Design of an Optimal Fuzzy Controller for Antilock Braking Systems

An investigation of the effects of pneumatic actuator design on slip control for heavy vehicles

“…Mauer [17] combined fuzzy logic and decision logic networks to generate a brake pressure demand signal for passenger cars based on the current wheel slip and brake pressure. Mirzaei et al [18] built on Mauer's work by attempting to create a fuzzy logic slip controller for passenger cars that tuned itself online using parallel genetic algorithms. The authors paid particular attention to modelling the hydraulic system dynamics in their simulations.…”

“…The sliding condition is analogous to Lyapunov's direct method, and ensures that the sliding surface is attractive and reached asymptotically. A term that is discontinuous across the surface s s = 0 is added to Equation (18) to satisfy the inequality in Equation (19):…”

Backstepping dynamic surface control for an anti-skid braking system

“…Among these methods, sliding-mode control is commonly used to reduce the dependency on a model (de Castro, Araujo, & Freitas, 2013;Harifi, Aghagolzadeh, Alizadeh, & Sadeghi, 2008;Lin & Hsu, 2003;Subudhi & Ge, 2012). Moreover, a class of fuzzy/neural network controls and their combination with adaptive approaches (Ćirović Contents Mirzaei et al, 2005;Sharkawy, 2010) have been used for adaptive prediction of wheel slip. In addition, feedback control (Mirzaeinejad & Mirzaei, 2010;Tanelli et al, 2008), iterative learning control (Mi, Lin, & Zhang, 2005) and extremum seeking control (Dincmen & Güvenc, 2012;Dincmen et al, 2014;Zhang & Ordóñez, 2007) are applied in this field.…”

Genetic fuzzy system for servo motors with a buck converter

“…Since genetic algorithms [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] are based on natural selection and natural genetics, and possess the simple implement ability and the capability of escaping from local optima, they have been incorporated into the design of fuzzy logic systems and/or neural networks systematically. In [18][19], for the fuzzy systems, the learning process utilizes genetic algorithms rather than the conventional learning methods.…”