ABSTRACT This paper presents a model predictive current control (MPCC) strategy for switched reluctance motors (SRMs) aimed at mitigating the computational complexity and torque ripple issues commonly associated with conventional control methods. To address these challenges, an integrated MPCC and direct torque control (DTC) framework is proposed, leveraging active voltage vectors (VVs) that incorporate rotor position‐dependent sector selection. This approach enables accurate prediction of phase current trajectories across various switching states. A key feature of the proposed method is the sector‐based allocation, which limits the number of active switching states to four, substantially improving its real‐time applicability. The control algorithm minimizes a cost function that penalizes both current tracking error and switching effort, thereby ensuring precise current regulation while reducing unnecessary switching transitions. Furthermore, the integrated MPCC‐DTC algorithm enhances torque control by employing Model Predictive Phase Compensation to identify the motor phase with optimal torque tracking capability. The proposed model was developed and validated using MATLAB, and the simulation results were verified for torque regulation under dynamic conditions. Experimental results demonstrate that the proposed hybrid control strategy significantly reduces computational overhead while achieving superior torque ripple suppression compared to conventional predictive controllers.
ABSTRACT Electric vehicles (EVs) are gaining popularity worldwide as a means to reduce carbon emissions (CE) and dependence on fossil fuels. However, the limited availability of public charging stations remains a major barrier to adoption. This study proposes a hybrid strategy for solar distributed generation (DG) and EV charging stations (EVCS) installation in distribution systems (DSs). The novelty of this paper lies in the innovation of the spatial Bayesian neural network (SBNN) and greater cane rat algorithm (GCRA). It is therefore called GCRA–SBNN. The main goals of this proposed approach are to lower power costs and losses while simultaneously enhancing the voltage profile of the system. The proposed GCRA technique is employed to optimize the rate of EVCS units in the DS, and the SBNN method is used to predict optimal locations for EVCS. Then, the proposed strategy is implemented in the MATLAB working platform, and the existing procedure is used to calculate the results. The proposed strategy outperforms all current approaches, including the gray wolf optimizer (GWO), genetic algorithm (GA), and particle swarm optimization (PSO). The existing method shows power losses of 69.428, 71.3, and 72.406 kW, while the proposed method results in a reduced power loss of 68.4272 kW. For the voltage profile, the present technique generates values of 0.932, 0.928, and 0.918 p.u, whereas the proposed strategy achieves a higher voltage profile of 0.9378 p.u. This improvement indicates that the proposed technique not only minimizes power loss but also improves voltage stability. The proposed technique provides better performance than the current method. Overall, these advances show that the proposed strategy provides a more efficient and stable solution for power DSs.
ABSTRACT The use of multiple shooting has become the standard for the numerical solution of optimal control problems. We investigate how multiple shooting affects the convergence properties of Newton‐type methods. For the first time, we conduct a systematic comparison of several multiple shooting strategies on a set of 40 optimal control problems. In addition, we consider differences between interior‐point and sequential quadratic programming methods, accounting for both Quasi‐Newton approximations and exact Hessians. Based on these observations, we propose an adaptive multiple shooting algorithm that reduces the number of iterations by about 27% on average across all problems and by more than 50% for selected problems compared with naïve multiple shooting approaches.
Scabies is a highly contagious parasitic skin disease that poses persistent public health challenges, particularly in overcrowded and resource-limited settings. A rigorous mathematical framework is developed to investigate the transmission dynamics of scabies by incorporating both direct human-to-human transmission and indirect environmental contamination by mites. A nonlinear SEIR-M compartmental model is formulated, coupling human population dynamics with an environmental mite reservoir. Qualitative analysis establishes the positivity and boundedness of solutions, ensuring the biological feasibility of the model. The basic reproduction number is derived and shown to govern the global dynamics of the system. Local and global stability analyses of the disease-free and endemic equilibria are performed, revealing threshold conditions for disease persistence and elimination. Bifurcation analysis demonstrates the occurrence of transcritical, backward, and Hopf bifurcations, highlighting the rich nonlinear dynamics induced by environmental transmission. An optimal control problem is formulated to evaluate intervention strategies involving treatment, public awareness, and environmental sanitation. Necessary optimality conditions are derived using Pontryagin's Maximum Principle and solved numerically via the forward-backward sweep method. Numerical simulations indicate that combined control strategies substantially reduce infection prevalence and environmental mite concentration compared to isolated interventions. The results provide a mathematically rigorous foundation for understanding scabies dynamics and demonstrate the effectiveness of integrated control strategies for environmentally mediated infectious diseases.
This paper proposes a novel dynamic terminal set approach for the model predictive control (MPC) design. It concerns the class of linear systems subject to stochastic disturbances and builds upon an indirect feedback (IF) strategy. The main contribution is to explore the time-varying definition of the constraints in the indirect feedback SMPC (IF-SMPC) to improve the feasible domain. Using this feature, the closed-loop performance of the indirect SMPC is not degraded, and the domain of attraction is enlarged, while the number of decision variables of the SMPC optimization problem is preserved. A constructive procedure is provided to obtain the sequence of time-varying (evolving) terminal sets. Moreover, simulation case studies based on two benchmarks illustrate the notions and show the advantage of the new time-varying terminal set approach for the IF-SMPC.
