A model-based fault accommodation control scheme that uses a recurrent wavelet Elman neural network (RWENN) is proposed to achieve satisfactory control without performance degradation for biped robot locomotion with unknown uncertainties and faults. In the fault accommodation scheme, a computed torque control is the main control that is used to track the desired trajectory when there is no fault; and a compensation control is used to eliminate the unknown model uncertainties. The proposed RWENN has an input from a context layer with self-feedback and an output recurrent layer to the hidden layer, which increases the precision and convergence time of the network compared with a recurrent neural network, a recurrent fuzzy neural network, and a recurrent wavelet neural network, so that any dynamic change, such as a fault on the system, can be estimated properly. Thus, it enhances the capability of fault accommodation. The adaptive laws of the RWENN-based fault accommodation control are derived from the Lyapunov theorem; hence, the stability of the system can be guaranteed. Finally, a case study of biped robot control with multiple faults and uncertainties is analyzed, and the effectiveness of the proposed fault accommodation scheme is demonstrated by simulation results. Its superiority is also assessed by a numerical comparison with other neural-network-based control schemes.Index Terms-Biped robot, Elman neural network, fault accommodation, recurrent wavelet neural network (RWNN).

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A self-learning fuzzy sliding-mode controller (SLFSMC) is proposed to control the temperature of a water bath. The SLFSMC system automatically tunes the rule bases using a rule modifier and the updating value of each rule is based on the fuzzy firing weight. In addition, this controller can be used for on-line learning in real-time control systems. In order to illustrate the performance of the proposed control method, it is compared with a proportional derivative-type fuzzy control (PDFC) and a gaintuning fuzzy control (GTFC). These three algorithms are applied to a water bath temperature control and are simulated under the same conditions. The effect of load disturbance, the response to control, the tracking performance, and suitable sampling time are determined for each system. The simulation results show that the SLFSMC has superior characteristics, is more simple to use and has a fast response, so the SLFSMC performs better than the PDFC and GTFC.

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