Robot Tracking Control With Natural Task-Space Decoupling

There exist numerous ways to achieve multitasking control in kinematically redundant robots to accomplish several goals simultaneously. In all approaches, regardless of the specific type of controller, one has to make a choice about the closed-loop inertia and consequently the dynamic task couplings. Here, we introduce a new control strategy that combines two fundamentally different properties that have not yet been brought together. First, we fully and dynamically decouple all individual subtasks, which cannot be achieved with classical passivity-based or hierarchical approaches. Second, we provide high robustness in practice, which is structurally not possible with any inverse dynamics approaches enforcing a decoupled but constant closed-loop inertia. Besides formal proofs of stability and passivity, we compare our approach with the other categories in various simulations and experiments. Since the proposed controller is grounded in the fundamental property of full natural task-space decoupling, this underlying strategy and its benefits can also be transferred to other design methods such as quadratic programming, model-predictive control, or learning-based approaches.