ࡱ> "W ^bjbjJJD(a(a*&nn)))8a )":#"%#%#%#$$$  $أJ/$$$$$/%#%#...$v%#%# .$ ..L1%#3L6v'^ٍZơ03ئ'ئ11ئ- $$.$$$$$//,$$$$$$$ئ$$$$$$$$$nm : NATIONAL UNIVERSITY OF LIFE AND ENVIRONMENTAL SCIENCES OF UKRAINE Machines and equipment design department APPROVED Faculty of design and engineering 10 June 2025 CURRICULUM OF ACADEMIC DISCIPLINE MODERN METHODS OF OPTIMIZATION OF MACHINES OF AGRICULTURAL PRODUCTION (title) Field of knowledge: G "Engineering, production and construction" Specialty: G11 "Mechanical Engineering (by specializations)" Academic programme "Machines and equipment of agricultural production" Orientation of the program: educational and scientific Faculty of design and engineering Developers: doctor of technical sciences, professor Romasevych Y.O., doctor of technical sciences, professor Loveikin V.S. Kyiv 2025 Description of the discipline MODERN METHODS OF OPTIMIZATION OF MACHINES OF AGRICULTURAL PRODUCTION (title) Studying the course is crucial for developing advanced, efficient, and intelligent machines applications. Optimization lies at the heart of machines, enabling tasks such as motion planning, control, sensor fusion, and machine learning integration. This course equips students with the tools to model and solve complex problems using techniques like convex optimization, evolutionary algorithms, and real-time decision-making strategies. As robotic systems become more autonomous and adaptive, mastering these modern optimization methods ensures better performance, resource efficiency, and safety. Furthermore, this knowledge is key to innovation in fields like autonomous vehicles, industrial automation, and medical machines, where optimal solutions are essential for reliability and competitiveness in rapidly evolving technological environments. Area of knowledge, specialty, academic programme, academic degreeAcademic degree MasterSpecialty G11 Mechanical Engineering (by specializations)Academic programmeMachines and equipment of agricultural productionCharacteristics of the disciplineTypeSelectiveGeneral volume of hours90Number of credits ECTS 3Number of modules2Course project-Control formExamIndicators of the discipline for full-time forms of university studyYear of study1Term2Lectures16 h.Practical classes and seminars-Laboratory classes16 h.Self-study58 h.Number of hours per week for full-time students2 h. 1. Aim, competences and expected learning outcomes of the discipline The aim of the discipline is to acquaint students with modern optimization methods and their application in various areas of mechanical engineering. The training course is aimed at forming in students a deep understanding of the theoretical aspects of optimization, as well as practical skills for their implementation. Tasks of the discipline: review of modern optimization methods (genetic algorithms, simulation modeling, direct search methods, gradient-oriented methods, etc.), determination of the main advantages and disadvantages of each method; optimization application models for solving optimization problems of industrial mechanical engineering (synthesis of optimal laws of movement of forklifts and robotic systems, automatic control systems, training of artificial neural networks, etc.). Competences acquired: integral competence: the ability to solve complex tasks and problems of industrial mechanical engineering, which involve research and/or innovation and are characterized by uncertainty of conditions and requirements. general competences (GC): GC2. Ability to learn and master modern knowledge. GC 4. Ability to be critical and self-critical. GC 6. Ability to generate new ideas (creativity). special (professional) competencies (SC): SC3. Ability to create new equipment and technologies in the field of mechanical engineering. SC4. Awareness of the promising tasks of modern production aimed at meeting the needs of consumers, mastery of trends in innovative development of technologies in the industry. SC7. Ability to use intelligent technologies to ensure the sustainable development of robotic systems of agricultural production. Expecting learning outcomes (ELO): ELO2. Knowledge and understanding of mechanics and mechanical engineering and the prospects for their development. ELO4. Perform engineering calculations to solve complex tasks and practical problems in the field of mechanical engineering. ELO6. Find the necessary scientific and technical information in available sources, in particular, in a foreign language, analyze and evaluate it. Programme and structure of the discipline Modules and topicsNumber of hoursfull-timetotalincludinglplabinds.stweeksModule 1. Methods of solving optimization of machines problemsTopic 1. General characteristics of optimization methods. Features of optimization methods42-2--1-2Topic 2. PSO optimization method142-2-103-4Topic 3. DE optimization method142-2-105-6Topic 4. GWO optimization method132-2-97-8Total for module 1458-8-29-Module 2. Problems of optimization of machinesTopic 5. Synthesis of optimal motion control systems162-4-109-11Topic6. Synthesis of optimal