Design of a Neural Controller Using Reinforcement Learning to Control a Rotational Inverted Pendulum

Artificial intelligence (AI) has an increasing influence in the manufacturing and processing industries. An increasingly interesting area of application is the design of controllers for technical systems through reinforcement learning. This allows the design of a controller with reduced effort for human experts, which is normally involved in the design process, for example by eliminating the determination of suitable controller parameters. In this paper a neural controller for a rotational inverted pendulum is developed by using artificial neural networks in combination with reinforcement learning. The rotational inverted pendulum is a classic example of a nonlinear and unstable system and has been little covered in previous publications on this subject. Furthermore, the behaviour of the neural controller is compared with a conventional controller when swinging up and balancing a rotational inverted pendulum.