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access icon free Semi-fuzzy CMAC and PD hybrid controller with compressed memory and semi-regularisation for electric load simulator

© The Institution of Engineering and Technology
Received 13/12/2018, Accepted 02/09/2019, Revised 25/07/2019, Published 03/09/2019

Cerebellar model articulation controller (CMAC) and proportional derivative hybrid controller is widely used for torque control in electric load simulator. However, due to some uncertain factors and especially the strong external interference of the surplus torque, the control stability could not be guaranteed. Here, a novel semi-regularised semi-fuzzy CMAC is proposed to get an efficient control effect. This study expands the weight smoothing into a semi-regularisation algorithm, and proves the stability of the hybrid control system based on the stability of the torque motor. To save storage space and reduce computation, the memory space is compressed and a semi-fuzzy mapping rule is proposed to smooth the output of CMAC with a high calculation speed. Both of simulation and experiment results demonstrate that this novel controller could keep stable and reach a good loading accuracy.

Inspec keywords: torque control; stability; cerebellar model arithmetic computers; pneumatic control equipment; neurocontrollers; fuzzy control; fuzzy set theory

Other keywords: memory space; torque control; control stability; semifuzzy mapping rule; efficient control effect; uncertain factors; proportional derivative hybrid controller; torque motor; novel semiregularised semifuzzy CMAC; compressed memory; hybrid control system; electric load simulator; cerebellar model articulation controller; PD hybrid controller; strong external interference; semiregularisation algorithm

Subjects: Combinatorial mathematics; Fuzzy control; Hydraulic and pneumatic control equipment; Stability in control theory; Neurocontrol; Mechanical variables control

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