Single Cost Function Research Articles

An improved finite set model predictive control of SRM drive based on a voltage vectors strategy for low torque ripple

The robust rotor structure and fault-tolerance characteristics of the Switched Reluctance Motors (SRMs) are the best choice for next-generation Electric Vehicle (EV) applications. This machine has few restraints like high torque and flux ripples. However, the existing Model Predictive Control (MPC) using multiple control objectives and maximum sectors in the switching table results in high torque ripples due to the improper sector partition, Voltage Vectors (VVs) and weight factor (K1) selection. This paper proposes a Finite Set-Model Predictive Control (FS-MPC) for an analytical model of a non-linearity SRM machine to analyze the torque ripple performance. The proposed VVs are derived using sector partition based on the rotor position. The control is designed as a single cost function with the weighting factor contributing to smooth torque by selecting optimal control signals. Simulation studies and experiments with a four-phase 8/6 SRM drive verifies the enhanced FS-MPC for real-time implementation. The dynamic speed and ripple values of SRM Drives are measured using a mixed signal oscilloscope and the sensor probes. The laboratory outcomes calculate the analytical equations to validate the findings. The calculated value of torque ripple is 9 % through this FS-MPC. The study reveals that the proposed method is well suited for torque ripple reduction than flux ripples.

Experimental analysis of enhanced finite set model predictive control and direct torque control in SRM drives for torque ripple reduction

The magnet-less switched reluctance motor (SRM) speed-torque characteristics are ideally suited for traction motor drive characteristics and its advantage to minimize the overall cost of on-road EVs. The main drawbacks are torque and flux ripple, which have produced high in low-speed operation. However, the emerging direct torque control (DTC) operated magnitude flux and torque estimation with voltage vectors (VVs) gives high torque ripples due to the selection of effective switching states and sector partition accuracy. On the other hand, the existing model predictive control (MPC) with multiple objective and optimization weighting factors produces high torque ripples due to the system dynamics and constraints. Therefore, existing DTC and MPC can result in high torque ripples. This paper proposed a finite set (FS)-MPC with a single cost function objective without weighting factor: the predicted torque considered to evaluate VVs to minimize the ripples further. The selected optimal VV minimizes the SRM drive torque and flux ripples in steady and dynamic state behaviour. The classical DTC and proposed model were developed, and simulation results were verified using MATLAB/Simulink. The proposed model operated in SRM drives experimental results to prove the effective minimization of torque and flux ripples compared to the existing DTC.

An improved model predictive control for linear induction machine drive-based split-source inverters

The Split Source Inverter (SSI) is a single-stage DC-AC converter topology widely recognized for its beneficial features compared to the Z-source inverter. These advantages include a reduced number of components, a constant input current, and minimized losses. This study aims to extensively investigate and analyze the operation of a 3 kW Linear Induction Motor (LIM) using SSI through comprehensive simulations. The suggested control approach, employing finite control-model predictive thrust control (FC-MPTC), exhibits remarkable dynamic behavior and achieves a fast transient response without necessitating modifications to the control loop. Moreover, the proposed method allows for the manipulation of multiple variables by utilizing a single cost function, eliminating the need for lookup tables. The efficacy of the proposed control method is validated through comprehensive simulations and experimental tests, which exhibit accurate reference tracking speed and superior dynamic performance in regulating the thrust and primary flux. The results also showed that the proposed method had lower undulations of 5.8% than the traditional method. The proposed approach outperforms the DTC-SVM method under varying load and speed conditions by delivering remarkable accuracy and improved control characteristics.

Model predictive control for Vienna rectifier with constant frequency based on inductance parameters online identification

When the finite control set model predictive control (FCS-MPC) is applied to the three-level converter, there are problems such as large current harmonics, high requirements for the computing efficiency of the microcontroller, complex multi-objective optimization and limited output vector switching. In addition,the mismatch of inductance parameter may directly affect the observation accuracy of FCS-MPC. To solve the above problem,a double vector model predictive control strategy with constant frequency strategy based on the inductance parameters on-line identification is presented.First, a single objective cost function based on the direct power is constructed by optimizing the redundant vector which is selected to balance the neutral-point potential, the design of weighting factor is avoided.At the same time, in order to effectively reduce the grid current ripple under single vector modulation, space vector pulse width modulation (SVPWM) is realized by combining with zero vector control mode. And, a parameter identification method based on model reference adaptive system(MARS) is proposed to overcome the effect of model parameter mismatch.Finally, the results show that the proposed F-MPCCF has good steady-state and dynamic performance from the static, transient and neutral-point potential control.

