Abstract
This paper presents innovative reinforcement learning methods for automatically tuning the parameters of a proportional integral derivative controller. Conventionally, the high dimension of the Q-table is a primary drawback when implementing a reinforcement learning algorithm. To overcome the obstacle, the idea underlying the n-armed bandit problem is used in this paper. Moreover, gain-scheduled actions are presented to tune the algorithms to improve the overall system behavior; therefore, the proposed controllers fulfill the multiple performance requirements. An experiment was conducted for the piezo-actuated stage to illustrate the effectiveness of the proposed control designs relative to competing algorithms.
Highlights
Design and Comparison of Recent advances in technology have ushered in the fourth industrial revolution (4IR), a concept introduced by Klaus Schwab [1] that includes three-dimensional (3D) printing, virtual reality, and artificial intelligence (AI), with AI being the most active [1]
The proposed control designs were tested along the x-direction of the piezo-actuated stage PI P-602.2CL; a data acquisition (DAQ) card NI PCIe-6346 was employed as an analog
This paper presents a time-varying proportional integral derivative (PID) controller design in which the control gains are automatically adjusted by the reinforcement algorithms
Summary
Design and Comparison of Recent advances in technology have ushered in the fourth industrial revolution (4IR), a concept introduced by Klaus Schwab [1] that includes three-dimensional (3D) printing, virtual reality, and artificial intelligence (AI), with AI being the most active [1]. As presented in [2], the branches of AI include expert systems, machine learning (ML), robotics, computer vision, planning, and natural language processing (NLP). ML is when an algorithm learns from data. The training set contains both the inputs and desired outputs. The training set is used to teach the model to generate the desired output, and the goal of supervised learning is to learn a mapping between the input and the output spaces [3]. Supervised learning was often used in image recognition, and self-supervised semi-supervised learning (S4L) was proposed to solve the image classification problem [4]
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