Multi-criteria Optimization Research Articles

Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck

Objective. Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume (CTV) in predefined scenarios, albeit at a price of increased planning times. We investigated a deep learning (DL) strategy for dose predictions in individual error scenarios in head and neck cancer IMPT treatment planning, enabling direct evaluation of plan robustness. The model is able to differentiate between scenarios by using embeddings of the scenario index.Approach. To accommodate resolution disparities in planning CT-scans and accommodate the setup error scenarios, we introduced scenario-specific isocentric distance maps as inputs to the DL models. For 392 H&N cancer patients, high-quality 9-scenario ground truth (GT) robust plans were generated with wish-list driven fully automated multi-criteria optimization. The scenario index is converted to one-hot-vector that is used to derive the scenarios embeddings through the training of the DL model, aiding the model to predict a scenario specific dose distribution.Main results. The model achieved within 1%-point of agreement with the GT the predictedV95%of the voxelwise minimum dose for CTV Low and CTV High for 96% and 75% respectively of the test patients. Considering all robustness scenarios, median differences were 0.035%-point for CTV HighV95%, 0.11%-point for CTV LowV95%, 0.29 GyE for parotidsDmean, 0.7 GyE for submandibular glandsDmeanand 0.9 GyE for oral cavityDmean. Prediction of full 3D dose distributions for all scenarios took around 14 s.Significance. Predicting individual scenarios for robust proton therapy using DL dose prediction is feasible, enabling direct robustness evaluation of the predicted scenario doses.

Multicriteria optimization achieves superior normal tissue sparing in volumetric modulated arc therapy for gastric cancer.

To evaluate the benefits of volumetric modulated arc therapy (VMAT) based on multicriteria optimization (MCO) for gastric cancer patients, particularly the protection of serial organs at risk (OARs) that overlap with the target volume. MCO and single-criterion optimization (SCO) VMAT plans were conducted among 30 gastric cancer patients, with a prescription dose of 50.4Gy delivered in 28 fractions. All treatment plans underwent review, and a comparison was made between the active planning time and different dose-volume parameters. Both the MCO and SCO VMAT plans achieved the target dose coverage, with no significant difference in the conformity index (CI) for the planning target volume (PTV), at median CI values of 0.887 and 0.891, respectively (P = 0.417). The MCO plans showed a slight but significant increase in the homogeneity index of the PTV, with a median increase of 0.029 (P < 0.001). Additionally, the MCO plans resulted in a lower D2% to the small intestine and duodenum, with reductions of 3.43Gy and 0.3Gy, respectively (P < 0.05). Furthermore, the Dmax to the small intestine correlated moderately with the overlapping volume between the small intestine and the target volume (ρ = 0.42, P = 0.023). Except for the mean dose to the liver, the MCO plans performed better in terms of dose indicators for other OARs. Moreover, compared to the SCO plans, the median active planning time in the MCO plans was significantly reduced by 53.2min (P < 0.0001). MCO can effectively help the physicians to quickly select an optimal treatment plan for patients with gastric cancer. It has been shown that MCO VMAT plans can significantly reduce the dose to OARs and shorten the active planning time, with acceptable target coverage. In addition, these plans take less dosimetric time, thereby streamlining the treatment planning process.

Multi-Criteria Optimization of the Paper Production Process Using Numerical Taxonomy Methods: A Necessary Condition for Predicting Heat and Electricity Output in a Combined Heat and Power (CHP) System

The subject of this study is the optimization of the paper production process in one of Poland’s leading paper mills. In addition to its primary objective of paper production, the company generates heat and electricity for internal consumption and external clients, including the local municipality. Surplus energy may be sold on the power exchange; however, this requires forecasting the quantity of energy to be sold 24 h in advance, which introduces an element of uncertainty. Production stoppages, often caused by random events such as paper breakage, force a power decrease in the CHP system, further complicating energy forecasting. To minimize the occurrence of such events, numerical taxonomy methods were employed to determine the optimal screen speed (Vs) and winding speed (Vn) for two paper machines, based on the type and weight of the paper produced. This analysis utilized detailed daily data collected by the company over the period 2015–2020. The findings contribute to minimizing the occurrence of paper roll tearing, thereby reducing the risk of inaccurate forecasts of the energy and heat produced by the CHP system. Furthermore, the methodology employed in this study may be effectively applied to other optimization problems in industrial processes.

