This letter describes the first derivatives estimation of nonlinear parameters through an embedded identifier in the hybrid system by using a feed-forward neural network (FFNN). The hybrid systems are modelled by the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is used to identify the full dynamics of the hybrid system. Moreover, the partial derivatives of an objective function J with respect to the parameters are estimated by the proposed identifier. Then, it is applied for the identification and estimation of the non-smooth nonlinear dynamic behaviors due to a saturation limiter in a practical engineering system.

This paper presents a wordline gating logic for reducing unnecessary BTB accesses. Partial bit of the branch predictor was simultaneously recorded in the middle of BTB to prevent further SRAM operation. Experimental results with embedded applications showed that the proposed mechanism reduces around 38% of BTB power consumption.

This paper presents a cache way enabling mechanism using branch target addresses. This mechanism uses branch prediction information to avoid the power consumption due to unnecessary cache way access by enabling only the cache way(s) that should be accessed. The proposed cache way enabling mechanism reduces the power consumption of the instruction cache by 63% without any performance degradation of the processor. An ARM1136 processor simulator and the Synopsys PrimeTime are used to perform the performance/power simulation and static timing analysis of the proposed mechanisms respectively.

In this paper, the interpolation line search (ILS) algorithm to find the desirable step length in a numerical optimization method is investigated to determine the optimal saturation limits with non-smooth nonlinearities. The simple steepest descent algorithm is used to illustrate that the ILS algorithm can provide adequate reductions in an objective function at minimal cost with fast convergence. The power system stabilizer (PSS) with output limits is used as an example for a nonlinear controller to be tuned. The efficient computation to implement the ILS algorithm in the steepest descent method is available by using the hybrid system model with the differential-algebraic-impulsive-switched (DAIS) structure. The simulation results are given to show the performance improved by the ILS algorithm.

This paper presents a study to estimate the composition of an electric load, i.e. to determine the amount of each load class by the direct measurements of the total electric current waveform from instrument reading. Kalman filter algorithm is applied to estimate the electric load composition on a consumer side of a distributed power system. The electric load supplied from the different voltage level by using a non-ideal delta-wye transformer is also studied with consideration of the practical environment for a distributed power system.

The output limits of the power system stabilizer (PSS) can improve the system damping performance immediately following a large disturbance. Due to non-smooth nonlinearities from the saturation limits, these values cannot be determined by the conventional tuning methods based on linear analysis. Only ad hoc tuning procedures have been used. A nonlinear least squares method, which is the Gauss-Newton optimization algorithm, is used in this paper. The gradient required in the Gauss-Newton method can be computed by applying trajectory sensitivities from the hybrid system model with the differential-algebraic-impulsive-switched (DAIS) structure. The optimal output limits of the PSS tuned by the proposed method are evaluated by time-domain simulation in a multi-machine power system (MMPS).