Fast Model Predictive Controller based on Imperialist Competitive Algorithm for Anti-lock Braking SystemABS

The main shortcoming of traditional model predictive control (MPC) methods is that they can only be used in applications with "sufficiently slow" dynamics and hence arenot suitable for many practical applications. For this reason the standard MPC algorithms have been retained away frommodern production vehicles. In fact, a number of inherenthardware constraints and limitations integrated with the vehicle electronic control unit (ECU), such as processing speedand memory, have made on-line implementations of these traditional predictive algorithms almost impossible. Anti-lockbraking systems (ABS) have been developed to reduce tendency for wheel lock and improve vehicle control during sudden braking especially on slippery road surfaces. Variations in the values of weight, the friction coefficient of the road, road inclination and other nonlinear dynamics may highly affect theperformance of ABS. This system, may not be easily controlled by classical control methods. MPC method is very useful forthis kind of nonlinear system. In this paper, MPC with using Imperialist competitive algorithm (ICA) have been improved in ABS.Results of simulation show how the problem of identifying the optimal tradeoff for a given set of requirements on online computation time, storage andperformance can be solved.