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Control Contraction Metrics: Convex and Intrinsic Criteria for Nonlinear Feedback Design

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arxiv 1503.03144 v3 pith:W543HF4M submitted 2015-03-11 cs.SY cs.SYmath.OC

classification cs.SYmath.OC
keywords controlnonlinearconditionscontractionconvexcriteriaderivedesign
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We introduce the concept of a control contraction metric, extending contraction analysis to constructive nonlinear control design. We derive sufficient conditions for exponential stabilizability of all trajectories of a nonlinear control system. The conditions have a simple geometrical interpretation, can be written as a convex feasibility problem, and are invariant under coordinate changes. We show that these conditions are necessary and sufficient for feedback linearizable systems, and also derive novel convex criteria for exponential stabilization of a nonlinear submanifold of state space. We illustrate the benefits of convexity by constructing a controller for an unstable polynomial system that combines local optimality and global stability, using a metric found via sum-of-squares programming.

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  1. Stochastic Multiple Shooting Trajectory Optimization via Sequential Local Policy Evaluation

    cs.RO 2026-08 conditional novelty 6.0 of 10

    A stochastic multiple-shooting optimizer that links short sampled control segments with local LQR feedback policies reaches terminal sets with fewer rollouts than MPPI and CEM on cartpole and VTOL landing benchmarks.

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