Aiming at howto achieve optimal control of joint pitch angles in the process of the robot surmounting obstacle, taking the developed\ncoal mine rescue snake robot as an experimental platform, a pose control algorithm based on particle swarm optimization weight\ncoefficient of extreme learning machine (PSOELM) is proposed. In order to obtain the optimized hidden layer matrix of the\nextreme learning machine (ELM), particle swarm optimization (PSO) is applied to optimize the weight coefficient of hidden layer\nmatrix.The simulation and experiment results prove that, compared with the ELMalgorithm, the smallermean square error (MSE)\nbetween the joint pitch angles of robot and the expected values is acquired by the PSOELM, which overcomes the shortcoming that\ntraditional extreme learning machine cannot reach the best performance because of the random selection of the parameters of the\nhidden layer nodes. PSOELM is superior to ELM algorithm in control accuracy, fast searching for the optimal and stability.Optimal\ncontrol of robot�s joint pitch angles is achieved. Thealgorithm is applied to the surmounting obstacle control of the developed snake\nrobot, and it lays the foundation for further implement of the coal mine rescue.
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