Multi-objective Trajectory Tracking Optimization for Robots with Elastic Joints

Ainhauser, Werner; Gerstmayr, Johannes; Giusti, Andrea

doi:10.1007/978-3-030-75259-0_27

Cited by 1 publication

(1 citation statement)

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“…Possible automatic tuning techniques for PID controllers are reviewed in [46]; however, these are not applicable to multi-input multi-output systems. Instead, genetic algorithms (GA) have shown promising results for the gain tuning of nonlinear controllers, as demonstrated by simulations in [47] for flight controllers and in [48]- [50] for robot controllers. Genetic algorithms are bio-inspired techniques, for which existing tools can be used, e.g., the Global Optimization Toolbox in MATLAB.…”

Section: Gain Tuning Using a Genetic Algorithmmentioning

confidence: 99%

Velocity Estimation of Robot Manipulators: An Experimental Comparison

Liu

Giusti

Althoff

2023

IEEE Open J. Control. Syst.

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Accurate velocity information is often essential to the control of robot manipulators, especially for precise tracking of fast trajectories. However, joint velocities are rarely directly measured and instead estimated to save costs. While many approaches have been proposed for the velocity estimation of robot joints, no comprehensive experimental evaluation exists, making it difficult to choose the appropriate method. This paper compares multiple estimation methods running on a six degrees-of-freedom manipulator. We evaluate: 1) the estimation error using a ground-truth signal, 2) the closed-loop tracking error, 3) convergence behavior, 4) sensor fault tolerance, 5) implementation and tuning effort. To ensure a fair comparison, we optimally tune the estimators using a genetic algorithm. All estimation methods have a similar estimation error and similar closed-loop tracking performance, except for the nonlinear high-gain observer, which is not accurate enough. Sliding-mode observers can provide a precise velocity estimation despite sensor faults.

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