Hydraulic compliance identification using a parallel genetic algorithm

Abstract

The application of a genetic algorithm to the estimation of hydraulic system parameters in heavy-duty hydraulically-actuated manipulators is described. Identification of hydraulic compliance (indication of joint flexibility) was particularly of interest because of its significant effect on the performance of the class of machines under investigation. Robust and fast identification of this important parameter is essential for diagnosis purposes as well as for improving the control actions. Using real experimental data obtained from an instrumented Caterpillar 215B excavator, the values of the hydraulic compliance for various links were successfully identified. The algorithm was able to handle the nonlinear and coupled actuation dynamics of the hydraulic system with its nonrecursive, population-based search power. The scheme was further studied in order to enhance its speed as well as its parameter tracking ability. Two different parallelization techniques, namely synchronous master-slave and cooperating sequential, were combined at both population and fitness evaluation levels. The algorithm was then implemented using 16 T800 transputers connected to a SUN host workstation. It achieved a speed-up factor of 12 over a traditional genetic algorithm. At such speed, real-time simultaneous monitoring of hydraulic compliances at two joints was possible. Using normal operating data, the right values of compliances from the parameter space were achieved in only a few iterations.