Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems

• URL: http://arxiv.org/abs/2303.01628v2
• Date: Mon, 14 Aug 2023 04:55:01 GMT
• Title: Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems
• Authors: Weiqiao Han, Ashkan Jasour, Brian Williams
• Abstract summary: We design a state feedback controller that minimizes deviations of the states of the system from the nominal state trajectories due to uncertainties and disturbances. We use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems.
• Score: 9.088960941718
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we consider the closed-loop control problem of nonlinear robotic systems in the presence of probabilistic uncertainties and disturbances. More precisely, we design a state feedback controller that minimizes deviations of the states of the system from the nominal state trajectories due to uncertainties and disturbances. Existing approaches to address the control problem of probabilistic systems are limited to particular classes of uncertainties and systems such as Gaussian uncertainties and processes and linearized systems. We present an approach that deals with nonlinear dynamics models and arbitrary known probabilistic uncertainties. We formulate the controller design problem as an optimization problem in terms of statistics of the probability distributions including moments and characteristic functions. In particular, in the provided optimization problem, we use moments and characteristic functions to propagate uncertainties throughout the nonlinear motion model of robotic systems. In order to reduce the tracking deviations, we minimize the uncertainty of the probabilistic states around the nominal trajectory by minimizing the trace and the determinant of the covariance matrix of the probabilistic states. To obtain the state feedback gains, we solve deterministic optimization problems in terms of moments, characteristic functions, and state feedback gains using off-the-shelf interior-point optimization solvers. To illustrate the performance of the proposed method, we compare our method with existing probabilistic control methods.

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