This paper introduces a particle swarm optimization (PSO) algorithm that uses experimental measurements for the identification of the direct ($d$ ) and quadrature ($q$ ) stator inductances and the stator resistance of permanent-magnet synchronous machines (PMSMs). Strategies for the identification of stator resistance and flux linkage of permanent magnets are also presented. The developed PSO algorithm has the advantages of fast and stable convergence characteristics, and it is relatively easy to implement; moreover, neither voltage source inverters nor closed-loop control strategies are needed. In this paper, the experimental results obtained by PSO are compared with other ones obtained through a current decay test to demonstrate the effectiveness of the presented method. A 32-bit C2000 real-time microcontroller and a commercial surface-mounted PMSM have been used to carry out the experimental measurements.

this paper introduces the comparison of four predictive torque control schemes for a permanent magnet synchronous machine (PMSM). The first method is the finite set model predictive control (FS-MPC). In FS-MPC the optimal switching state is selected based on the evaluation and minimization of a cost function for all possible voltage space vectors (VSV's) of the inverter. The second method performs a simplified FS-MPC (SFS-MPC) where the selection and evaluation of the possible VSV's are reduced to only three. The third method is based on the principle of predictive direct torque control (PDTC), where the duty cycle of the switching state is optimized for application in the inverter. Finally, a method that combines FS-MPC and PDTC named model predictive torque control (MPTC) is presented. This paper introduces the methodology and the results of a compressive comparison of the four predictive schemes based on different criterions. The control schemes are implemented on a field programmable gate array and are applied to a PMSM. Experimental results are presented to validate the presented comparison and discussion.

This research presents a way to improve the autonomous maneuvering capability of a four-degrees-of-freedom (4DOF) autonomous underwater vehicle (AUV) to perform trajectory tracking tasks in a disturbed underwater environment. This study considers four second-order input-affine nonlinear equations for the translational (x,y,z) and rotational (heading) dynamics of a real AUV subject to hydrodynamic parameter uncertainties (added mass and damping coefficients), unknown damping dynamics, and external disturbances. We proposed an identification-control scheme for each dynamic named Dynamic Neural Control System (DNCS) as a combination of an adaptive neural controller based on nonparametric identification of the effect of unknown dynamics and external disturbances, and on parametric estimation of the added mass dependent input gain. Several numerical simulations validate the satisfactory performance of the proposed DNCS tracking reference trajectories in comparison with a conventional feedback controller with no adaptive compensation. Some graphics showing dynamic approximation of the lumped disturbance as well as estimation of the parametric uncertainty are depicted, validating effective operation of the proposed DNCS when the system is almost completely unknown.

This paper presents an improved model predictive torque control (MPTC) scheme for a permanent magnet synchronous machine (PMSM). In conventional finite set model predictive control (FS‐MPC), the optimal voltage space vector (VSV) is selected for application in the voltage source inverter (VSI) by the evaluation and minimisation of a cost function; however, the application of a single VSV in the whole control cycle leads to a relatively high torque ripple and a high sampling time is necessary to overcome this issue. In order to reduce these shortcomings without sacrificing torque reaction performance, a modified MPTC is proposed. First, in order to reduce the computational burden, a switching table is introduced for a reduction in the selection of the possible VSVs; further, to reduce the torque ripple, the duty cycle optimisation of a subset of VSVs available from the VSI is performed. The conventional FS‐MPC and the proposed control scheme are implemented using a field programmable gate array, and are experimentally compared in terms of torque and flux ripple, current harmonics, and computational effort.

This paper proposes the use of a Permanent Magnet Synchronous Motor (PMSM) as a propulsion system of an electric vehicle (EV). The motor is digitally controlled by using the classical Direct Torque Control (DTC) scheme. The use of a direct torque controlled PMSM-drive offers several advantages such as quick, and robust torque reaction, highperformance control speed and increase efficiency. Experimental results obtained through the use of a commercially available interior PMSM, a self-developed voltage source inverter of own design and a cost effective digital TMS320F28335 Delfino® digital system show an excellent torque and speed performance of the developed electric-propulsion system.