Computational intelligence methods for dynamic control of mobile robots

Abstract

The ever-increasing scale and complexity of modern machines and processes, coupled with a tightening of performance specifications, necessitates control systems with higher levels of intelligence. Towards this aim, the field of intelligent control strives to endow systems with the key abilities of adaptation, learning and autonomy, so that they operate successfully in complex and uncertain environments with minimal human intervention. Despite a substantial body of work that has accumulated in this field of research over the past two decades, there is still ample scope for development. In particular, there is a pressing need for intelligent schemes with enhanced functional adaptability for the control of multivariable nonlinear systems such as autonomous mobile robots. The work presented in this thesis is a step in this direction. More specifically, the first part of the thesis presents novel explicit dual adaptive neural network-based control schemes, for a general stochastic class of multivariable nonlinear systems. These schemes address issues of system complexity such as multivariable nonlinear dynamics, functional uncertainty, unpredictable external disturbances and measurement noise. In contrast to the majority of adaptive controllers, which rely on the certainty equivalence assumption, a dual adaptive scheme seeks to adapt to unknown situations as quickly as possible, and at the same time strives to minimize the errors it makes in the process by taking into consideration the uncertainty of its estimates. Few such controllers have ever been implemented and tested in practical applications, especially within the context of intelligent control, and to the best of the author's knowledge none for the motion control of mobile robots. For this reason, the second part of the thesis deals with the development of novel dual adaptive neuro-control schemes for the dynamic control of nonholonomic wheeled mobile robots with un known or uncertain dynamics. A comprehensive statistical comparative analysis, including Monte Carlo simulations and hypothesis testing, confirms that the proposed dual adaptive schemes are truly effective. In addition, the schemes proposed for mobile robots are also validated experimentally on a real robot designed and built by the author for the purpose of this research, thereby bridging the gap between theory and practice.