Alan Royce Gabriel
Neuromuscular Robotics

Four Linearizations at Every Muscle Equilibrium

Hill-type muscle models with switched activation dynamics and a force–velocity kink do not have one local linearization at equilibrium. They have four directional branches. This study derives the switches and measures how choosing the wrong branch affects prediction and closed-loop control.

StatusComputational study · 2026
MethodsHill-type muscle models, piecewise-linear systems, MPC
Hill-type muscle simulation: raising and lowering with measured-state relinearization. The dashed line shows the target; muscle opacity indicates activation. Motion is magnified 12× about 60°, with playback at 0.1× speed.

Why four branches?

At equilibrium, excitation equals activation and fibre velocity is zero. Those are precisely the switching surfaces for asymmetric activation dynamics and the force–velocity relation. Their two independent switches yield four directional Jacobians. The derivation checks the continuity conditions for piecewise-linear systems and gives closed-form slope ratios.

Modeled elbow flexion and extension strength compared with measurements from Buchanan and Amis
Model checks against published elbow-strength measurements. Agreement varies by muscle group and reference dataset; these checks do not validate the controller on hardware.

Prediction and control

At the representative posture, using the wrong branch produces errors of 14–75% of the small activating-step response. In the reported closed-loop comparison, a blended linearization overshoots a raising step eight to nine times as much as the naive, sign-test and relinearizing rules. Relinearizing at the measured state reduces lowering-step overshoot from 0.37° to 0.02°.

Beyond one operating point

The analysis extends to multiple flexors, parameter sets and an elastic tendon. Antagonist co-contraction can shrink the force–velocity kink toward cancellation. Delay experiments motivate a predictor that accounts for queued inputs; controller weighting and measurement noise affect which rule performs best.

Scope

The work combines a mathematical derivation with simulation and checks against published strength data. The relinearizing controller has not been validated on hardware. The reported overshoot ratios refer to the raising step under the study’s baseline settings, not every motion or tuning.

BiomechanicsHybrid SystemsModel-Predictive ControlNumerical Methods

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