This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.

Three-phase current reconstruction techniques with a single current sensor have been studied for permanent magnet synchronous machines (PMSMs) drive system due to its reducing cost and volume. In order to minimize the current reconstruction dead zone in the conventional method with a single dc-link current sensor, a strategy to couple multiple available positions through the single current sensor is proposed in this paper. With the proposed method, two independent equations of the stator currents can be obtained by sampling the single current sensor twice in both zero voltage vectors and active voltage vectors. By solving these two equations, the phase current reconstruction can be achieved accurately, which moves the current reconstruction dead zone toward six corner area of the space vector hexagon. Meanwhile, in order to guarantee the proposed method suitable in the whole linear modulation region, the suitable selection of the pulsewidth modulation switching period is analyzed in detail. Furthermore, the optimal selections of multiple sampling positions are discussed to lower the actual implementation difficulty of the proposed method. Finally, the experimental results on the PMSM demonstrate that the reconstructed phase currents with the proposed method can be always in consistent with the actual measured currents under different working conditions.

A novel control scheme is proposed in this paper for the sensorless control of interior permanent magnet synchronous machines (IPMSM) with the automatic tracking of the maximum-torque-per-ampere (MTPA) trajectory. Differing from the conventional method that the estimation of the rotor position and the tracking of MTPA trajectory are two separate processes, the proposed control scheme can achieve sensorless MTPA control of IPMSMs in one step by defining and constructing a proper virtual q-axis inductance to directly calculate the current space vector angle along the MTPA trajectory rather than the rotor position, which greatly simplifies the whole implementation process. Meanwhile, considering that the virtual q-axis inductance is changing with the machine parameters and the applied load, a simple signal processing technique based on the virtual high frequency signal injection is developed for the real-time and accurate acquisition of the virtual q-axis inductance. Furthermore, in order to ensure the reliability and stability of the proposed control scheme, the effective range of the virtual q-axis inductance is analyzed in detail. Finally, the experimental results on an IPMSM demonstrate the proposed control scheme can achieve the high-performance sensorless control with accurate MTPA self-tracking operation.

Flux linkage estimation based on the voltage model has been widely employed in surface-mounted permanent magnet synchronous machine (SPMSM) sensorless drives due to its simple implementation. To deal with dc-offset and dc-drift problems, the pure integrator is modified as a programmable low pass filter (LPF)in practice. However, the estimation accuracy of the rotor position is affected by the adopted filter. Although the position deviation is compensated in steady-state based on the frequency characteristics of the filter, the compensation effect deteriorates during a dynamic process which severely degrades the sensorless control performance. In order to effectively tackle this problem, a novel compensation strategy based on the linear extended state observer (LESO) is proposed in this paper. With the constructed LESO in the estimated synchronous rotating reference frame, the position estimation error can be detected and compensated both in the steady and dynamic state. Moreover, the observer is enhanced to improve the estimation accuracy in linear acceleration and deceleration. Finally, the feasibility of the proposed method is verified under various operating conditions with a 5.5 kW SPMSM drive system.

This paper aims to improve the sensorless control performance of surface-mounted permanent magnet synchronous machine (SPMSM) drives in the whole speed range. Based on the thought of speed partition optimization and smooth transition, a hybrid sensorless control strategy integrating with an enhanced flux linkage observer and the current-frequency (I-f) starting method is proposed. At zero-low speed, the I-f control method is utilized for the stable startup and strong anti-jamming capability. At medium-high speed, an enhanced flux linkage observer based on the sliding-mode compensator is designed for the closed-loop sensorless operation. Taking advantage of the sliding-mode compensator, not only the position estimation errors caused by the adopted filter can be compensated effectively without any speed iteration to improve the system stability, but also the suitable cutoff frequency of the filter can be selected for the fast system dynamic response. Meanwhile, the influence of machine parameters variations on the rotor position estimation accuracy is discussed. Furthermore, in order to achieve the smooth operation between two different control schemes, an adaptive transition algorithm is described in detail. Experimental results indicate that the proposed hybrid sensorless control strategy can show an excellent performance for both steady-state and dynamic operations during full speed range.

This paper proposes a hybrid strategy by combining fundamental pulsewidth modulation excitation (FPE) and additional test vectors, which is used in interior permanent-magnet synchronous motor (IPMSM) sensorless control. In contrast to the typical indirect flux detection by on-line reactance measurement-based methodology where the rotor position is calculated and updated in every three successive pulsewidth modulation (PWM) cycles, the proposed hybrid strategy can achieve the one-cycle acquisition of the rotor position by utilizing both response currents of FPE and additional test vectors, which makes the proposed strategy more robust and suitable in higher speed or load sudden change conditions. Meanwhile, a new PWM scheme for four-space-vector PWM (FSVPWM) is presented, which sharply simplifies the implementation process. Furthermore, for the case that the amplitude of the object fundamental voltage is so small that it cannot satisfy minimum time demand for the slopes of response currents sampling, a compensation technique is presented. In addition, the multiple-point current sampling (MPCS) technology and linear least square fitting method are used to decrease the current sampling disturbance, which improves obviously the accuracy of position estimation. Finally, the experimental results confirm that the proposed strategy can obtain the rotor position accurately and simply.