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Pneumatic muscle joints have the character of flexibility, meanwhile it increases angle tracking control difficulty. Static equilibrium equation of pneumatic muscle joint was established to analyze the nonlinear relationship between pressure and angle. To overcome single neuron adaptive PID control algorithm’s weaknesses of slow convergence rate and low control accuracy, three improved methods were proposed from three aspects of feedback cyclicality,proportional gain coefficient and attenuation factor. All of these were based on Hebb learning rule. From the experiments we have done on physical platform and contrastive analysis of the results ,we can concluded that (1)Decreasing closed-loop feedback cyclicality can improve tracking accuracy .But it will cause concussion if closed-loop feedback cyclicality is too small.(2) A sigmoid function associated with error is proposed to define proportional gain coefficient. This method enhances the adaptive ability and improves the tracking accuracy. (3) An attenuation factor can be added to simulate people’s forgotten learning mechanism, which can improve convergence rate of weight numbers.