Neural network-based fault-tolerant control approach considering a submarine system

Abstract

The stability and satisfactory performance of a fault-tolerant control approach is important for designers and therefore the corresponding closed-loop systems are essential to be dealt with. Considering one of the most important faults including the operator fault in robotic systems leads us to reach the better performance, usually. The radial basis function neural network-based fault-tolerant control method is designed in this research to estimate the model uncertainties, the noise affecting the system, and the operator fault, in order to compensate this one. The results of simulating the fault-tolerant control system propose that this technique successfully secured the tracking function of the control system besides ensuring closed-loop stability. Since the traditional control methods fail to effectively fix the aforementioned problem, a new method so-called neural-network-based fault-tolerant control is proposed. This study proposes the design of a nominal controller, its examinations under operator faults, and finally the expansion of the controller into a fault-tolerant controller. In a word, the contribution made in the research over the state-of-the-art materials focusing on fault-tolerant controls designed in the area of submarine systems is briefly taken into real consideration as its nonlinearity, its fixed structure, the lack of need for switching systems, resistance to noise, and the ability to compensate the effect of the performance drop fault on the collective fault of the operators. Subsequently, the simulation results verified the effectiveness of the proposed approach in coping with the operator’s performance faults, tangibly.