Decentralized Model Predictive Control Algorithm Research Articles

Nonlinear Decentralized Model Predictive Control for Unmanned Vehicles Moving in Formation

Unmanned vehicles operating in formation may perform more complex tasks than vehicles working indi- vidually. In order to control a formation of unmanned vehicles, however, the following main issues must be faced: vehicle motion is usually described by nonlinear models, feasible control actions for each vehicle are constrained, collision between the members of the formation must be avoided while, at the same time, the computational efforts must be kept low due to limitations on the onboard hardware. To solve these problems, a nonlinear decentralized model predictive control algorithm is presented in this paper. The adopted model is based on the nonlinear kinematic equations describing the motion of a body with six degrees of freedom, where each vehicle shares information with its leader only by means of a wireless local area network. Saturation and collision-free constraints are included within the formulation of the optimization problem, while de- centralization allows to distribute the computational efforts amongst all the vehicles of the formation. In order to show the effectiveness of the proposed approach, it has been applied to a formation of quadrotor vehicles. Simulation results prove that the approach presented in this paper is a valid way to solve the problem of controlling a formation of unmanned vehicles, granting at the same time the possibility to deal with constraints and nonlinearity while limiting the computational efforts through decentralization. DOI: http://dx.doi.org/10.5755/j01.itc.44.1.7219

Nonlinear Decentralized Model Predictive Control Strategy for a Formation of Unmanned Aerial Vehicles

Aerial vehicles flying in formation may perform more complex tasks than vehicles flying independently. Formation control, however, is a difficult problem to cope with for these kind of vehicles, since they are highly nonlinear, underactuated and the feasible control actions are constrained. Moreover the computational capabilities which can be mounted on aerial vehicles are usually limited, thus centralized solutions should be avoided. To solve these problems, a Nonlinear Decentralized Model Predictive Control algorithm is presented in this paper, taking into account both physical and actuation constraints. Each vehicle is equipped with sensors providing inertial measurements and communicates with its neighbours using a Wireless Local Area Network. Simulation results applied to a formation of quadrotor vehicles show that the proposed technique is a valid way to solve the control problem for a generic formation, granting at the same time the possibility to deal with constraints.

An iterative decentralized MPC algorithm for large-scale nonlinear systems

This paper presents a robust stabilizing decentralized model predictive control (MPC) algorithm for discrete-time nonlinear large-scale systems comprised of multiple subsystems. Each subsystem is controlled with a tube-based model predictive control algorithm that renders the subsystem input-to-state stable (ISS) with respect to both the external disturbances affecting it and the interconnections to the other subsystems (treated as additional disturbance inputs). The algorithm entails redesigning the MPC control strategy periodically, taking into account newly acquired information regarding the interconnection uncertainty sets. By using a regional version of the small-gain theorem for multiple nonlinear systems, we then show that the overall closed-loop system is ISS with respect to the external disturbances.