This book presents a class of novel, self-learning, optimal control schemes based on adaptive dynamic programming techniques, which quantitatively obtain the optimal control schemes of the systems. It analyzes the properties identified by the programming methods, including the convergence of the iterative value functions and the stability of the system under iterative control laws, helping to guarantee the effectiveness of the methods developed. When the system model is known, self-learning optimal control is designed on the basis of the system model; when the system model is not known, adaptive dynamic programming is implemented according to the system data, effectively making the performance of the system converge to the optimum. With various real-world examples to complement and substantiate the mathematical analysis, the book is a valuable guide for engineers, researchers, and students in control science and engineering.

Designed for one-semester introductory senior-or graduate-level course, the authors provide the student with an introduction of analysis techniques used in the design of nonlinear and optimal feedback control systems. There is special emphasis on the fundamental topics of stability, controllability, and optimality, and on the corresponding geometry associated with these topics. Each chapter contains several examples and a variety of exercises.

Vehicle formation control is one of important research topics in transportation. Control of uncertain nonlinear systems is one of fundamental problems in vehicle control. In this dissertation, we consider this fundamental control problem. Specially, we considered self-organizing based tracking control of uncertain nonaffine systems and optimal control of uncertain nonlinear systems. In tracking control of nonaffine systems, a self-organizing online approximation based controller is proposed to achieve a prespecified tracking accuracy, without using high-gain control nor large magnitude switching. For optimal control of uncertain nonlinear systems, we considered point-wise min-norm optimal control of uncertain nonlinear systems and approximately optimal control of uncertain nonlinear systems. In point-wise non-norm optimal control, optimal regulation and optimal tracking controllers were proposed with the aid of locally weighted learning observers. By introducing control Lyapunov functions and redefining the optimal criterions, analytic controllers were proposed and were optimal in the sense of min-norm. In approximately optimal control of uncertain nonlinear systems, adaptive optimal controllers were proposed with the aid of iterative approximation techniques and adaptive control. By iteratively learning, the difficulty of solving Hamilton-Jacobian-Bellman (HJB) equation is overcome. The proposed adaptive optimal algorithms can be applied to solve optimal control problem of a large class of nonlinear systems. To show effectiveness of the proposed controllers for above problems, simulations were done in computers.

This outstanding reference presents current, state-of-the-art research on importantproblems of finite-dimensional nonlinear optimal control and controllability theory. Itpresents an overview of a broad variety of new techniques useful in solving classicalcontrol theory problems.Written and edited by renowned mathematicians at the forefront of research in thisevolving field, Nonlinear Controllability and Optimal Control providesdetailed coverage of the construction of solutions of differential inclusions by means ofdirectionally continuous sections ... Lie algebraic conditions for local controllability... the use of the Campbell-Hausdorff series to derive properties of optimal trajectories... the Fuller phenomenon ... the theory of orbits ... and more.Containing more than 1,300 display equations, this exemplary, instructive reference is aninvaluable source for mathematical researchers and applied mathematicians, electrical andelectronics, aerospace, mechanical, control, systems, and computer engineers, and graduatestudents in these disciplines .

This volume discusses advances in applied nonlinear optimal control, comprising both theoretical analysis of the developed control methods and case studies about their use in robotics, mechatronics, electric power generation, power electronics, micro-electronics, biological systems, biomedical systems, financial systems and industrial production processes. The advantages of the nonlinear optimal control approaches which are developed here are that, by applying approximate linearization of the controlled systems’ state-space description, one can avoid the elaborated state variables transformations (diffeomorphisms) which are required by global linearization-based control methods. The book also applies the control input directly to the power unit of the controlled systems and not on an equivalent linearized description, thus avoiding the inverse transformations met in global linearization-based control methods and the potential appearance of singularity problems. The method adopted here also retains the known advantages of optimal control, that is, the best trade-off between accurate tracking of reference setpoints and moderate variations of the control inputs. The book’s findings on nonlinear optimal control are a substantial contribution to the areas of nonlinear control and complex dynamical systems, and will find use in several research and engineering disciplines and in practical applications.