Building intelligent transportation systems based on quasi-optimal control structures and fuzzy logical inference

It is considered the problem of providing automatic control of dynamic objects that are part of an intelligent transport system and operate in various modes which requires an increase in the efficiency of the control laws used in order to maintain speed, stability and others. The initial system is presented in the form of Lagrange equations of the second kind, which restrictions are imposed on it to guarantee full controllability of the system. To construct the control, the principle of decomposition is used, which divides the system with n degrees of freedom into n independent subsystems with limited perturbations. In this paper, an intelligent control law for a subsystem with a priori unknown perturbations is constructed based on a law that is quasi-optimal in terms of speed and a law that ensures the maximization of the attraction area, using fuzzy logical inference. Modeling is carried out on examples with various non-linear types of perturbations. The analysis of the simulation results shows that the constructed control law ensures that the ambit of the terminal point is reached in a time close to optimal, while, in comparison with the known quasi-optimal control law in terms of speed, the proposed law does not enter the oscillatory mode near the terminal point, and also has no discontinuity points on trajectories of motion of a nonlinear dynamic system. It is shown that the application of the decomposition principle makes it possible to construct intelligent control laws for a system with several degrees of freedom.

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