Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Stabilization of the fast modes of a flexible-joint robot
International Journal of Robotics Research
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Intelligent control for autonomous systems
IEEE Spectrum
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Dynamics and Control
| Hi-index | 0.00 |
The increased complexity of the dynamics of robots considering joint elasticity makes conventional model-based control strategies complex and difficult to synthesize. In this paper, a model-free control using integrated PID-type learning and fuzzy control for flexible-joint manipulators is proposed. Optimal PID gains can be learned by a neural network learning algorithm and then a simple standard fuzzy control could be incorporated in the overall control strategy, if needed, for enhancing the system responses. A modified recursive least squares algorithm is suggested for faster learning of the connection weights representing the PID-like gains. Simulation results show that the suggested simple model-free approach can control a complex flexible-joint manipulator to meet stringent requirements for both transient and steady-state performances.