Evolution of dosimetric treatment planning for pediatric total lymphoid irradiation (TLI): a single-institution experience.

Total lymphoid irradiation (TLI) is a conditioning regimen in allogeneic hematopoietic stem cell transplantation (allo-HSCT) which may reduce long-term toxicities attributed to other techniques, such as total body irradiation (TBI). At our institution, TLI treatments were first planned with the three-dimensional conformal radiation therapy (3D-CRT) technique and later with volumetric modulated arc therapy (VMAT). With the recent availability of a basic helical tomotherapy (HT), the possible dosimetric gain of the latter for TLI is studied. 22 pediatric patients were planned for VMAT and HT, prescribed to 8 Gy in 4 fractions. VMAT was planned with template based on a single cost function, using the Monaco treatment planning system (TPS). HT plans were planned using Accuray Precision TPS for a basic HT without the dynamic jaws feature or VOLO-Ultra algorithm. Plan quality was analyzed based on four quality indices, mean and maximum doses to planning target volume (PTV) and organs at risk (OARs), dose gradient and integral doses. Differences were analyzed with Wilcoxon signed-rank test. HT plans resulted in improved conformity (CI) and homogeneity indices (HI) (p < 0.05) but less steep dose gradient (p = 0.181). VMAT plans created larger areas with high doses within the PTV, while comparable doses to OARs, except mainly for the spinal marrow, for which a reduction of 37.7% in D2% was obtained (p < 0.05). Integral dose for non-tumor tissue was 11.3% lower with the VMAT template (p < 0.05). HT achieves better conformity and homogeneity even without its more advanced features. Nevertheless, the VMAT template achieves dosimetric results close to those of HT, both with similar clinical outcome.

Explaining Known Past Routes, Underdetermination, and the Use of Multiple Cost Functions

Explaining material traces of movement as proxies for past movement is fundamental for understanding the processes behind why people in the past traversed the landscape in the way that they did. For this, least-cost path analysis and the use of slope-based cost functions for estimating the cost of movement when walking have become commonplace. Despite their prevalence, current approaches misrepresent what these cost functions are, their relationship to the hypotheses that they aim to represent, and their role in explanation. As a result, least-cost paths calculated using single cost functions are liable to spurious results with limited power for explaining known past routes, and by extension the decision-making processes of past people. Using the ideas of multiple model idealisation and robustness analysis, and applied via a tactical simulation, this study demonstrates that similar least-cost paths can be produced from slope-based cost functions representing both the same hypothesis and different hypotheses, suggesting that least-cost path results are robust but underdetermined under the tested environmental settings. The results from this tactical simulation are applied for the explanation of a Roman road in Sardinia. Using probabilistic least-cost paths as an approach for incorporating multiple cost functions representing the same hypothesis and error in the digital elevation model, it is shown that both model outcomes representing the minimisation of time and energy are unable to explain the placement of the Roman road. Rather, it is suggested that the Roman road was influenced by pre-existing routes and settlements.

