controller is also done in real time level control process station, a laboratory level experimental test rig. It is observed that the proposed RL-PID performs satisfactorily better than the other controllers in both qualitative and quantitative metrics. The striking feature of this control scheme is that it eliminates the need of process modeling and pre-requisite knowledge on process dynamics and controller tuning. The proposed controller is a data driven generic approach that can be directly applied to any industrial process. • Model free data driven controller is proposed for unstable systems. • Reinforcement learning-Proportional Integral Derivative controller is proposed. • Modified Proximal Policy Optimization is employed for optimal tuning of controller. • Early stopping, action repeat and modified reward are used in optimization process. • Validation is done with linear and complex nonlinear unstable systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
congenialboy发布了新的文献求助10
刚刚
孙燕应助ioio采纳,获得20
1秒前
Della发布了新的文献求助10
1秒前
Everything完成签到,获得积分10
2秒前
黄海发布了新的文献求助10
2秒前
3秒前
4秒前
4秒前
幸福大白发布了新的文献求助10
5秒前
5秒前
c2完成签到,获得积分10
5秒前
Hey发布了新的文献求助20
5秒前
5秒前
5秒前
科研力力完成签到 ,获得积分10
5秒前
6秒前
7秒前
7秒前
7秒前
幸福大白发布了新的文献求助10
7秒前
7秒前
c2发布了新的文献求助10
8秒前
lalala发布了新的文献求助10
8秒前
9秒前
殷勤的哈密瓜完成签到,获得积分10
9秒前
10秒前
阜睿发布了新的文献求助10
11秒前
紫竹魔笛发布了新的文献求助10
11秒前
高兴芯发布了新的文献求助10
12秒前
12秒前
展希希发布了新的文献求助10
13秒前
14秒前
李健应助liii采纳,获得10
14秒前
辛辛完成签到,获得积分10
16秒前
17秒前
科研通AI2S应助xiaoyuanbao1988采纳,获得10
17秒前
我是老大应助超男采纳,获得30
18秒前
范范发布了新的文献求助10
20秒前
Nn完成签到 ,获得积分10
20秒前
20秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989510
求助须知:如何正确求助?哪些是违规求助? 3531756
关于积分的说明 11254536
捐赠科研通 3270255
什么是DOI,文献DOI怎么找? 1804947
邀请新用户注册赠送积分活动 882113
科研通“疑难数据库(出版商)”最低求助积分说明 809176