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The previous step CMAC for online tuning robust fuzzy controllers is proposed in this paper. There are two processes in the proposed schemes: one is the robust fuzzy controller and the other is the previous step CMAC learning algorithm. The robust fuzzy controller can achieve a certain goal without concern for instability of the controlled system in the presence of significant plant uncertainties,...
In this paper, a novel scheme of incorporating a learning mechanism into previous step supervisory controllers for adaptive fuzzy control is proposed to relax bounds required in the control process. In traditional supervisory adaptive fuzzy control approaches, the use of fuzzy estimators for approximating system functions and a robust supervisory control law are necessary to deal with any possible...
One disadvantage of hardware controller is that it cannot learn from experiential information. Many software neural networks and fuzzy algorithms can resolve this problem, but their processes are complicated and waste time. Therefore, a simple firmware controller with learning ability programmed on FPGA is proposed in this paper. The proposed firmware controller is a CMAC based supervisory controller...
The CMAC tuning effects to improve the Hinfin control performance is proposed in this paper. The Hinfin norm is utilized for evaluating the supremum of the robust control variable such that it ensures the Lyapunov stability of the proposed control schemes. Under the Lyapunov stable criterion, we apply the novel pre-step CMAC online tuning scheme for tuning the control variable in order to improve...
In this paper, a novel approach of genetic algorithm based robust learning credit assignment cerebellar model articulation controller (GCA-CMAC) is proposed. The cerebellar model articulation controller (CMAC) is a neurological model, which has an attractive property of learning speed. However, the distributions of errors into the addressed hypercubes of CMAC are not proportional to their credibility...
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