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Optimal input shape terminal iterative learning control for robust backlash mitigation in EV powertrains
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DOI：10.1016/j.conengprac.2026.107123.png)
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Abstract
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• Proposal of a low-complexity backlash mitigation strategy based on input-shaped Terminal Iterative Learning Control (TILC) for EV powertrains. • Elimination of explicit backlash state estimation by learning the switching time of a slope-limited bang-bang torque. profile • Development of a model-based compensation scheme to handle iteration-varying disturbances and sampling-time. effects • Experimental validation on a drivetrain testbench demonstrating improved backlash reduction and drivability performance.
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