PD Control with Neural Compensation
Abstract
In this chapter, we use a neural network to estimate the nonlinear terms of the exoskeleton robots, such as friction, gravity forces, and unmodeled dynamics, to guarantee stability of the closed-loop system with simple PD control law. This approach reduces the computation time with a simple neural network (NN). The learning rules obtained for the NN are very closed to the backpropagation rules [64]. This NN does not need offline previous learning, and the initial parameters are independent of the robot dynamics.
The other part of the chapter uses a modified algorithm to overcome the two drawbacks of PD control at the same time. First, the high-gain observer is joined with the normal ...
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