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As the tracked vehicles often run in harsh environments, a certain type of tracked vehicles with hydrodynamic and hydrostatic steering mechanism often have a big pressure fluctuation in the hydrostatic system in the steering process and the steering properties are seriously affected. In order to smoothly match the hydrodynamic and hydrostatic steering mechanism, we implemented the electro-hydraulic proportional valves instead of the oil-filling valves that equipped in the real vehicle and modeled the steering system in the Matlab/Simulink platform in this paper. A PID controller that optimized by the Generic Algorithm (GA) can control the oil flow into the hydraulic equipped according to the fluctuation of pressure was designed. The simulation results showed that the optimized system inhibit the pressure fluctuation well, with a rapid dynamic response and good steering performance.

This paper introduces a simple recursive algorithm for nonlinear dynamic system identification using linear-in-the-parameters models for NARX or RBF network where both the structure and parameters can be obtained simultaneously and recursively. The main objective is to improve the numerical stability when the model terms are highly correlated. This is based on the "innovation" idea and net contribution criteria. Using the recursive formulae for the computation of the Moore-Penrose inverse of matrices and the net contribution of model terms, it is possible to combine the structure term determination and parameters estimation within one framework by adding and deleting an item in the selected candidate model. The formulae for enhancing and reducing a matrix are given. Simulation results show the proposed method is numerically more stable than existing approaches.nonlinear dynamic system, linear-in-the-parameters models, NARX, RBF, Moore-Penrose inverse

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