An intelligent sliding mode controller based on LAMDA for a class of SISO uncertain systems

In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.

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Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network.

Adaptive finite-time super-twisting sliding mode control for robotic manipulators with control backlash

Adaptive fuzzy tracking for flexible-joint robots with random noises via command filter control

PR-FCM: A Polynomial Regression-Based Fuzzy C-Means Algorithm for Attribute-Associated Data

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

Study the micro chaotic phenomena and its control of rigid-flexible robot

To accurately, stably, and efficiently control the complex rigid‐flexible coupled robot, this paper takes Delta robot as the study object and decomposes it into slow subsystem and fast subsystem in different timescales by using singular perturbation principle, which the slow subsystem representing the rigid motion and the fast subsystem representing flexible vibration. In addition, the backstepping control system is designed for the slow subsystem, and the dynamic surface control system based on K‐observer is designed for the fast subsystem, and the overall control scheme is proposed by combining the backstepping control system and the dynamic surface control system, and the stability of the proposed overall control scheme is proven. Finally, the proposed control scheme is compared with proportional derivative control and trajectory control with workspace lattices, and the related experimental results are analyzed in details.

Kunming Zheng

Papers

Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system

Study the micro chaotic phenomena and its control of rigid-flexible robot

Trajectory control and vibration suppression of rigid‐flexible parallel robot based on singular perturbation method

Adaptive memetic differential evolution-back propagation-fuzzy neural network algorithm for robot control

Investigation on particle assisted through-mask electrochemical etching of micro pit array