1,869 research outputs found
A receding horizon generalization of pointwise min-norm controllers
Control Lyapunov functions (CLFs) are used in conjunction with receding horizon control to develop a new class of receding horizon control schemes. In the process, strong connections between the seemingly disparate approaches are revealed, leading to a unified picture that ties together the notions of pointwise min-norm, receding horizon, and optimal control. This framework is used to develop a CLF based receding horizon scheme, of which a special case provides an appropriate extension of Sontag's formula. The scheme is first presented as an idealized continuous-time receding horizon control law. The issue of implementation under discrete-time sampling is then discussed as a modification. These schemes are shown to possess a number of desirable theoretical and implementation properties. An example is provided, demonstrating their application to a nonlinear control problem. Finally, stronger connections to both optimal and pointwise min-norm control are proved
Unconstrained receding-horizon control of nonlinear systems
It is well known that unconstrained infinite-horizon optimal control may be used to construct a stabilizing controller for a nonlinear system. We show that similar stabilization results may be achieved using unconstrained finite horizon optimal control. The key idea is to approximate the tail of the infinite horizon cost-to-go using, as terminal cost, an appropriate control Lyapunov function. Roughly speaking, the terminal control Lyapunov function (CLF) should provide an (incremental) upper bound on the cost. In this fashion, important stability characteristics may be retained without the use of terminal constraints such as those employed by a number of other researchers. The absence of constraints allows a significant speedup in computation. Furthermore, it is shown that in order to guarantee stability, it suffices to satisfy an improvement property, thereby relaxing the requirement that truly optimal trajectories be found. We provide a complete analysis of the stability and region of attraction/operation properties of receding horizon control strategies that utilize finite horizon approximations in the proposed class. It is shown that the guaranteed region of operation contains that of the CLF controller and may be made as large as desired by increasing the optimization horizon (restricted, of course, to the infinite horizon domain). Moreover, it is easily seen that both CLF and infinite-horizon optimal control approaches are limiting cases of our receding horizon strategy. The key results are illustrated using a familiar example, the inverted pendulum, where significant improvements in guaranteed region of operation and cost are noted
Optimal Switching Synthesis for Jump Linear Systems with Gaussian initial state uncertainty
This paper provides a method to design an optimal switching sequence for jump
linear systems with given Gaussian initial state uncertainty. In the practical
perspective, the initial state contains some uncertainties that come from
measurement errors or sensor inaccuracies and we assume that the type of this
uncertainty has the form of Gaussian distribution. In order to cope with
Gaussian initial state uncertainty and to measure the system performance,
Wasserstein metric that defines the distance between probability density
functions is used. Combining with the receding horizon framework, an optimal
switching sequence for jump linear systems can be obtained by minimizing the
objective function that is expressed in terms of Wasserstein distance. The
proposed optimal switching synthesis also guarantees the mean square stability
for jump linear systems. The validations of the proposed methods are verified
by examples.Comment: ASME Dynamic Systems and Control Conference (DSCC), 201
State feedback policies for robust receding horizon control: uniqueness, continuity, and stability
Published versio
A Parallel Decomposition Scheme for Solving Long-Horizon Optimal Control Problems
We present a temporal decomposition scheme for solving long-horizon optimal
control problems. In the proposed scheme, the time domain is decomposed into a
set of subdomains with partially overlapping regions. Subproblems associated
with the subdomains are solved in parallel to obtain local primal-dual
trajectories that are assembled to obtain the global trajectories. We provide a
sufficient condition that guarantees convergence of the proposed scheme. This
condition states that the effect of perturbations on the boundary conditions
(i.e., initial state and terminal dual/adjoint variable) should decay
asymptotically as one moves away from the boundaries. This condition also
reveals that the scheme converges if the size of the overlap is sufficiently
large and that the convergence rate improves with the size of the overlap. We
prove that linear quadratic problems satisfy the asymptotic decay condition,
and we discuss numerical strategies to determine if the condition holds in more
general cases. We draw upon a non-convex optimal control problem to illustrate
the performance of the proposed scheme
An Optimization-Based Receding Horizon Trajectory Planning Algorithm
This paper presents an optimization-based receding horizon trajectory
planning algorithm for dynamical systems operating in unstructured and
cluttered environments. The proposed approach is a two-step procedure that uses
a motion planning algorithm in a first step to efficiently find a feasible, but
possibly suboptimal, nominal solution to the trajectory planning problem where
in particular the combinatorial aspects of the problem are solved. The
resulting nominal trajectory is then improved in a second optimization-based
receding horizon planning step which performs local trajectory refinement over
a sliding time window. In the second step, the nominal trajectory is used in a
novel way to both represent a terminal manifold and obtain an upper bound on
the cost-to-go online. This enables the possibility to provide theoretical
guarantees in terms of recursive feasibility, objective function value, and
convergence to the desired terminal state. The established theoretical
guarantees and the performance of the proposed algorithm are verified in a set
of challenging trajectory planning scenarios for a truck and trailer system.Comment: Submitted for IFAC World Congress 202
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