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This enables the real-time adjustment of controller parameters during the course of simulations or experiments, thereby expediting the overall optimization process.
The first one is the transient performance and the steady performance of the system is guaranteed by a user-defined function rather than depending on repeated adjustment of controller parameters.
The use of emerging soft computing random search techniques such as particle swarm optimization (PSO) and genetic algorithm (GA) is also gaining acceptance as effective tools in dynamic on line tuning, self-regulating, and adjustment of controller to ensure that specific objective functions are optimized.
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Data-based control design methods most often consist of iterative adjustment of the controller's parameters towards the parameter values which minimize an H2 performance criterion.
A Lyapunov based adaptation mechanism is proposed for adjustment of the controller parameters.
A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters.
In addition, a real time estimation of the load is developed using an algebraic approach for online adjustment of the hybrid controller.
The control strategy is based on feedback linearization and adjustment of two linear controllers.
As we demonstrate below, these polarization artifacts could generally be suppressed beneath the noise floor with careful adjustment of these polarization controllers.
The enhancement of dynamic stability is based on the adjustment of the MTDC converters' controller setpoints to drive the system away from HB point and increase the damping of oscillatory modes.
Adjustment of the reference polarization controller can suppress such artifacts beneath the noise floor, as shown in Fig. 8. Figure 9 shows a representative B-scan of the anterior segment of a healthy human volunteer acquired with the coherence revival configuration.
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