4,618 research outputs found
Nonlinear Model Predictive Control for Constrained Output Path Following
We consider the tracking of geometric paths in output spaces of nonlinear
systems subject to input and state constraints without pre-specified timing
requirements. Such problems are commonly referred to as constrained output
path-following problems. Specifically, we propose a predictive control approach
to constrained path-following problems with and without velocity assignments
and provide sufficient convergence conditions based on terminal regions and end
penalties. Furthermore, we analyze the geometric nature of constrained output
path-following problems and thereby provide insight into the computation of
suitable terminal control laws and terminal regions. We draw upon an example
from robotics to illustrate our findings.Comment: 12 pages, 4 figure
Perception-aware time optimal path parameterization for quadrotors
The increasing popularity of quadrotors has given rise to a class of
predominantly vision-driven vehicles. This paper addresses the problem of
perception-aware time optimal path parametrization for quadrotors. Although
many different choices of perceptual modalities are available, the low weight
and power budgets of quadrotor systems makes a camera ideal for on-board
navigation and estimation algorithms. However, this does come with a set of
challenges. The limited field of view of the camera can restrict the visibility
of salient regions in the environment, which dictates the necessity to consider
perception and planning jointly. The main contribution of this paper is an
efficient time optimal path parametrization algorithm for quadrotors with
limited field of view constraints. We show in a simulation study that a
state-of-the-art controller can track planned trajectories, and we validate the
proposed algorithm on a quadrotor platform in experiments.Comment: Accepted to appear at ICRA 202
Linear Model Predictive Control under Continuous Path Constraints via Parallelized Primal-Dual Hybrid Gradient Algorithm
In this paper, we consider a Model Predictive Control(MPC) problem of a
continuous time linear time-invariant system under continuous time path
constraints on the states and the inputs. By leveraging the concept of
differential flatness, we can replace the differential equations governing the
system with linear mapping between the states, inputs and the flat outputs (and
their derivatives). The flat output is then parameterized by piecewise
polynomials and the model predictive control problem can be equivalently
transformed into an Semi-Definite Programming (SDP) problem via Sum-of-Squares
with guaranteed constraint satisfaction at every continuous time instant. We
further observe that the SDP problem contains a large number of small-size
semi-definite matrices as optimization variables, and thus a Primal-Dual Hybrid
Gradient (PDHF) algorithm, which can be efficiently parallelized, is developed
to accelerate the optimization procedure. Simulation on a quadruple-tank
process illustrates that our formulation can guarantee strict constraint
satisfaction, while the standard MPC controller based on discretized system may
violate the constraint in between a sampling period. On the other hand, we
should that the our parallelized PDHG algorithm can outperform commercial
solvers for problems with long planning horizon
Receding horizon control of vectored thrust flight experiment
Abstract:
The application of a constrained receding horizon control technique to stabilise an indoor vectored-thrust flight experiment, known as the Caltech ducted fan, is given. The receding horizon control problem is formulated as a constrained optimal control problem and solved in real time with an efficient, computational method that combines nonlinear control theory, B-spline basis functions, and nonlinear programming. Characteristic issues, including non-zero computational times, convergence properties, choice of horizon length and terminal cost are discussed. The study validates the applicability of real-time receding horizon control for constrained systems with fast dynamics
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