Real-time control of ball balancer using neural integrated fuzzy controller

Research on Solving Nonlinear Problem of Ball and Beam System by Introducing Detail-Reward Function

As a complex nonlinear system, the first-order incremental relationship between the state variables of the beam and ball system (BABS) is asymmetric in the definition domain of the variables, and the characteristics of the system do not satisfy the superposition theorem. Studying the balance control of the BABS can help to better grasp the relevant characteristics of the nonlinear system. In this paper, the deep reinforcement learning method is used to study the BABS based on a visual sensor. First, the detail-reward function is designed by observing the control details of the system, and the rationality of the function is proved based on Q-function; secondly, considering and comparing the applicability of image processing methods in ball coordinate location, an intelligent location algorithm is proposed, and the location effects between the algorithms are compared and analyzed; then, combining the nonlinear theory and LQR theory, a reinforcement learning policy model is proposed to linearize near the equilibrium point, which significantly improves the control effect. Finally, experiments are designed to verify the effectiveness of the above methods in the control system. The experimental results show that the design scheme can be effectively applied to the control system of the BABS. It is verified that the introduction of detail-reward mechanism into a deep reinforcement learning algorithm can significantly reduce the complexity of the nonlinear control system and iterative algorithm, and effectively solve nonlinear control problems.

Comparison of various fuzzy sliding mode based controller on single link inverted pendulum

The present work represents the implementation of the various fuzzy controller with robust sliding mode control (SMC) technique on a nonlinear system considering various external disturbances and model uncertainties. The nonlinear system considered here is a single link inverted pendulum. The proposed work combines the advantages of the sliding mode controlling technique and fuzzy logic controller. A set of linguistic rules are designed in fuzzy logic control, which causes the system to be chattering free. Parameters of the nonlinear system are adjusted according to fuzzy adaptive laws, while the uncertainties of the nonlinear system have been approximated using a fuzzy system. Various types of controller based on fuzzy sliding mode, like approximation based sliding mode control technique; equivalent control based fuzzy sliding mode technique, and switch-gain regulation based sliding mode control methods have been implemented here. A comparative analysis of various methods is also have been discussed.