Model-based predictive control that highly depends on system parameters has been widely investigated. In contrast, model-free predictive current control (MFPCC) can be performed based on input or output measured data, rather than on any system model information. In such a strategy, the current gradients due to each of the possible voltage vectors are stored and used to predict future currents. Current gradient knowledge therefore provides a significant foundation for MFPCC. In conventional MFPCC, however, the stagnation existing in the current gradient update always impacts the reliability of current gradients and further worsens the control performance. In this paper, an improved MFPCC with an advanced current gradient updating mechanism is proposed. In the proposed strategy, to guarantee the reliability of the current gradients, two contiguous measured current gradients due to the applied voltage vectors are used to estimate the current gradients for all the possible voltage vectors. This simple method takes advantage of the mathematical relationships between the voltage vectors. In this way, all the current gradients can be obtained within one control period, effectively reducing the stagnation effect in conventional MFPCC. The proposed MFPCC scheme is evaluated on a permanent magnet synchronous machine (PMSM) drive setup to demonstrate its effectiveness

This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.

In this paper, a novel torque boundary-based model predictive torque control (MPTC) is proposed to improve the torque and flux performance in permanent magnet synchronous motor (PMSM) drives by straightforwardly reducing the corresponding ripples. For this purpose, an optimal sequential list of two voltage vectors is considered as candidate input and the time durations of the two vectors to be applied are determined based on a predefined torque ripple tolerance band. Considering the torque outcomes both at the switching instant and at the end of the control period, the torque can be limited within the upper and lower boundaries of the preset tolerance band during the whole control period. Meanwhile, by monitoring the number of defined valid vector sequences, the torque boundaries can be optimized online. As a result, the torque ripple can be restrained to a fairly small range, and the torque boundary design work is avoided. In addition, according to the predicted torque outcomes of the candidate inputs, a group of vector sequences can be excluded from the control set before optimization, reducing the computation cost. Moreover, due to the preset torque ripple tolerance, the weighting factor in the cost function is eliminated, thereby avoiding the corresponding tuning work. Experimental results are presented to reveal the effectiveness of the proposed strategy.

A series-connected 36-pulse rectifier with passive pulse-tripling circuit (PPTC) on the dc side is proposed in this article. The proposed rectifier consists of a series-connected 12-pulse rectifier and a PPTC. The PPTC includes a passive current forming network, a single-phase auxiliary transformer with dual secondary windings and two auxiliary single-phase rectifiers (ASRs). One ASR in PPTC is connected in series with the load to directly modulate the output current of rectifier bridges. The other ASR in PPTC is connected in parallel with the load to modulate the output current of rectifier bridges according to the relationship between its input voltage and load voltage. Composite modulation of the two ASRs increases the pulse number of the rectifier from 12 pulses to 36 pulses. The resulting rectifier obtains an approximate sinusoidal input current with less than 5% THD. Only a few low-capacity passive components are sufficient given the absence of active components in the proposed scheme. The proposed scheme presents advantages of simple circuit structure, easy implementation, and high reliability. Detailed analysis and experimental results are provided to verify the effectiveness of the proposed scheme.

Using the bilevel optimization (BIO) scheme, this paper presents a time-optimal path planner for autonomous underwater vehicles (AUVs) operating in grid-based environments with ocean currents. In this scheme, the upper optimization problem is defined as finding a free-collision channel from a starting point to a destination, which consists of connected grids, and the lower optimization problem is defined as finding an energy-optimal path in the channel generated by the upper level algorithm. The proposed scheme is integrated with ant colony algorithm as the upper level and quantum-behaved particle swarm optimization as the lower level and tested to find an energy-optimal path for AUV navigating through an ocean environment in the presence of obstacles. This arrangement prevents discrete state transitions that constrain a vehicle’s motion to a small set of headings and improves efficiency by the usage of evolutionary algorithms. Simulation results show that the proposed BIO scheme has higher computation efficiency with a slightly lower fitness value than sliding wavefront expansion scheme, which is a grid-based path planner with continuous motion directions.