3 research outputs found
One algorithm for testing annulling of mixed trigonometric polynomial functions on boundary points
This paper considers one problem of modern automated
theorem provers. While there are many different numerical
methods for computation of real roots of polynomials, theorem
provers require higher level of assurances of result correctness.
In this paper is shown one method that circumvents limitations
of application of Sturm’s theorem for such tasks
Non-Gaussian Uncertainty Minimization Based Control of Stochastic Nonlinear Robotic Systems
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.Comment: International Conference on Intelligent Robots and Systems (IROS),
202