Frequency-Domain Data-Driven Position-Dependent Controller Synthesis for Cartesian Robots

Cartesian robots have position-dependent dynamics that must be taken into account for high-performance applications. Traditional methods design linear time-invariant (LTI) controllers are robustly stable with respect to position variations but result in reduced performance. Advanced require parameter varying (LPV) models and LPV controller not well-established the industry. On other hand, classical model-based gain-scheduled technique involves parametric identification, each position, interpolation of parameters, real-time validation, making it time-consuming costly. Our approach uses frequency response at different operating points an using a convex optimization algorithm based on second-order cone programming. The is applied industrial three-axis robot, showing significant improvements over state-of-the-art control strategies. Data acquisition can performed automatically, reducing significantly engineering costs synthesis.