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The stator winding structure of the main exciter of the brushless synchronous starter/generator (BSSG) is changed at high speed for flux weakening, leading to a sudden excitation decrease of the main generator (MG) of BSSG. Consequently, when proportional-integral (PI) current loop-based control strategies are applied, large current overshoots will occur due to the lack of fast current control ability. This paper adopts model predictive current control (MPCC) to replace PI current control for a better dynamic performance. Besides, a parameter estimation scheme, based on the recursive least square (RLS) algorithm, is proposed to provide MG parameters for MPCC. An effectiveness analysis of the RLS algorithm is performed and two rules are given. According to the two rules, rather than the parameter dynamic characteristics adopted by traditional methods, three MG parameters, dq-axes inductances, and excitation flux linkage are estimated with two decoupled RLS estimators. In addition, a method is proposed to adjust the forgetting factor to track fast changing parameters of MG when the excitation changes suddenly. With the estimated parameters, MPCC is conducted to start BSSG. Experimental results show that fast current control of MG is achieved and large current overshoots caused by the sudden excitation change are avoided. [ABSTRACT FROM AUTHOR]

This paper presents two sets of estimator designs for distributed sensor networks in multitarget tracking under signal transmission faults due to the uncertain environments. A two-target tracking sensor network model is then established. Based on the tracking error systems, new augmented systems are proposed. It is proved that, using these systems, one can construct two different sets of estimators. The first estimation approach is to construct a fault estimator for the signal transmission faults occurred in the process of the two-target tracking in distributed sensor networks. The second estimation approach is to design a state/fault estimator. Alternatively, the second approach is proposed to design an estimator for both fault and state simultaneously in sensor networks tracking. Furthermore, the work can be extended to multitarget tracking problems in a more general setting. Finally, two application examples are used to illustrate the effectiveness of the theoretical results. [ABSTRACT FROM PUBLISHER]

Various functional candidates such as flux, back electromotive force, and reactive power are used to form a model reference adaptive system (MRAS) to estimate the speed of an induction motor drive. Of these, reactive power ( $Q$) based controllers perform well at low speeds and are inherently independent of stator resistance. However, such configuration fails to provide stability in the regenerative mode. This paper proposes a new formulation of reactive-power-based MRAS that is stable in all the four quadrants of operation. A detailed MATLAB/Simulink based simulation study is presented. Superior performance of the proposed controller is confirmed by prototype experimentation using a field-programmable-gate-array based controller in the laboratory. [ABSTRACT FROM PUBLISHER]

Many control schemes rely on an analytical model of the servomechanism to be controlled, and hence, accurate knowledge about the position- and time-dependent parameter variability becomes crucial in many contexts, such as robust control methods. Although a properly designed robust controller can cope with a large parameter variation, real-time identification of the system parameter behavior could lead to several advantages by means of monitoring the varying dynamics, e.g., the predetermined uncertainty region around the nominal value. This paper addresses issues in online parameter estimation of a linear tooth belt drive with a limited stroke. Particular attention is paid to detecting the position-dependent changes in the system dynamics by using recursive least squares algorithm and exciting the system in different cart positions in order to identify the varying dynamics. The algorithm used is based on an indirect output-error identification scheme. The experimentally estimated parameters are compared with the corresponding two-mass model parameters. The results show an acceptable agreement and demonstrate the feasibility of the estimation method to estimate the parameters of a closed-loop controlled servomechanism with a limited stroke and time-varying parameters. [ABSTRACT FROM PUBLISHER]

A state-space electromyography (EMG) model is developed for continuous motion estimation of human limb in this paper. While the general Hill-based muscle model (HMM) estimates only joint torque from EMG signals in an “open-loop” form, we integrate the forward dynamics of human joint movement into the HMM, and such an extended HMM can be used to estimate the joint motion states directly. EMG features are developed to construct measurement equations for the extended HMM to form a state-space model. With the state-space HMM, a normal closed-loop prediction–correction approach such as the Kalman-type algorithm can be used to estimate the continuous joint movement from EMG signals, where the measurement equation is used to reject model uncertainties and external disturbances. Moreover, we propose a new normalization approach for EMG signals for the purpose of rejecting the dependence of the motion estimation on varying external loads. Comprehensive experiments are conducted on the human elbow joint, and the improvements of the proposed methods are verified by the comparison of the EMG-based estimation and the inertial measurement unit measurements. [ABSTRACT FROM PUBLISHER]