Autonomous helicopter formation using model predictive control

Formation ∞ight is the primary movement technique for teams of helicopters. However, the potential for accidents is greatly increased when helicopter teams are required to ∞y in tight formations and under harsh conditions. The starting point for safe autonomous ∞ight formations is to design a distributed control law attenuating external disturbances coming into a formation, so that each vehicle can safely maintain su‐cient space between it and all other vehicles. In order to avoid the conservative nature inherent in distributed MPC algorithms, we begin by designing a stable MPC for individual vehicles, and then introducing carefully designed inter-agent coupling terms in each performance index. The proposed algorithm works in a decentralized manner, and is applied to the problem of helicopter formations comprised of heterogenous vehicles. The disturbance attenuation property of the proposed MPC controller is validated throughout a series of computer simulations.

[1]  T. Kanade,et al.  System Identification Modeling of a Model-Scale Helicopter , 2000 .

[2]  Mark B. Milam,et al.  A new computational approach to real-time trajectory generation for constrained mechanical systems , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[3]  S. Shankar Sastry,et al.  Unmanned helicopter formation flight experiment for the study of mesh stability , 2007 .

[4]  Jie Yu,et al.  Unconstrained receding-horizon control of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[5]  T. Keviczky,et al.  Hierarchical design of decentralized receding horizon controllers for decoupled systems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[6]  Andrew R. Teel,et al.  Examples when nonlinear model predictive control is nonrobust , 2004, Autom..

[7]  David Q. Mayne,et al.  Constrained model predictive control: Stability and optimality , 2000, Autom..

[8]  Jos F. Sturm,et al.  A Matlab toolbox for optimization over symmetric cones , 1999 .

[9]  Stephen P. Boyd,et al.  Distributed optimization for cooperative agents: application to formation flight , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[10]  S. Shankar Sastry,et al.  Decentralized nonlinear model predictive control of multiple flying robots , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[11]  R. Nikoukhah,et al.  LMITOOL: a package for LMI optimization , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[12]  Peter J Seiler,et al.  Coordinated Control of Unmanned Aerial Vehicles , 2001 .

[13]  Mario Sznaier,et al.  Receding Horizon Control Lyapunov Function Approach to Suboptimal Regulation of Nonlinear Systems , 2000 .

[14]  Alberto Bemporad,et al.  Robust model predictive control: A survey , 1998, Robustness in Identification and Control.

[15]  Alan J. Laub,et al.  The LMI control toolbox , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[16]  Hyoun-Jin Kim Multiagent pursuit-evasion games: Algorithms and experiments , 2001 .

[17]  Ali Jadbabaie,et al.  Receding horizon control of nonlinear systems: A control Lyapunov function approach , 2001 .

[18]  Andrew R. Teel,et al.  Model predictive control: for want of a local control Lyapunov function, all is not lost , 2005, IEEE Transactions on Automatic Control.

[19]  O. V. Stryk,et al.  Numerical Solution of Optimal Control Problems by Direct Collocation , 1993 .

[20]  Eduardo Camponogara,et al.  Distributed model predictive control , 2002 .

[21]  S. Shankar Sastry,et al.  Autonomous formation flight of helicopters: model predictive control approach , 2006 .

[22]  S. Sastry,et al.  Autonomous Exploration in Unknown Urban Environments for Unmanned Aerial Vehicles , 2005 .

[23]  R. Bulirsch Optimal control : calculus of variations, optimal control theory and numerical methods , 1993 .

[24]  Aniruddha G. Pant Mesh stability of formations of unmanned aerial vehicles , 2002 .

[25]  H. ChenT,et al.  A Quasi-Infinite Horizon Nonlinear Model Predictive Control Scheme with Guaranteed Stability * , 1998 .

[26]  B. Fabien Some tools for the direct solution of optimal control problems , 1998 .

[27]  Phillip J. McKerrow,et al.  Introduction to robotics , 1991 .

[28]  Bernard Mettler,et al.  Identification Modeling and Characteristics of Miniature Rotorcraft , 2002 .

[29]  Robert R. Bitmead,et al.  Performance and Computational Implementation of Nonlinear Model Predictive Control on a Submarine , 2000 .

[30]  M. Kothare,et al.  Robust constrained model predictive control using linear matrix inequalities , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[31]  Bernard Mettler Development of the Identification Model , 2003 .

[32]  S. Shankar Sastry,et al.  HIERARCHICAL CONTROL SYSTEM SYNTHESIS FOR ROTORCRAFT-BASED UNMANNED AERIAL VEHICLES , 2000 .

[33]  William B. Dunbar,et al.  Distributed receding horizon control of multiagent systems , 2004 .

[34]  Frank Allgöwer,et al.  A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability , 1997, 1997 European Control Conference (ECC).