Experimental Investigation and NSGA-III Multi-Criteria Optimization of 60CrMoV18-5 Cold-Work Tool Steel Machinability Under Dry CNC Hard Turning Conditions

This work concerns an experimental investigation dealing with the machinability of 60CrMoV18-5 cold-work tool steel under dry CNC hard turning conditions using a CBN cutting insert. A response surface experiment based on the central composite design was set to conduct dry CNC hard-turning experiments with three different levels for cutting conditions, cutting speed Vc (m/min), feed rate f (mm/rev), and depth of cut α (mm) while selecting main cutting force and surface roughness Ra as the two machinability responses. The results were analyzed by applying analysis of variance (ANOVA). The effect of cutting conditions on main cutting force and surface roughness was studied through contour plots. Full quadratic regression models were generated to model the relationships between inputs and outputs. Finally, the NSGA-III algorithm was applied to simultaneously optimize the selected machinability parameters by providing beneficial values for determining cutting conditions. The results have shown that surface roughness is mainly affected by feed rate and cutting speed, whereas main cutting force is affected by depth of cut and feed rate.

Design of a hybrid solar and biomass-based energy system integrated with near-zero energy building: Techno-environment investigation and multicriteria optimization

The current research aims to meet the energy needs of a group of residential buildings in Norway with the least amount of carbon dioxide emission and the greatest amount of renewable energy. The supply side based on the renewable renewable-based energy system is planned and scaled using the real-time data use of domestic hot water (DHW), heating and electricity necessary for the buildings. PVT panels are used in the hybrid solar and biomass-based energy systems to produce both the DHW requirements and the electricity production. The digester is used with the heat pump, which generates heat. A double-effect absorption refrigeration system is also utilized to provide the necessary cooling requirements. On the supply side, the management of the heat streams and the redirection of flows are done using a rule-based regulating system. The whole plant's size is supplied, and the dynamic energy simulation is run. The primary deciding criteria for heating the buildings are the costs of electricity and biomass. The whole system is then optimized depending on operating circumstances, and the outcomes are compared to the design point. PVT may be used to create more than 80% of the yearly DHW. Furthermore, summertime radiation accounts for 64.8% of cooling output since it is more intense and may be converted into cooling energy. Digester/CC also contributes 66.55% of the building's heating, showing that the designers should rely on biomass as wintertime energy costs are higher. The parametric study demonstrates that increasing PVT time and tank capacity has varied effects on efficiency and emissions. Also, On winter days, there was a significant drop in renewable energy generation from summer to autumn, from 82.7 MWh to 28.52 MWh. The optimization results show that at the TOPSIS point, the total cost, efficiency, and emission index are 9.73 $/hr, 36.8%, and 7.75kg/MWh, respectively.

Saturday, July 13, 202410:30 AM - 11:30 AMPPP01 Presentation Time: 10:30 AM: A First-in-Man Randomized Clinical Trial of GPU Based Multi-Criteria Optimization (gMCO) versus IPSA in HDR Prostate Brachytherapy

Saturday, July 13, 202410:30 AM - 11:30 AMPPP01 Presentation Time: 10:30 AM: A First-in-Man Randomized Clinical Trial of GPU Based Multi-Criteria Optimization (gMCO) versus IPSA in HDR Prostate Brachytherapy

Evaluation of prototyping methods for interactive virtual systems based on fuzzy preference relation

