Abstract
This paper presents a self-structured organizing single-input control system based on differentiable cerebellar model articulation controller (CMAC) for an n-link robot manipulator to achieve the high-precision position tracking. In the proposed scheme, the single-input CMAC controller is solely used to control the plant, so the input space dimension of CMAC can be simplified and no conventional controller is needed. The structure of single-input CMAC will also be self-organized; that is, the layers of single-input CMAC will grow or prune systematically and their receptive functions can be automatically adjusted. The online tuning laws of single-input CMAC parameters are derived in gradient-descent learning method and the discrete-type Lyapunov function is applied to determine the learning rates of the proposed control system so that the stability of the system can be guaranteed. The simulation results of three-link De-icing robot manipulator are provided to verify the effectiveness of the proposed control methodology.
Highlights
Robotic manipulators have to face various uncertainties in their dynamics, such as friction and external disturbance
A three-link De-icing robot manipulator as shown in Figure 1 is utilized in this paper to verify the effectiveness of the proposed control scheme
A structured organizing single-input CMAC (SOSICM) control system is proposed to control the joint position of a three-link De-icing robot manipulator
Summary
Robotic manipulators have to face various uncertainties in their dynamics, such as friction and external disturbance. The authors proposed a multilayer hierarchical CMAC model and used Shannon’s entropy measure and golden-section search method to determine the input space quantization Their approach is too complicated and lacks online real-time adaptation ability. We propose a novel self-structured organizing single-input CMAC (SOSICM) control system for three-link De-icing robot manipulator to achieve the high-precision position tracking. This control system combines advantages of S-CMAC and it does not require prior knowledge of a certain amount of memory space, and the self-organizing approach demonstrates the properties of generating and pruning the input layers automatically.
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