四旋翼的改进 PSO-RBF 神经网络自适应滑模控制

An improved particle swarm optimization-radial basis function (PSO-RBF) neural network adaptive sliding mode controller is proposed for quadrotor systems with nonlinearity, strong coupling, and inaccurate interference. First, based on smooth improvement of the control amount of the RBF neural network sliding mode controller, an improved particle swarm optimization with global optimization capability was used to adjust the fitting parameters of the RBF neural network, thus improving the fitting ability of the network. Next, a dynamic model of quadrotor was built according to themodel parameters of actual quadrotors, the stability of which was then proved by Lyapunov theory.In contrast to the RBF neural network adaptive sliding mode controller and the double closed-loop PID controller, the improved PSO-RBF neural network adaptive sliding mode controller can find the appropriate control quantity in one control cycle, and its adjustment time is about 50% and 75% faster than that of RBF neural network adaptive sliding mode controller and double closed-loop PID controller, respectively. The simulation results show that the improved PSO-RBF neural network adaptive sliding mode controller featuresfasttrack speed with high accuracy, strong disturbance rejection and better robustness.