Neural network architecture for trajectory generation and control of automated car parking

This paper describes the development of a control system to support an automated parking mode in driving passenger cars. By using recent advances in the artificial neural network technology and a combination of linear feedback and nonlinear feedforward control, we propose a novel architecture for the parking motion controller. The paper presents the results of the controller design and analysis, including parking problem analysis, stability analysis for the feedback controller, formulation and optimal solution of the parking trajectory planning problem, and design of a parking motion planning architecture based on a radial basis function network. Two general cases of backward parking considered in this work are emulated using the proposed controller. The emulation results reveal high efficiency of the presented approach and demonstrate that the proposed system can be implemented on a typical passenger car.

[1]  R. V. Dooren,et al.  A Chebyshev technique for solving nonlinear optimal control problems , 1988 .

[2]  B. Widrow,et al.  Neural networks for self-learning control systems , 1990, IEEE Control Systems Magazine.

[3]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.

[4]  S. Sastry,et al.  Nonholonomic motion planning: steering using sinusoids , 1993, IEEE Trans. Autom. Control..

[5]  A. Bloch,et al.  Control and stabilization of nonholonomic dynamic systems , 1992 .

[6]  D.M. Gorinevsky.  Learning and approximation in database for feedforward control of flexible manipulator , 1991, Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments.

[7]  Andrew A. Goldenberg,et al.  Radial basis function network architecture for nonholonomic motion planning and control of free-flying manipulators , 1996, IEEE Trans. Robotics Autom..

[8]  Stephen A. Billings,et al.  Please Scroll down for Article , 1992 .

[9]  A. Goldenberg,et al.  Design of smooth dynamic feedback for stabilization of nonholonomic systems , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[10]  David S. Broomhead,et al.  Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..

[11]  C. Micchelli.  Interpolation of scattered data: Distance matrices and conditionally positive definite functions , 1986 .

[12]  Thomas H. Connolly,et al.  Comparison of Some Neural Network and Scattered Data Approximations: The Inverse Manipulator Kinematics Example , 1994, Neural Computation.

[13]  F. Girosi,et al.  Networks for approximation and learning , 1990, Proc. IEEE.

[14]  Karim Ait-Abderrahim,et al.  Feedback stabilization of a nonholonomic wheeled mobile robot , 1991, Proceedings IROS '91:IEEE/RSJ International Workshop on Intelligent Robots and Systems '91.

[15]  S. Sastry,et al.  Stabilization of trajectories for systems with nonholonomic constraints , 1994 .