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To meet the control demand of power system in for hydraulic walking platform, based on the brief analysis of digital PID structure and its shortcoming, fast regulation and control precision control for requirement of power system is given, control algorithm for neural network structure PID is presented, control strategy of closed-loop control is made. Control algorithm and effect of control system is verified in experimental bench, experimental results showed that neural network structure PID algorithm effect is stable and it can meet the adjusting requirement in transient speed. Combined with many technologies of articulation mechanism, hydraulic pressure transmission, electronic control and intelligent contro(1), hydraulic walking platform formed by four-legged robot can finish different task in the harsh condition, it plays an important role in emergency and disaster saving. In order to make rapid response for the gait change and work for long time in the wild condition, power system of high power density and flexible control is needed, since there are some advantages of large torque and easy control in variable bump, and small volume, easy starting in gasoline engine(2), small type gasoline engine and variable bump are selected to form power system. Control algorithm is a key part to quickly regulate rotation speed of engine for transient load change, for self-adapt control of more variable profile, PID algorithm is a common method to control gasoline throttle, there were technical materials about PID control for gasoline in periodical and some reports. Among these materials, PID coefficient self-tuning is a research key point based on model or rule, Liu Dao put through PID coefficient self-tuning by improved particle swarm optimization algorithm, it simplified calculation of PID coefficient self-tuning(3). Li Rui xia presented PID coefficient self-tuning strategy with improved genetics algorithm, this method can optimize calculation speed and parameters(4). DE Xiang yi gave PID coefficient self-tuning of fuzzy prediction model, fuzzy control algorithm solved uncertain factor question, prediction model was used to evaluate future deviation, its regulation time was less and control precision was higher (5). Zhang Shi feng presented controller design of PID coefficient self-tuning based on neural network, neural network was added to controller to compute PID coefficient(6), these thesises were focus on independent calculation of PID coefficient and effect for PID control. some disadvantages of digital PID were still existed, complex calculation of coefficient self-tuning, and poor control effect for variable target, In order to emit shortcoming of digital PID, neural network structure PID is presented and calculated in real time based on microprocessor, finally real test is made to verify application effect.

Neural Network Structure PID Control of Power System for Hydraulic Walking Platform