movement trajectories162-4-1012-14Topic 7. Optimal setting of frequency-controlled drives134---915Total for module 3458-8-29-Total hours9016-16-58- 3. Topics of lectures !TopicHours1General characteristics of optimization methods. Features of optimization methods22PSO optimization method23DE optimization method24GWO optimization method25Synthesis of optimal motion control systems26Synthesis of optimal movement trajectories27Optimal setting of frequency-controlled drives4 4. Topic of laboratory (practical, seminars) classes !TopicHours1Study of topological characteristics of objective functions of optimization robotics problem22Study of the influence of parameters of swarm dimension and the number of iterations on the efficiency of the PSO algorithm23Investigation of search properties of the DE differential evolution method and its modifications24A study of the search properties of the GWO gray wolf method25Optimum setting of the PI controller of the motion of the machine link46Planning the optimal movement trajectory of the gripping body of the robot manipulation system4 Topics of self-study !TopicHours1Modifications of the PSO method102Modifications of the DE method103Modifications of the GWO method94Types of restrictions and criteria for optimal control of robot movement105Approaches to solving the problem of synthesis of optimal robot movement trajectories106Mathematical models of frequency-controlled drives and optional settings of frequency converters9 6. Methods of assessing expected learning outcomes: When teaching this discipline, the following diagnostic tools are used: oral interview; exam; module tests; defense of laboratory work. 7. Teaching methods: When teaching this discipline, the following methods are used: problem-based learning method; practice-oriented learning method; research-based learning method; educational discussions and debates method; teamwork and brainstorming method. Results assessment The knowledge of a higher education applicant is assessed on a 100-point scale, which is translated into a national assessment in accordance with the current "Regulations on Examinations and Tests at the Vlog of Ukraine." Distribution of points by types of educational activities Type of training activitiesResults teachingEvaluationModule 1. Methods of solving optimization of machines problemsLab 1ELO2, ELO6, ELO8. To know the main ideas of metaheuristics. To be able to apply them to find minimum or maximum of an objective function, to be able to set proper setting of optimization algorithms.10Lab 210Self-study work 110Lab 310Self-study work 210Lab 410Self-study work 310Module 1 test-30Overall on 1st module-100Module 2. Problems of optimization of machinesLab 5ELO4, ELO6, ELO8. To know how to reduce a machines problem to the problem of optimization. To be able to select proper algorithm to solve it. 15Self-study work 415Lab 615Self-study work 515Self-study work 610Module 2nd test-30Overall on 2nd module-100Class work-(1+2)/2*0,7 d"70Exam-30Overall-(Class work+Exam)d"100 8.2 Scale for assessing students knowledge Students rating, pointsNational grading (exam/credits)90-100excellent74-89good60-73satisfactory0-59unsatisfactory 8.3 Assessment policy Deadlines and exam retaking rules works that are submitted late without valid reasons will be assessed with a lower grade. Module tests may be retaken with the permission of the lecturer if there are valid reasons (e.g. a sick leave). Academic integrity rulescheating during tests and exams is prohibited (including using mobile devices). Term papers and essays must have correct references to the literature usedAttendance rulesattendance is compulsory. For good reasons (e.g. illness, international internship), training can take place individually (online by the faculty deans consent) Teaching and learning aids Course on E-learn: https://elearn.nubip.edu.ua/course/view.php?id=5499 lecture notes and their presentations (in electronic form); methodological materials for studying the academic discipline for higher education students. 10. Recommended sources of information . . 04VT20. 5B>48 >?B8<V70FVW B0 ?>HC:C >?B8<0;L=8E @VH5=L. 02G0;L=89 ?>AV1=8:.  V<. 3>@O !V:>@AL:>3>. 2023. - 73 A. BCs|  ) 5 L ˿٨~m]P@h^h^5CJ aJmH sH h^5CJ aJ mH sH hJ!Jh{5CJ aJ mH sH !h! U6CJ$OJQJ^JmH sH  h! 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C7=TF>2, .. @840;L=89 "5>@VO B5E=VG=8E A8AB5< 2 0A?5:B0E 4>A;V465=L B0 B5E=VG=>W B2>@G>ABV. ( CFL:: 560-@C:, 2023.  284 A. .. 02@8;5=:>. 5B>48 G8A5;L=>W >?B8<V70FVW 2 B5E=VG=8E A8AB5<0E.  8W2: 0C:>20 4C<:0, 2021.  212 A. R./ Ghabcheloo, A./ M./ Pascoal. Motion Optimization and Control of Single and Multiple Autonomous Aerial, Land, and Marine Robots.  Basel: MDPI, 2023.  348/ p. D./ Richiedei, P./ Boscariol. Optimization of Motion Planning and Control for Automatic Machines, Robots and Multibody Systems.  Basel: MDPI, 2020.  266/ p. A./ T./ Azar, A./ J./ Humaidi, A./ K./ Al Mhdawi. Modeling, Optimization and Control of Robotic Systems.  Basel: MDPI, 2024.  224/ p. 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