| 000 | 02489 a2200289 4500 | ||
|---|---|---|---|
| 001 | 1351831801 | ||
| 005 | 20250317111605.0 | ||
| 008 | 250312042017xx 80 eng | ||
| 020 | _a9781351831802 | ||
| 037 |
_bTaylor & Francis _cGBP 81.99 _fBB |
||
| 040 | _a01 | ||
| 041 | _aeng | ||
| 072 | 7 |
_aTJF _2thema |
|
| 072 | 7 |
_aTHY _2thema |
|
| 072 | 7 |
_aTJF _2bic |
|
| 072 | 7 |
_aTHRB _2bic |
|
| 072 | 7 |
_aTEC007000 _2bisac |
|
| 072 | 7 |
_aTEC008000 _2bisac |
|
| 072 | 7 |
_a003.83 _2bisac |
|
| 100 | 1 | _aEdgar N. Sanchez | |
| 245 | 1 | 0 | _aDiscrete-Time Inverse Optimal Control for Nonlinear Systems |
| 250 | _a1 | ||
| 260 |
_bCRC Press _c20171219 |
||
| 300 | _a268 p | ||
| 520 | _bDiscrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). The synthesized discrete-time optimal controller can be directly implemented in real-time systems. The book also proposes the use of recurrent neural networks to model discrete-time nonlinear systems. Combined with the inverse optimal control approach, such models constitute a powerful tool to deal with uncertainties such as unmodeled dynamics and disturbances. Learn from Simulations and an In-Depth Case Study The authors include a variety of simulations to illustrate the effectiveness of the synthesized controllers for stabilization and trajectory tracking of discrete-time nonlinear systems. An in-depth case study applies the control schemes to glycemic control in patients with type 1 diabetes mellitus, to calculate the adequate insulin delivery rate required to prevent hyperglycemia and hypoglycemia levels. The discrete-time optimal and robust control techniques proposed can be used in a range of industrial applications, from aerospace and energy to biomedical and electromechanical systems. Highlighting optimal and efficient control algorithms, this is a valuable resource for researchers, engineers, and students working in nonlinear system control. | ||
| 700 | 1 |
_aFernando Ornelas-Tellez _4A01 |
|
| 999 |
_c4473 _d4473 |
||