Structured model-following neuro-adaptive design for attitude maneuver of rigid bodies

A new structured model-following adaptive approach is presented in this paper to achieve large attitude maneuvers of rigid bodies. First, a nominal controller is designed using the dynamic inversion philosophy. Next, a neuro-adaptive design is proposed to augment the nominal design in order to assure robust performance in the presence of parameter inaccuracies as well as unknown constant external disturbances. The structured approach proposed in this paper (where kinematic and dynamic equations are handled separately), reduces the complexity of the controller structure. From simulation studies, this adaptive controller is found to be very effective in assuring robust performance.

[1]  Anthony J. Calise,et al.  Neural-Network Augmentation of Existing Linear Controllers , 2001 .

[2]  John Valasek,et al.  Fault-Tolerant Structured Adaptive Model Inversion Control , 2006 .

[3]  Bong Wie,et al.  Space Vehicle Dynamics and Control , 1998 .

[4]  M. Akella Rigid body attitude tracking without angular velocity feedback , 2000 .

[5]  Marcel J. Sidi,et al.  Spacecraft Dynamics and Control: A Practical Engineering Approach , 1997 .

[6]  Zhongwu Huang,et al.  Robust adaptive critic based neurocontrollers for helicopter with unmodeled uncertainties , 2001 .

[7]  Bong Wie,et al.  Quaternion feedback for spacecraft large angle maneuvers , 1985 .

[8]  Steven C. Chapra,et al.  Numerical Methods for Engineers , 1986 .

[9]  J. Wen,et al.  The attitude control problem , 1991 .

[10]  Radhakant Padhi,et al.  Model-following neuro-adaptive control design for non-square, non-affine nonlinear systems , 2007 .

[11]  Klaus H. Well,et al.  Attitude Control of a Reentry Vehicle with Internal Dynamics , 2002 .

[12]  Darren M. Dawson,et al.  Quaternion-Based Adaptive Attitude Tracking Controller Without Velocity Measurements , 2001 .

[13]  J. Cloutier State-dependent Riccati equation techniques: an overview , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[14]  M.H. Hassoun,et al.  Fundamentals of Artificial Neural Networks , 1996, Proceedings of the IEEE.

[15]  Rush D. Robinett,et al.  Nonlinear Adaptive Control of Spacecraft Maneuvers , 1997 .

[16]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[17]  J. Junkins,et al.  Stereographic Orientation Parameters for Attitude Dynamics: A Generalization of the Rodrigues Parameters , 1996 .

[18]  Piotr Kulczycki,et al.  Slew Maneuver Control for Spacecraft Equipped with Star Camera and Reaction Wheels , 2005 .

[19]  M. Akella,et al.  Globally stabilizing saturated attitude control in the presence of bounded unknown disturbances , 2005 .

[20]  R. Mehra,et al.  Robust Adaptive Variable Structure Control of Spacecraft Under Control Input Saturation , 2001 .

[21]  M. Shuster A survey of attitude representation , 1993 .

[22]  Christopher J. Damaren,et al.  Hardware emulation strategies for concurrent microsatellite hardware and software development , 2002 .

[23]  Dan Bugajski,et al.  Dynamic inversion: an evolving methodology for flight control design , 1994 .

[24]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[25]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[26]  Anthony J. Calise,et al.  Adaptive output feedback control of a class of non-linear systems using neural networks , 2001 .

[27]  Anthony J. Calise,et al.  Analysis of Adaptive Neural Networks for Helicopter Flight Control , 1997 .

[28]  Ivica Kostanic,et al.  Principles of Neurocomputing for Science and Engineering , 2000 .

[29]  Anthony J. Calise,et al.  Nonlinear flight control using neural networks , 1994 .