In this brief, we propose a predictive algorithm for direct yaw moment control (DYC) in which a vehicle model is identified by a finite-dimensional approximation of the Koopman operator. The Koopman operator is a linear predictor for nonlinear dynamical systems based on raising the nonlinear dynamics into a higher-dimensional space where its evolution is linear. A novel method for the finite-dimensional numerical approximation of the Koopman operator is proposed, called enhanced extended dynamic mode decomposition (E ² DMD). This method allows the reduction of the basis dimension, determined by a user-defined dictionary of observable functions, to achieve a trade-off between model complexity and accuracy. The E ² DMD Koopman vehicle model was obtained from the dataset generated by simulating different scenarios using the nonlinear vehicle model and was then used to develop a Koopman operator model predictive control (KMPC) algorithm. KMPC was compared to a linear time variant (LTV) and a nonlinear model predictive control (NMPC), which are widely used in the literature, and showed better performance in some cases and a reduction in computational complexity in all cases.

This paper proposes a dual-mode model predictive direct current control (MP-DCC) of a permanent magnet synchronous generator (PMSG). The proposed algorithm is capable of minimizing switching losses in a two-level synchronous generator side converter (SGSC). A new prediction model which takes the converter dead time into consideration when choosing the optimal switching state is introduced. The proposed prediction model provides a more accurate state prediction ensuring that the states of the system stay inside the control invariant set in the steady state even in the case of a significant converter dead time. To guarantee recursive feasibility and closed-loop stability a flexible control Lyapunov function (CLF) is employed as an optimization problem constraint which enables the minimization of switching losses both during transients and in the steady state. The influence of the converter dead time on the performance of the proposed algorithm is considered, and accordingly, a control invariant set is determined. Simulation results show that stator currents are kept within the control invariant set if dead time is taken into account in the prediction model. Furthermore, the proposed algorithm is implemented in a digital control system and experimentally verified on a 375 kW interior PMSG. Experimental results verify that the proposed control algorithm provides a successful flying start of the PMSG, and show that the application of the flexible CLF results in lower switching frequency, but also higher current ripple. By adjusting the upper bound of the control invariant set a desired trade-off between the low stator current ripple and the minimization of switching losses can be achieved.

We consider problem of exploration and mapping of unknown indoor environments using laser range finder. We assume a setup with a resolved localization problem and known uncertainty sensor models. Most exploration algorithms are based on detection of a boundary between explored and unexplored regions. They are, however, not efficient in practice due to uncertainties in measurement, localization and map building. The exploration and mapping algorithm is proposed that extends Ekman's exploration algorithm by removing rigid constraints on the range sensor and robot localization. The proposed algorithm includes line extraction algorithm developed by Pfister, which incorporates noise models of the range sensor and robot's pose uncertainty. A line representation of the range data is used for creating polygon that represents explored region from each measurement pose. The polygon edges that do not correspond to real environmental features are candidates for a new measurement pose. A general polygon clipping algorithm is used to obtain the total explored region as the union of polygons from different measurement poses. The proposed algorithm is tested and compared to the Ekman's algorithm by simulations and experimentally on a Pioneer 3DX mobile robot equipped with SICK LMS-200 laser range finder.

This paper proposes a direct model predictive control method with a fixed switching frequency for stator flux control of a synchronous reluctance machine. Besides ensuring a fixed switching frequency, the main objective is to minimize the ripple of the stator flux. The objective function, which calculates the switching time instants within the switching period based on the stator flux gradients, is formulated as a standard constrained quadratic programming (QP) problem. Instead of using general-purpose QP solvers, an iterative algorithm based on the active-set method with Lagrange multipliers is proposed to solve this particular QP problem while reducing the computational complexity of the proposed algorithm and enabling successful implementation in real-time embedded systems. The proposed method has been successfully implemented on a laboratory model and the results are compared with a conventional indirect model predictive control. The experimental results show better performance in stator flux ripple and lower total stator current distortion factor for the proposed method compared to the conventional method. A 7.5 kW synchronous reluctance motor drive was used for the experimental validation of the proposed control method.