An efficient approximation to the pull policy for hybrid manufacturing and remanufacturing systems with setup costs

We consider a continuous-review inventory control problem for a hybrid manufacturing and remanufacturing system with product recovery and setup costs. A pull policy has been proposed in the literature, however, finding its optimal parameters requires an exhaustive search and a single cost function evaluation is itself complex. We propose a tractable approach to finding these parameters using an interim policy called double (r, Q) with parameters ( r m , Q m , r r , Q r ) . When the inventory position of the serviceable item reaches rr , a remanufacturing lot size Qr is setup if recoverable inventory is sufficient. Otherwise, we allow the inventory position to decrease further. As it drops to rm , a manufacturing lot size Qm is placed. Unlike the pull policy, this interim policy suspends the remanufacturing option when the inventory position is less than rr . This facilitates an efficient approximation of the recoverable inventory which decouples the double (r, Q) problem into two standard (r, Q) problems. It can then be efficiently solved using a modification of existing (r, Q) algorithms for two instances. Numerical studies show that our approach performs well relative to the optimal pull policy with parameters estimated from an extensive Simulation-Optimization method and other heuristics found in the literature. The approach is also extended to correlated demand and return arrivals.

Multiobjective Intelligent Energy Management for Hybrid Electric Vehicles Based on Multiagent Reinforcement Learning

This paper proposes a multi-objective energy management strategy (EMS) based on multi-agent reinforcement learning (MARL) for a hybrid electric vehicle equipped with an engine-generator set (EGS) and a hybrid energy storage system (HESS, consisting of a battery and ultracapacitor). First, besides improving fuel economy, maintaining battery state of charge (SOC), reducing battery degradation and constraint on ultracapacitor SOC are also taken into consideration, formulating multi-objective energy management. Then, the problem is solved using MARL which combines game theory and reinforcement learning. In this framework, EGS and HESS are viewed as two intelligent agents respectively, and their interactions are described as a general-sum stochastic game. Following the principle of MARL, the two agents can learn the optimal control policy which guarantees the Nash equilibrium of multiple objectives, thus achieving a satisfactory balance among them. In the simulation, the MARL-based EMS is compared with single-agent reinforcement learning (SARL) which ignores the relations of different agents, and dynamic programming (DP) which integrates various targets into a single cost function with weight coefficients. The simulation results verify the superiority of the proposed EMS in optimizing multiple objectives.

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Classical calibration methods in hydrology typically rely on a single cost function computed on long-term streamflow series. Even when hydrological models achieve acceptable scores in NSE and KGE, imbalances can still arise between overall model performance and its ability to simulate flood events, particularly flash floods. Multi-scale signatures, which refer to hydrological signatures computed at different temporal and/or spatial scales, and distributed flood modeling, which accounts for spatial variability in input variables and model parameters, are important concepts in hydrological modeling. In this study, the potential of using multi-scale signatures is explored to enhance multi-criteria calibration methods for spatially distributed flood modeling, which remains considerable challenges. We present a novel signatures and sensitivity-based calibration approach implemented into a variational data assimilation algorithm capable to deal with high dimensional spatially distributed hydrological optimization problems. It is tested on 141 flash flood pronecatchments mostly located in the French Mediterranean region. Our approach involves computing several signatures, including flood event signatures, using an automated flood segmentation algorithm. We select suitable signatures for constraining the model based on their global sensitivity with the input parameters through global signature-based sensitivity analysis (GSSA). We then perform two multi-criteria calibration strategies using the selected signatures, including a single-objective optimization approach, which transforms the multi-criteria problem into a single-objective function, and a multi-objective optimization approach, which uses a simple additive weighting method to select an optimal solution from the Pareto set. Our results show significant improvements in both calibration and temporal validation metrics, especially for flood signatures, demonstrating the robustness and delicacy of our signatures-based calibration framework for enhancing flash flood forecasting systems.

Effective multi-objective inverse lithography technology at full-field and full-chip levels with a hybrid dynamic priority algorithm.