The object of this study is the process of determining the optimal method for prototyping interactive virtual systems using the principle of multi-criteria optimization. The paper addresses the task to design tools for evaluating prototyping methods and introduces software developed to identify the best option according to priority criteria. The optimal prototyping method was chosen based on the maximum value of the membership function with indicators of 0.3, 0.7, and 1. Thus, the best method for the given task is rapid prototyping, with a membership function value equal to 1. These results have made it possible to solve the problem by evaluating fuzzy preference relations on a set of alternatives. Additionally, a relation convolution was constructed, from which a subset of non-dominated alternatives was identified. The key features of the proposed approach are obtaining clear quantitative evaluation parameters by processing descriptive, non-unified, and non-formalized input data. The fuzzy preference relations for the set of alternatives are explained by the analysis of the most common methods for prototyping interactive systems. The developed information system could be used for managerial decision-making regarding the selection of optimal prototyping option from several possibilities, by comparing methods based on predefined criteria with arbitrary weighting coefficients. The result of the research is a universal system adaptable to specific user tasks. The resulting data will contribute to improving the efficiency of prototyping and, consequently, reducing costs.

Optimizing dry milling of stir-cast and heat-treated AZ80 magnesium alloy using multiple criteria optimization technique: an experimental study.

The primary aspects of this research are to evaluate surface roughness, cutting force, and material removal rate and optimize it with dry milling process parameters for heat-treated and stir-cast AZ80 magnesium alloy. Multiple methodologies are utilized in the research, which includes the Integration of design of experiments-response surface methodology for experimental design with the technique for order of preference by similarity to ideal solution for multi-criteria optimization. In order to evaluate the effect of process parameters on the response, the experimental design manipulates the depth of cut, feed rate, and cutting speed in a systematic manner. An evaluation of the machined surface's quality is conducted via surface roughness measurements. Likewise, insights into the forces exerted during milling can be obtained through continuous monitoring of cutting forces. The calculation of material removal rate is predicated on weight reduction. The interaction between the depth of cut and feed rate has a significant impact on the critical-to-quality characteristics of the alloy, which has contribution percentage greater than 25%. This finding validates that despite the heat-treated alloy having a similar composition to the as-cast alloy (where the closeness coefficient is 0.9843), the optimal process parameters of the former are not applicable to the latter. Nevertheless, the technique used to prepare the specimen has no bearing on the material removal rate, which is a process parameter-specific effect.

Analysis of Influential Parameters in the Dynamic Loading and Stability of the Swing Drive in Hydraulic Excavators

The proper design and configuration of the swing drive mechanism of a hydraulic excavator are crucial to improve energy consumption and efficiency and ensure operational stability. This paper analyzes the influence of the relationship between the parameters of a hydraulic motor and a reducer, which form the integrated transmission of a swing drive, the dynamic characteristics of a hydraulic excavator on loading, and the dynamic stability of the drive. The analysis deals with an excavator model that has the same parameters of the kinematic chain members, the same parameters of the upper structure drive mechanisms, and two variants of the swing drive that, with different integrated transmission parameters, provide the upper structure with the identical number of revolutions and equal rotating moment. One swing drive variant possesses an integrated transmission with a hydraulic motor with a low specific flow and a reducer with a high transmission ratio, while the other drive variant has the opposite parameters. Understanding this relationship is essential for optimizing the design of excavators to achieve better performance and dynamic stability under varying operational conditions. As an example, this paper provides the analysis results regarding the influence of the relationship between the parameters of the integrated transmission hydraulic motor and reducer on the loading and dynamic stability of the swing drive in a tracked hydraulic excavator of 100,000 kg in mass and 4.4 m3 in loading bucket volume, as obtained from the developed dynamic mathematical models of the excavator using the MSC ADAMS program. The results indicate that the dynamic loads on the swing drive’s axial bearing are higher in the variant with a low-specific-flow motor and high transmission ratio reducer during the acceleration and deceleration phases. However, this configuration demonstrated better dynamic stability, with lower oscillation amplitudes and shorter damping times compared to the variant with a high-flow motor and low transmission ratio. Those findings provide valuable criteria for the optimal synthesis of swing drive mechanisms in large hydraulic excavators using multi-criteria optimization methods.

