Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates

• URL: http://arxiv.org/abs/2207.06362v1
• Date: Wed, 13 Jul 2022 17:10:47 GMT
• Title: Iterative Linear Quadratic Optimization for Nonlinear Control: Differentiable Programming Algorithmic Templates
• Authors: Vincent Roulet, Siddhartha Srinivasa, Maryam Fazel, Zaid Harchaoui
• Abstract summary: We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint. We derive the computational complexities of all algorithms in a differentiable programming framework and present sufficient optimality conditions. The algorithms are coded in a differentiable programming language in a publicly available package.
• Score: 9.711326718689495
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present the implementation of nonlinear control algorithms based on linear and quadratic approximations of the objective from a functional viewpoint. We present a gradient descent, a Gauss-Newton method, a Newton method, differential dynamic programming approaches with linear quadratic or quadratic approximations, various line-search strategies, and regularized variants of these algorithms. We derive the computational complexities of all algorithms in a differentiable programming framework and present sufficient optimality conditions. We compare the algorithms on several benchmarks, such as autonomous car racing using a bicycle model of a car. The algorithms are coded in a differentiable programming language in a publicly available package.

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