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The papers by the PID control, particle swarm optimization algorithm , ship motion mathematical model , applied to the organic combination of ship course control . Particle swarm optimization algorithm principle , this paper presents an adaptive search space adaptive particle swarm optimization . The algorithm to adapt to the different levels of particles values ??take different inertia weight weight , and continue to narrow the search space of the particle swarm with the iteration of the algorithm , at the same time to select the better part of the current generation of particles directly into the next generation , other particles through reduced randomly generated within the search space . This paper, particle swarm optimization algorithm to improve the convergence speed and precision of convergence of the algorithm is proved by testing a series of standard functions . Improved particle swarm optimization algorithm applied to PID control design , using the improved particle swarm optimization algorithm offline tuning PID controller three parameters , and simulation comparison experiments car inverted pendulum model , for example , through simulation comparison experiments proved based improved particle swarm optimization car inverted pendulum the PID control settling time is shorter , more stable control performance . Finally , the proposed particle swarm optimization algorithm applied to ship course PID control , using the improved particle swarm optimization three parameters of the PID controller tuning algorithm offline ship heading , and parameter tuning PID controller is compared with the use of genetic algorithms , the experimental results demonstrate the improved particle swarm optimization ship Heading PID autopilot performance has been greatly improved , the system has no overshoot , rise fast , stable work , has strong robust performance .
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