Inverse lithography technology (ILT), such as source mask optimization (SMO), is used to improve lithography performance. Usually, a single objective cost function is selected in ILT, and an optimal structure for one field point is achieved. The optimal structure is not the case for other images at full field points where the aberrations of the lithography system are different, even in high-quality lithography tools. The optimal structure that must match the high-performance images at the full field is urgently required for extreme ultraviolet lithography (EUVL). In contrast, multi-objective optimization algorithms (MOAs) limit the application of multi-objective ILT. Assigning target priority is incomplete in current MOAs, which results in the over-optimization of some targets and under-optimization of others. In this study, multi-objective ILT and a hybrid dynamic priority (HDP) algorithm were investigated and developed. High-performance images with high fidelity and high uniformity were obtained at multi-field and multi-clip areas across the die. A hybrid criterion was developed for the completion and reasonable prioritization of each target to ensure sufficient improvement. Compared to the current MOAs, the uniformity of images at full-field points was improved by up to 31.1% by the HDP algorithm in the case of multi-field wavefront error-aware SMO. The multi-clip source optimization (SO) problem showed the universality of the HDP algorithm to deal with different ILT problems. It acquired higher imaging uniformity than existing MOAs, which indicated that the HDP is more qualified for multi-objective ILT optimization than existing MOAs.

Model predictive control ofconsensus-based energy management system for DC microgrid.

The increasing deployment and exploitation of distributed renewable energy source (DRES) units and battery energy storage systems (BESS) in DC microgrids lead to a promising research field currently. Individual DRES and BESS controllers can operate as grid-forming (GFM) or grid-feeding (GFE) units independently, depending on the microgrid operational requirements. In standalone mode, at least one controller should operate as a GFM unit. In grid-connected mode, all the controllers may operate as GFE units. This article proposes a consensus-based energy management system based upon Model Predictive Control (MPC) for DRES and BESS individual controllers to operate in both configurations (GFM or GFE). Energy management system determines the mode of power flow based on the amount of generated power, load power, solar irradiance, wind speed, rated power of every DG, and state of charge (SOC) of BESS. Based on selection of power flow mode, the role of DRES and BESS individual controllers to operate as GFM or GFE units, is decided. MPC hybrid cost function with auto-tuning weighing factors will enable DRES and BESS converters to switch between GFM and GFE. In this paper, a single hybrid cost function has been proposed for both GFM and GFE. The performance of the proposed energy management system has been validated on an EU low voltage benchmark DC microgrid by MATLAB/SIMULINK simulation and also compared with Proportional Integral (PI) & Sliding Mode Control (SMC) technique. It has been noted that as compared to PI & SMC, MPC technique exhibits settling time of less than 1μsec and 5% overshoot.

Structured Factor Analysis: A Data Matrix-Based Alternative Approach to Structural Equation Modeling

Jöreskog’s covariance-based approach (JCA) has been considered a standard method for structural equation modeling. However, JCA is prone to the occurrence of improper solutions and cannot make probabilistic inferences about the true factor scores. To address the enduring issues of JCA, we propose a data matrix-based alternative, termed structured factor analysis (SFA). Given a data matrix of indicators, SFA begins by estimating both measurement model parameters and factor scores by minimizing a single cost function via an alternating least squares algorithm, which mathematically guarantees convergence to proper solutions. It then employs the factor score estimates to estimate structural model parameters. Once all parameters are estimated, SFA further estimates the probability distribution of the factor scores that can generate the data matrix of indicators, which can be used for probabilistic inferences about the true factor scores. We investigate SFA’s performance and empirical utility through simulated and real data analyses.

Thrust Ripple Suppression for Linear Induction Machines Based on Improved Finite Control Set-Model Predictive Voltage Control

In this article, two kinds of finite control set-model predictive voltage control (FCS-MPVC) methods have been proposed for linear induction machine to eliminate the cumbersome selection of the weighting factor (WF), achieve lower ripples of both thrust and flux, and reduce the heavy calculation steps. The first method, FCS-MPVC I, uses one voltage vector (VV) during the whole control period, and the second method, FCS-MPVC II, uses two VVs. Moreover, the proposed methods use a single cost function without any WF. This function consists of the errors between primary voltage references with their predicted values in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">αβ</i> -axis. Depending on the distance between the reference VV (RVV), as calculated using the deadbeat control method, and all candidate VVs (CVVs), the first proposed method, FCS-MPVC I, can select the nearest VV to the RVV as the optimal VV (OVV), which can reduce the redundant evaluation work greatly. Meanwhile, the second proposed method, FCS-MPVC II, applies two VVs, active VV and null VV, to adjust the duty ratio effectively. To avoid the iterated evaluation for all CVVs, one simplified search order excluding unsuitable CVVs has been proposed to determine the optimal OVV, which selects only one active VV from the two adjacent active VVs in the sector owning the RVV. Afterward, based on a prototype of 3-kW arc induction machine with a large radius, extensive simulation and experiments have fully demonstrated that the proposed methods can get much lower thrust ripples and less calculation steps effectively, compared with those of conventional finite control set-model predictive thrust control strategy.

