Model Predictive Control for Compliant Pneumatic Systems

Abstract

Pneumatic systems possess inherent compliance and potentially variable stiffness that make them an appealing actuator choice for tracking applications where contact and interaction are likely. However, good control of pneumatic systems is impeded by discontinuous and nonlinear dynamics, especially compliance and friction. The most successful previous solutions have either applied high-gain PD or sliding mode control. These achieve tracking control for compliant systems by transforming them into stiffer ones. Model predictive control can better balance precision tracking with compliance (low output impedance), so that the system is safer in case of collision disturbance. It can be coupled with a predictive observer that estimates friction as a known disturbance. The estimate is incorporated into the optimization, improving friction compensation for pneumatics, which has slow dynamics that do not react quickly enough with traditional feedforward compensation. Finally, predictive control enables constrained finite-time optimization, driving the system closer to its peak performance capability.