Abstract
This paper studies the online adaptive optimal
controller design for a class of nonlinear systems through a
novel policy iteration (PI) algorithm. By using the technique of
neural network linear differential inclusion (LDI) to linearize the
nonlinear terms in each iteration, the optimal law for controller
design can be solved through the relevant algebraic Riccati
equation (ARE) without using the system internal parameters.
Based on PI approach, the adaptive optimal control algorithm
is developed with the online linearization and the two-step
iteration, i.e., policy evaluation and policy improvement. The
convergence of the proposed PI algorithm is also proved. Finally,
two numerical examples are given to illustrate the effectiveness
and applicability of the proposed method.
controller design for a class of nonlinear systems through a
novel policy iteration (PI) algorithm. By using the technique of
neural network linear differential inclusion (LDI) to linearize the
nonlinear terms in each iteration, the optimal law for controller
design can be solved through the relevant algebraic Riccati
equation (ARE) without using the system internal parameters.
Based on PI approach, the adaptive optimal control algorithm
is developed with the online linearization and the two-step
iteration, i.e., policy evaluation and policy improvement. The
convergence of the proposed PI algorithm is also proved. Finally,
two numerical examples are given to illustrate the effectiveness
and applicability of the proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 549-558 |
| Journal | IEEE Transactions on NEural Networks and Learning Systems |
| Volume | 31 |
| Issue number | 2 |
| Early online date | 11 Apr 2019 |
| DOIs | |
| Publication status | Published - 11 Apr 2019 |
Keywords
- Nonlinear systems
- policy iteration (PI)
- algebraic Riccati equation (ARE)
- adaptive optimal control
- linear differential inclusion (LDI)