CMAC-Adaptive Force-Position Control of a Flexible-Joint Robot

Although many hybrid force-position controllers appear in the literature, the problem of touching and leaving a surface rarely gets addressed - many leave this as a practical matter for the engineers. If the force control results in inappropriate signals in free space then the designer must try to switch controllers at the surface, a solution that can introduce unwanted vibrations; note that stability problems can easily result with such a design in light of imperfect knowledge/measurement of where the surface actually lies and the reality of (possibly unmodelled) joint elasticity. In this work we propose an adaptive backstepping approach that guarantees Lyapunov stability when in contact with the surface and in free-space i.e. without switching, for both non-redundant and redundant manipulators. We develop the controls for a flexible-joint robot in order to demonstrate the guarantee of stability and the ability to avoid excessive vibrations even in the case of elasticity. The proposed controls use neural networks to estimate nonlinear terms and unmodelled dynamics. Simulations show the proposed method significantly outperforms a proportional-derivative hybrid force-position control.