Multi-criteria process optimization for better performance of grinding AISI 1060 hardened steel using different hybrid taguchi-based MCDM methods

Multi-criteria process optimization for better performance of grinding AISI 1060 hardened steel using different hybrid taguchi-based MCDM methods

ІНКРЕМЕНТНИЙ ПІДХІД ДО ПЛАНУВАННЯ РОБОТИ СТУДЕНТІВ НАД СПІЛЬНИМ ПРОГРАМНИМ ПРОЄКТОМ

As of today, well-known product and outsourcing IT companies use Agile models for the software development life cycle (SDLC), the advantages of which include rapid adaptation to changes, continuous feedback, improved product quality, increased team engagement, focus on customer value, transparency and control, as well as the reduction of internal and external risks. All Agile models are iterative, meaning the development process is divided into separate, similar iterations, which have different names in different models. Familiarization with and practical application of modern Agile models by students at the first (bachelor’s) level of higher education in the field of information technology is an important task for future IT specialists. To achieve this task, the most popular Agile model, Scrum, was chosen, which is also incremental, meaning that each iteration delivers additional functionality that is ready for use. The main roles of participants and their responsibilities, artifacts, and ceremonies of the Scrum process are reviewed. The article proposes applying the Scrum model for iterative and incremental software development that will solve a multi-criteria optimization problem using a game-theoretic approach. The formulation and general algorithm for solving the multi-criteria optimization problem using a game-theoretic approach are presented, as well as an example of formulating the Product Backlog, which contains twelve functional requirements to be implemented by the student Scrum team over twelve iterations (sprints). The recommended process for dividing students into Scrum teams and assigning roles to participants is described. An example of incremental implementation based on the Scrum model of the SDLC for solving the multi-criteria optimization problem using a game-theoretic approach is also provided.

Development of a geothermal-driven multi-output scheme for electricity, cooling, and hydrogen production: Techno-economic assessment and genetic algorithm-based optimization

This study aims to develop an environmentally friendly multi-energy system for sustainable production of electricity, cooling and hydrogen. The study introduces a pioneering geothermal-based system, integrating an ejector refrigeration cycle, a dual-loop organic Rankine cycle, and a hydrogen production unit with proton exchange membrane electrolyzers. The study provides a thorough analysis of the system's energy and exergy performance, as well as its economic feasibility. Through sensitivity and parametric analyses, the research identifies key parameters that significantly influence system performance. The system's innovative design promises minimal environmental impact while delivering multifaceted performance: generating 1.38 MW of electricity, supplying 436 kW of cooling load, and producing 5.39 kg/h of hydrogen. In the exergy analysis, Evaporator1 is identified as the primary contributor to exergy loss, representing 34 % of the total exergy destruction. This is followed by the electrolysis unit, the condenser, and the ejector refrigeration cycle, which contribute 18 %, 14 %, and 12 %, respectively. The system achieves optimal efficiency at an organic Rankine cycle turbine1 inlet temperature of 387 K, yielding a power generation of 885.4 kW and an exergy efficiency of 26.7 %. Beyond this temperature, any further increase leads to a decline in power output due to operational disturbances. A multi-criteria optimization using genetic algorithm is applied, resulting in an optimized system with a cost rate of 18.13 $/h and an exergy efficiency of 38.96 %.

Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination

Background and purposeThe ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team’s winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic. Materials and methodsOur planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0–100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases. ResultsOur team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a “Plan Only” phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the “Plan Only” leaderboard. ConclusionsOur automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.

