Abstract
This article offers new insights on the learning control approach developed by [Hu et al. IEEE/ASME Trans. Mechatronics, 19(1): 191–200, 2014]. Theoretical insights are further proposed to unveil why the contraction-type iterative learning control (ILC) schemes are suitable and effective in compensating for hysteresis, widely existing in biorobotic locomotion. Under such circumstances, iteration-based second-order dynamics is adopted to describe the biorobotic systems acted upon by one unknown Preisach hysteresis term. The memory clearing operator is mathematically proven to enable feasibility of contraction-type ILC methods, regardless of whether the initial state is accurately set or not. The simulation examples confirm that the developed iteration-based controller combined with a preceded operator effectively reduce tracking errors caused by the hysteresis nonlinearity. Furthermore, the new insights on theoretical feasibility are definitively corroborated in accordance with the previously published experimental results.
Highlights
1.1 Overview of biorobotic systemsBiorobotics, or biologically inspired robotics, integrates the interests of both biologists and roboticists
+ hk (t) = uk (t) yk (t) = qk (t) where t denotes the time and the nonnegative subscript k denotes the operation or iteration number; θ(t) denotes the biorobotic joint angle; y(t) the system output; u(t) the control input containing the torques or forces to be applied at the biorobotic joint; M(θ) the inertia matrix; C(θ, θ ) a coefficient resulting from Coriolis and centrifugal forces; G(θ) the dynamic term resulting from gravitational forces and h(t) the hysteresis nonlinearity in the biorobotic joints
It was mathematical‐ ly proven that the memory clearing operator enables the hysteretic term satisfying the Lipschitz condition
Summary
Biorobotics, or biologically inspired robotics, integrates the interests of both biologists and roboticists. Biorobotics is increasingly contributing to scientific understanding of, and inspiration from, biological princi‐ ples. Bio-inspirations are used to develop robotic systems that satisfy the practical requirements of stability, manoeu‐ vrability, autonomy and endurance. Biorobots have served as important scientific tools in the investigation of animal locomotion, when used as physical models. Biorobots are employed in the testing of hypotheses since they are qualified for repeatable and parameterized experiments [1]. When biorobots are employed in investigation of animal locomotion, the question arises as to whether and to what extent the developed robotic systems can replicate animal movement and behaviours. Inconsistency between the desired and actual performance is comprehensive in bio-inspired robotic locomotion.
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