As a solution to the above problem, a novel sliding mode Gaussian controller with state estimation (SMGC/SE) is proposed, whose gains dynamically vary with respect to the error signal.
Also, since it has been shown that the physiological dynamics of inhibitory control networks are best reflected by models where the race to the decision boundary has dynamic, nonlinear properties (Ratcliff and Frank, 2012), our model also includes a dynamic gain that dynamically accelerates the execution process as it approaches the time boundary (i.e., the deadline for making a response).
The global estimate includes the covariance (environment conditions) from all the local estimators in the derived global fusion equations in the adjustment of fusion gain for dynamically adjusting the tracking in the optimal estimate.
The gain varied dynamically with the stimulus at each luminance; rather than being a simple static nonlinearity, i.e. a curve, it covered large pear-shaped areas in these "input-output" maps.
Further, the control gains can dynamically self-adjust during the voltage regulation after a disturbance.
The adaptive gains are dynamically determined in a structural way instead of keeping them constants, which can guarantee system stability and parameter estimation convergence.
Steendam and Moeneclaey [20], Harvatin and Ziemer [21], and Tufvesson and Maseng [22] have demonstrated the impact of varying the sub-carrier bandwidth on the system performance in a time and frequency-selective channel (either in terms of interference power or in terms of BER), but do not discuss the gains from dynamically adjusting the bandwidth.
Stem cells and their xylem-side daughters dynamically gain organizer-cell gene expression as they mature towards xylem identity.
A characteristic and innovative feature of this observer is the mixed low/high-gain structure in combination with saturated state estimates and dynamically tuned gains and saturation levels.
We show that the error of the high gain observer can be dynamically bounded, and then we generate guaranteed bounds on this error through an interval observer.
