Abstract
Controlling feedback control systems in continuous action spaces has always been a challenging problem. Nevertheless, reinforcement learning is mainly an area of artificial intelligence (AI) because it has been used in process control for more than a decade. However, the existing algorithms are unable to provide satisfactory results. Therefore, this research uses a reinforcement learning (RL) algorithm to manage the control system. We propose an adaptive speed control of the motor system based on depth deterministic strategy gradient (DDPG). The actor-critic scenario using DDPG is implemented to build the RL agent. In addition, a framework has been created for traditional feedback control systems to make RL implementation easier for control systems. The RL algorithms are robust and proficient in using trial and error to search for the best strategy. Our proposed algorithm is a deep deterministic policy gradient, in which a large amount of training data trains the agent. Once the system is trained, the agent can automatically adjust the control parameters. The algorithm has been developed using Python 3.6 and the simulation results are evaluated in the MATLAB/Simulink environment. The performance of the proposed RL algorithm is compared with a proportional integral derivative (PID) controller and a linear quadratic regulator (LQR) controller. The simulation results of the proposed scheme are promising for the feedback control problems.
Highlights
Reinforcement learning is a leading neural network method used to train and process intelligent decisions to optimize a control system
Training the model requires a different number of episodes and further tests the trained model to check whether the algorithm has learned an excellent optimal strategy or needs more episodes to train
In Tab. 2, we have shown the similarities between reinforcement learning (RL) and the control system
Summary
Reinforcement learning is a leading neural network method used to train and process intelligent decisions to optimize a control system. It allows agents/bots to learn through trial and error search by interacting with the environment using a reward function. It is exciting to use model-free RL for model-free optimal control in the continuous domain. We have designed a universal controller to overcome the prescribed shortcomings. This controller can accurately learn the feedback control law from the data. Since RL methods are model-free, they use Actor-Critic (AC) scenarios to learn closed-loop control laws directly through collaboration with factories or systems without prominent model recognition. The key contributions of this paper are summarized as follows:
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