Selective and optimal dynamic pricing strategy for residential electricity consumers based on genetic algorithms

The continuous increase in residential population implies an increase in electricity demand. The demand prediction performed by the utility company (UC) to schedule and plan the next energy purchase from the market may not be efficient since the actual demand consistently exceeds the supply. To use the energy production infrastructure efficiently and decrease the grid system's overload, the UC seeks to match the demand with the supply. Dynamic pricing is a simple yet effective way to influence consumption as price-sensitive consumers may reschedule the operation of some of the appliances. This paper presents a novel, consumption data-driven, selective and dynamic Time-of-Use (ToU) electricity pricing strategy applicable by the UC for residential electricity consumers. The method consists of clustering real consumption data using the k-means clustering technique and heuristically categorizing clusters based only on their consumption data to simplify the design of the ToU tariffs and limit the number of different tariffs in the same population. The method includes a heuristic determination of the time period for the ToU tariff changes for each category. The ToU tariff parameters are determined by minimizing a single cost function using genetic algorithms and considering all consumer clusters such that the consumer behavior model is based on price elasticity. The optimal ToU tariffs result in decreasing the demand in power peaks by targeting the overconsumer categories in the latter. Implementing the ToU tariff results in a profit margin distributed among the UC and the consumer. Moreover, the optimal prices guarantee positive gains for both of them with the presence of a wide range of parameter uncertainties. The proposed pricing strategy necessitates the presence of consumption data only, which implies the conservation of the consumers' privacy. Real consumption data obtained for two towns in Hungary over three years is used to show the efficiency of the proposed pricing strategy.

Optimal Sector-Based Sequential Model Predictive Control for Current Source Rectifier

Conventional model predictive control (MPC) methods for current source rectifier (CSR) use either a PI controller or weight factor-based methods, which require time-consuming tuning methods to develop an efficient controller. Moreover, the limitations on the bandwidth requirements of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> filter cause resonances and grid current distortions if a variable switching frequency-based predictive control is used. So, a new fixed switching frequency-based MPC algorithm has been developed to control the load and source current simultaneously, thereby facilitating the weight factor less operation and applying a predefined space vector sequence. Unlike the conventional single cost function methods applied to CSR, the proposed method uses separate cost function for each control variable. Simultaneously, to provide fixed frequency operation, duty ratio-based modulation of grid current control has been applied. A virtual impedance-based active damping has been embedded into the supply current control to suppress the oscillations caused by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$LC$ </tex-math></inline-formula> filter and grid current harmonics. Simulation and experimental results have been supported to validate the improvements in terms of supply current total harmonic distortion (THD), steady state, and transient switching performance.

Hybrid MPPT-based predictive speed control model for variable speed PMSG wind energy conversion systems

In this research, a predictive speed control (PSC) technique based on permanent magnet synchronous generators is proposed for variable-speed wind energy conversion systems (VS-WECS) (PMSG). The control approach that has been developed makes it possible to regulate mechanical and electrical variables concurrently within the context of a single cost function. The power converter will then use the optimum switching state that will result in the lowest possible cost function when it has been chosen. The maximum power point tracking (MPPT) algorithms used in the proposed control approach are combined in order to achieve optimum efficiency. As a direct result of this, the conventional cascade structure of proportional-integral (PI) controllers has been removed, which results in an improvement in the system's dynamic responsiveness. In addition, predictive current control, also known as PCC, is implemented on the grid-side converter, also known as the GSC, in order to accomplish decoupled grid current control. Using MATLAB/SIMULINK, we analyze the performance of the suggested control methods and compare it to the performance of a traditional PI speed controller. The findings demonstrated that the MPC controller is superior than the PI controller in terms of its ability to handle system dynamics.

