Receding horizon control has recently been used for regulating discrete-time Piecewise Affine (PWA) systems. One of the obstructions for implementation consists in guaranteeing closed-loop stability a priori. This is an issue that has only been addressed marginally in the literature. In this paper we present an extension of the terminal cost method for guaranteeing stability in receding horizon...

The paper describes a receding horizon control design framework for continuous-time stochastic nonlinear systems subject to probabilistic state constraints. The intention is to derive solutions that are implementable in real-time on currently available mobile processors. The approach consists of decomposing the problem into designing receding horizon reference paths based on the drift component...

In this paper, a receding horizon control strategy for a class of bilinear discrete-time systems with Markovian jumping parameters and constraints is investigated. Specifically, the stochastic jump system under consideration involves control and state multiplicative noise and partly unknown transition probabilities (TPs). The receding horizon formulation adopts an on-line optimization paradigm ...

We consider an approximation scheme for solving Markov Decision Processes (MDPs) with countable state space, finite action space, and bounded rewards that uses an approximate solution of a fixed finite-horizon sub-MDP of a given infinite-horizon MDP to create a stationary policy, which we call “approximate receding horizon control”. We first analyze the performance of the approximate receding h...

A torque tracking and parameter identification scheme using a receding horizon optimization is developed for an over-actuated spacecraft Reaction Wheel Array (RWA). Additional actuator motion, constrained to the null-space of the array, is provided in order to enhance persistence of excitation. Therefore, the system parameters are learned without inducing large perturbations to the controllable...

A robotic agent is tasked to explore an a priori unknown environment. The objective is to maximize the amount of information about the partially observable state. The problem is formulated as a partially observable Markov decision process (POMDP) with an informationtheoretic objective function, further approximated to a form suitable for robotic exploration. An open-loop approximation is applie...

The control of high-dimensional, continuous, non-linear dynamical systems is a key problem in reinforcement learning and control. Local, trajectory-based methods, using techniques such as Differential Dynamic Programming (DDP), are not directly subject to the curse of dimensionality, but generate only local controllers. In this paper,we introduce Receding Horizon DDP (RH-DDP), an extension to t...

The control of high-dimensional, continuous, non-linear dynamical systems is a key problem in reinforcement learning and control. Local, trajectory-based methods, using techniques such as Differential Dynamic Programming (DDP), are not directly subject to the curse of dimensionality, but generate only local controllers. In this paper,we introduce Receding Horizon DDP (RH-DDP), an extension to t...

We present a receding horizon method for controlling an autonomous vehicle that must satisfy a rich mission specification over service requests occurring at the regions of a partitioned environment. The overall mission specification consists of a temporal logic statement over a set of static, a priori known requests, a regular expression over a set of dynamic requests that can be sensed only lo...

This work is focused on a model predictive control (MPC) based approach for the regulation of systems whose actuators are subject to loss effectiveness. Such degradation process, evolution assumed be measurable and predictable, can lead breakdown whenever certain thresholds exceeded. Within this context, if problem defined with ad hoc operational constraints and/or running costs, intrinsic MPC ...