New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use.

Design of a Two-Phase buck converter with fourth-order output filter for envelope amplifiers of limited bandwidth

The use of techniques such as envelope tracking (ET) and envelope elimination and restoration (EER) can improve the efficiency of radio frequency power amplifiers (RFPA). In both cases, high-bandwidth DC/DC converters called envelope amplifiers (EA) are used to modulate the supply voltage of the RFPA. This paper addresses the analysis and design of a modified two-phase Buck converter optimized to operate as EA. The effects of multiphase operation on the tracking capabilities are analyzed. The use of a fourth-order output filter is proposed to increase the attenuation of the harmonics generated by the PWM operation, thus allowing a reduction of the ratio between the switching frequency and the converter bandwidth. The design of the output filter is addressed considering envelope tracking accuracy and distortion caused by the side bands arising from the nonlinear modulation process. Finally, the proposed analysis and design methods are supported by simulation results, as well as demonstrated by experiments obtained using two 100-W, 10-MHz, two-phase Buck EAs capable of accurately tracking a 1.5-MHz bandwidth OFDM signal.

Compensation of biased excitation effects for MLS-based nonlinear systems' identification

MLS-based identification of nonlinear systems is largely affected by deviations in the excitation signal amenable to the combined effect of DC-offset and an arbitrary gain. These induce orthogonality loss in the MLS filter bank output, thus invalidating the underlying identification construction. In this paper we present a correction algorithm to derive the corrected Volterra kernels from the biased estimations provided by the standard MLS-based procedure.