Socially fair network design via iterative rounding

We show that the multi-criteria generalized Steiner network problem with ℓ≥2 cost functions has a polynomial-time ℓ-approximation algorithm. This generalizes the celebrated result of Jain for a single cost function. We also show that the approximation factor is tight for the natural LP relaxation. The problem is motivated by network design where multiple players have different or competing valuations and must agree on a common network (e.g., infrastructure design).

MPC-Based Startup Current Shaping Strategy With State-Space Model of DAB in DC Distribution System

The popularity of renewable energy sources and load structure changes make the development of dual active bridge (DAB) dc–dc converters in the dc distribution system. However, due to the fast start and the large output capacitors, DAB converters suffer from inrush current. This article aims to design a current shaping scheme with a fast dynamic response to startup quickly and control the inrush current. First, the state-space average model of the startup process and its simplification are analyzed. It presents a theoretical basis for the widely used vary-frequency segmented startup scheme. Then, the model predictive control with the current shaping (MPC-CS) scheme that features fast dynamic response is presented. Also, single objective cost function, demarcation method between operating modes, and enhanced preprocessing modules are introduced to address the difficulties increased by the high switching frequency using wide bandgap devices. Finally, evaluations and comparisons among two typical schemes and MPC-CS scheme are carried out on simulations and a full-silicon-carbide-based DAB converter with the distributed heterogeneous controllers, which verify the practicality of the MPC-CS.

Quantum algorithm for electronic band structures with local tight-binding orbitals

While the main thrust of quantum computing research in materials science is to accurately measure the classically intractable electron correlation effects due to Coulomb repulsion, designing optimal quantum algorithms for simpler problems with well-understood solutions is a useful tactic to advance our quantum “toolbox”. With this in mind, we consider the quantum calculation of a periodic system’s single-electron band structure over a path through reciprocal space. Previous efforts have used the Variational Quantum Eigensolver algorithm to solve the energy of each band, which involves numerically optimizing the parameters of a variational quantum circuit to minimize a cost function, constructed as the expectation value of a Hamiltonian operator. Traditionally, a unique Hamiltonian operator is constructed for each k-point, so that many cost functions, each with their own parameter space, must be optimized to generate a single band. Similarly, calculating higher bands than the first has traditionally involved modifying the cost function with additional overlap terms to ensure higher-energy eigenstates are orthogonal to those of lower bands. In this paper, we adopt a direct space approach, using a novel hybrid first/second-quantized qubit mapping which allows us to construct a single Hamiltonian, and a single cost-function, suitable for solving the entire electronic band structure. In contrast to previous approaches, the k-point and the band index are selected by additional parameters in our quantum circuit, rather than through modifications to the cost function. The result is a technically and conceptually simpler approach to band structure calculations on a quantum computer. Moreover, we expect that the tools developed herein will motivate new strategies for tackling highly-correlated materials beyond the grasp of classical computing.

From Fitting the Average to Fitting the Individual: A Cautionary Tale for Mathematical Modelers.

An outstanding challenge in the clinical care of cancer is moving from a one-size-fits-all approach that relies on population-level statistics towards personalized therapeutic design. Mathematical modeling is a powerful tool in treatment personalization, as it allows for the incorporation of patient-specific data so that treatment can be tailor-designed to the individual. Herein, we work with a mathematical model of murine cancer immunotherapy that has been previously-validated against the average of an experimental dataset. We ask the question: what happens if we try to use this same model to perform personalized fits, and therefore make individualized treatment recommendations? Typically, this would be done by choosing a single fitting methodology, and a single cost function, identifying the individualized best-fit parameters, and extrapolating from there to make personalized treatment recommendations. Our analyses show the potentially problematic nature of this approach, as predicted personalized treatment response proved to be sensitive to the fitting methodology utilized. We also demonstrate how a small amount of the right additional experimental measurements could go a long way to improve consistency in personalized fits. Finally, we show how quantifying the robustness of the average response could also help improve confidence in personalized treatment recommendations.