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The SOA yields off-line, nominal system parameters.
The first can be denoted as the Lyapunov-based controller for the nominal system.
Firstly, the nearly optimal controller design for the constrained nominal system is considered.
The optimal controller based on the linear quadratic regulator method is designed for the nominal system.
The estimated uncertainty is used to robustify the predictive controller designed for the nominal system.
Then, the authors construct an observer and controller to make the nominal system globally asymptotically stable.
Firstly, we formulate the optimal regulation control problem for the nominal system, and then, the robust controller for the original uncertain system is designed by adding a constant feedback gain to the optimal controller of the nominal system.
First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesized offline for optimal regulation of the nominal system.
With the proposed method the desired control input is approximated by an inverse fuzzy model of the nominal system.
Considering a second order uncertain plant, a controller is designed for nominal system to meet desired performance specifications.
We first design an output feedback stabilizer to globally stabilize the nominal system without the perturbing nonlinearities.
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