Multi-criteria optimization next to a comparative analysis of a polygeneration layout based on a double-stage gas turbine cycle fueled by biomass and hydrogen

The significance and advantages of utilizing renewable energy sources are widely recognized. Upon examining past studies, it was revealed that there has been a lack of research on harnessing the waste energy from double-stage gas turbines powered by biomass-hydrogen hybrid fuel for polygeneration applications. Thus, a new configuration is proposed in the study for the multi-production of electricity, cooling, heating, and potable water. In the current research, a high-pressure turbine is driven by a combustion chamber fed by a biomass gasifier. Meanwhile, a low-pressure turbine is driven by a post-combustion chamber fed by hydrogen as fuel. The heat loss of the topping gas turbine cycle is recuperated by a supercritical Brayton cycle (SBC), a steam Rankine cycle (SRC), and a water heater. The waste heat of the supercritical Brayton cycle and steam Rankine cycle is recovered through an absorption refrigeration cycle and a thermoelectric generator, respectively. Thus, the heat loss of the topping system is completely recovered. The net power output of the gas turbine cycle is considered the electricity production of the system while the output power of the SBC and SRC is utilized in a reverse osmosis desalination system to produce potable water. The investigations conducted on the system are thermodynamic and exergy-economic assessments and three-objective optimization utilizing 5 gases (i.e., CO2, CO, Nitrogen, Air, and Helium) in the SBC. The final results reveal the superiority of Helium as the supercritical gas, bringing about an exergy efficiency () of 32.11%, a total cost rate () of 103 , a payback period (PP) of 0.159 years, and a specific cost of polygeneration () of 22.03 for the system. Although the levelized cost of products of the configuration is high due to the freshwater production, of the proposed system is higher than the previous systems based on a double-stage gas turbine cycle driven by biomass and hydrogen.

A joint vehicular device scheduling and uncertain resource management scheme for Federated Learning in Internet of Vehicles

Federated learning (FL) offers an effective framework for the efficient process in vehicular edge computing. However, FL encompasses the process of distributing and uploading model parameters, which are inevitably transmitted in a wireless network environment. Some challenges in FL-assisted Internet of Vehicles (IoV) sceneries gradually emerging, such as data heterogeneity, concerned device resources, and unstable communication environment, which necessitate intelligent vehicle selection schemes that accelerate training efficiency. Based on these, we consider a new scenario, specifically an FL-assisted IoV system under uncertain communication conditions, and develop an interval many-objective vehicle selection and bandwidth allocation (IMoVSBA) joint optimization scheme. This scheme takes into account computation latency, energy consumption, server utilization, and data quality, while meeting multi-criteria resource optimization requirements. Among these, server utilization is a new objective designed specifically for this joint optimization problem. For the proposed problem, a novel interval many-objective evolutionary algorithm with individual comprehensive indicator to control the evolution direction (IMaOEACI) is designed. Simulation results demonstrate that this method outperforms other schemes in terms of accuracy, training cost, and server utilization, effectively improving training efficiency in wireless channel environments and reasonably utilizing bandwidth resources. It provides significant scientific value and application potential in the field of the IoVs.

Multi-criteria decision making-based optimization of thermal modeling of electric discharge machining

Multi-criteria decision making-based optimization of thermal modeling of electric discharge machining

Enhancing skimmed milk powder functionalities through multicriteria optimization of outlet drying air temperature and spraying air pressure

This study investigated the effects of spraying air pressure and outlet air temperature on the physicochemical and functional properties of skimmed milk powder. Experimental design methodology showed that higher outlet air temperatures improved dry matter content, gelation ability, and flowability, but strengthened colour, moisture sensitivity, and favoured protein denaturation. Spraying air pressure primarily influenced particle size, affecting powder rheological properties and colour. This study highlights the importance of controlling drying parameters to optimize powder properties. The use of a genetic evolutionary algorithm in the optimization process allowed the simultaneous improvement of several functionalities of skimmed milk powder. The findings of this study can contribute to the development of enhanced powders with tailored properties for specific industrial applications.

