Research on Target Path Tracking Method for Smart Car Using Multi-Objective Bionic Game Theory

To improve smart car drive performance and avoid side-slip during target path tracking, a linearized four wheel car model was adopted as a predictive control model, and target path tracking method was built based on multi-objective bionic game theory. Through calculating interaction factors, it established mapping factor indicators between design variables and objective functions, solving for the respective strategy spaces of each game participant, and ensuring that all game participants follow a common constraint protocol. The behavior and survival mechanism of side-blotched lizard were studied; Opportunism, egoism and collectivism were defined according to it’s own color, and three behaviors were considered as set by the corresponding player. Based on the behavioral characteristics exhibited by each species during the evolutionary process, it established the mapping relationships among respective adaptation and objective functions to evaluate the three evolved lizard individuals nature adaptability. During evolution, three types of male lateral spotted lizards evolve games with their respective fitness functions as the objective. After each round of evolutionary games, the optimal genes of the three lizards were solved, and new chromosomes were constructed. Convergence criteria were used to determine convergence, and after multiple evolutionary iterations, the optimal chromosome, multi-objective solution was obtained. The bio-mimetic lizard evolutionary game algorithm was used to solve the smoothness index, and the simulation results showed the effectiveness of this algorithm. The test results show the method can track smart car quickly and steadily, and has good real-time performance.