Optimal design and stability control of an automotive magnetorheological brake considering the temperature effect

Magnetorheological brake (MRB) provide a potential alternative to traditional mechanical friction brakes in automobile applications owing to their technical advantages in terms of being compact yet powerful, having superior control performance, and wire-control features. However, the temperature effect has been an important issue that should be considered in the design and precise control of MRB. This paper presents the multi-objective optimal design and stability control of an automotive MRB considering the temperature effect. First, a description of the configuration design, magnetostatic field simulation, and mathematical modeling of the automotive MRB is presented in detail. Subsequently, design optimization of the MRB is carried out. The design of experiment method was adopted to screen out the major design variables, and the optimal solution was obtained using a multi-objective genetic algorithm (GA) non-dominated sorting genetic algorithm (NSGA-II). Thereafter, the torque output and response performance, as well as the temperature characteristics of the optimal MRB prototype are experimentally evaluated. The results indicate that the optimal design of the MRB was reasonable and effective. Finally, a GA-based back propagation neural network proportion integration differentiation controller is proposed for the stability control of the MRB during automobile braking. Its performance was verified to be satisfactory through both simulations and experiments.