Enhanced Hybrid Congestion Mitigation Strategy for ‘6LoWPAN-RPL based patient-centric IoHT’

The Internet of Healthcare Things (IoHT) is rapidly evolving, providing new opportunities to enhance healthcare delivery. However, the resource limitation of connected medical sensing devices leads to congestion, resulting in reduced network performance, delayed data transmission, and loss of critical medical information, which can have significant consequences in healthcare. To address this issue, this paper proposes an Enhanced Hybrid Congestion Mitigation Strategy (EHCMS) for IPv6 over low-power wireless personal area networks (6LoWPAN) and routing protocol for low-Power and lossy networks (RPL) based patient-centric IoHT (PC-IoHT). The EHCMS combines several techniques, including traffic management, network topology optimization, and load balancing, to enhance network performance and reduce congestion. The proposed framework is a hybrid strategy that utilizes resource- and traffic-control mechanisms to alleviate congestion in the 6LoWPAN-RPL-based patient-centric IoHT network. For the resource-control-based approach, a congestion-aware composite objective function is designed using a few congestion-specific routing metrics and formulated as a multi-attribute decision-making problem solved using Grey relational analysis (GRA). In addition, a non-linear multi-criteria optimization problem-based transmission rate adaptation mechanism is contrived as a traffic-control scheme for congestion mitigation. The effectiveness of the proposed EHCMS is evaluated using simulations on the Cooja simulator in the Contiki-3.0 OS and compared with existing congestion-alleviating strategies. The results demonstrate that the proposed framework can significantly reduce congestion in the IoHT network, perform better than existing works, and improve the quality of service. This research paper contributes to the field of IoHT by proposing an effective congestion mitigation strategy that enhances the reliability and performance of the PC-IoHT network, ultimately improving the quality of healthcare delivery.

The model for optimizing cutting parameters for processing products on metal-cutting machines

The paper discusses the issues of multi-criteria optimization of the process of manufacturing products on metal-cutting machines. The efficiency of the optimization process directly depends on its level of detail and the optimal set of targets and control parameters. The change in the structure and properties of a product during its manufacture can be represent-ed in the form of a hierarchical model based on the decomposition of the original goal, which must be achieved through the comprehensive optimization of individual elements of the process. Described herein is a multi-level hierarchical model for optimizing a machining process comprising five levels of control. The following control levels are identified: Technological Process, Processing Stage, Technological Operation, Process Transition, Work Stroke. For each structural element of the model, control parameters, specific optimization criteria are defined, and vector optimiza-tion criteria are formed. The practical implementation of the control model is presented on the example of optimization of the target indicators of the technologi-cal process of the ”Roller” product. Graph of change of object states in process of its manufacturing is pre-sented, optimal values of target indicators and cutting parameters are determined.

Multicriteria Optimization of Stochastic Robust Control of the Tracking System

A multicriteria optimization of stochastic robust control with two degrees of freedom of a tracking system with anisotropic regulators has been developed to increase accuracy and reduce sensitivity to uncertain object parameters. Such objects are located on a moving base, on which sensors for angles, angular velocities and angular accelerations are installed. Improvements in the accuracy of control with two degrees of freedom include closed-loop feedback control and open-loop feedback control through the use of reference and perturbation effects. The multicriteria optimization of the stochastic robust control tracking system with two degrees of freedom with anisotropic controllers is reduced to the iterative solution of a system of four coupled Riccati equations, the Lyapunov equation, and the determination of the anisotropy norm of the system by an expression of a special form, which is numerically solved using the homotopy method, which includes vectorization matrices and iterations according to Newton's method. The objective vector of robust control is calculated in the form of a solution of a vector game, the vector gains of which are direct indicators of the quality that the system should achieve in different modes of its operation. The calculation of the vector gains of this game is related to the simulation of a synthesized system with anisotropic regulators for different modes of operation with different input signals and object parameter values. The solutions of this vector game are calculated on the basis of a set of Pareto-optimal solutions taking into account the binary relations of preferences on the basis of the metaheuristic algorithm of multi-swarm Archimedes optimization. Based on the results of the synthesis of stochastic robust control of a tracking system with two degrees of freedom with anisotropic controllers, it is shown that the use of synthesized controllers made it possible to increase the accuracy of system control, reduce the time of transient processes by 3–5 times, reduce the variance of errors by 2.7 times, and reduce the sensitivity of the system to the change of object parameters compared to typical regulators.