This article proposes an optimization-based method for robust yet efficient control of flexible-joint robots by
using the model predictive control approach. The time-delay estimation (TDE) technique is used to approximate
uncertain and nonlinear dynamic equations, where neither concrete knowledge of mathematical system model
parameters is required in the approximation, thus granting the model-free property for dynamics compensation
and real-time system linearization. TDE is integrated with model predictive control, which is designated as the
incremental model predictive control (IMPC) framework. This approach guarantees the tracking performance
of the flexible joint robot with input and output constraints, such as motor torque and joint states. Moreover,
the proposed controller can practically circumvent high-order derivatives in implementation while providing
robust tracking, a capability that conventional methods for flexible joint robots often face challenges due to
the inherent nature of their high-order dynamics. The input-to-state stability of IMPC in a local region around
the reachable reference trajectory is theoretically proven, and the high approximation accuracy of the resulting
incremental system is analyzed. Finally, a series of experiments is conducted on a flexible-joint robot to verify
the practical effectiveness of IMPC, and superior performance in terms of high accuracy, high computational
efficiency, and constraint admissibility is demonstrated.
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This article proposes an optimization-based method for robust yet efficient control of flexible-joint robots by
using the model predictive control approach. The time-delay estimation (TDE) technique is used to approximate
uncertain and nonlinear dynamic equations, where neither concrete knowledge of mathematical system model
parameters is required in the approximation, thus granting the model-free property for dynamics compensation
and real-time system linearization. TDE is integrated wi...
»