Discrete-time inverse optimal control for nonlinear systems
Discrete-time systems Kalman filtering Neural networks (Computer science) Nonlinear control theory
CRC Press, Taylor & Francis Group
2013
EISBN 9781466580886
ch. 1. Introduction.
ch. 2. Mathematical preliminaries.
ch. 3. Inverse optimal control : a passivity approach.
ch. 4. Inverse optimal control : a CLF approach, part I.
ch. 5. Inverse optimal control : a CLF approach, part II.
ch. 6. Neural inverse optimal control.
ch. 7. Glycemic control of type 1 diabetes mellitus patients.
ch. 8. Conclusions.
"This book presents a novel inverse optimal control approach for stabilization and trajectory tracking of discrete-time nonlinear systems, avoiding the need to solve the associated Hamilton-Jacobi-Bellman equation, and minimizing a cost functional, resulting in efficient controllers. Additionally, the book proposes the use of recurrent neural networks as a tool to model discrete-time nonlinear systems; such models combined with the inverse optimal control constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Different simulations illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems"--
ch. 2. Mathematical preliminaries.
ch. 3. Inverse optimal control : a passivity approach.
ch. 4. Inverse optimal control : a CLF approach, part I.
ch. 5. Inverse optimal control : a CLF approach, part II.
ch. 6. Neural inverse optimal control.
ch. 7. Glycemic control of type 1 diabetes mellitus patients.
ch. 8. Conclusions.
"This book presents a novel inverse optimal control approach for stabilization and trajectory tracking of discrete-time nonlinear systems, avoiding the need to solve the associated Hamilton-Jacobi-Bellman equation, and minimizing a cost functional, resulting in efficient controllers. Additionally, the book proposes the use of recurrent neural networks as a tool to model discrete-time nonlinear systems; such models combined with the inverse optimal control constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Different simulations illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems"--
