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The purpose of the paper is to analyze the active suppression of the aeroelastic vibrations of ailerons with strongly nonlinear characteristics by neural network/reinforcement learning (NN/RL) control method and comparing it with the classic robust methods of suppression.

The flexible wing and aileron with hysteresis nonlinearity is treated as a plant-controller system and NN/RL and robust controller are used to suppress the nonlinear aeroelastic vibrations of aileron. The simulation approach is used for analyzing the efficiency of both types of methods in suppressing of such vibrations.

The analysis shows that the NN/RL controller is able to suppress the nonlinear vibrations of aileron much better than linear robust method, although its efficiency depends essentially on the NN topology as well as on the RL strategy.

Only numerical analysis was carried out; thus, the proposed solution is of theoretical value, and its application to the real suppression of aeroelastic vibrations requires further research.

The work shows the NN/RL method has a great potential in improving suppression of highly nonlinear aeroelastic vibrations, opposed to the classical robust methods that probably reach their limits in this area.

The work raises the questions of controllability of the highly nonlinear aeroelastic systems by means of classical robust and NN/RL methods of control.

The purpose of this paper is to search the optimal arrangement scheme of random motion accuracy of joints for optimal synthesis of pose repeatability which can make robot design more reasonable and reduce the development cost of robots.

In this paper, a mathematical model of pose repeatability, which includes positioning repeatability and orientation repeatability of robots, is established. According to the ISO 9283 standard, an optimal synthesis method of pose repeatability for collaborative robots is introduced, and three optimization objective functions are proposed. The optimization model is solved by using numerical analysis software, and the optimal arrangement scheme of random motion accuracy of joints is obtained which meets the requirements of pose repeatability of robot.

It is found that, in three optimization objective functions, the single-objective evaluation function of maximization of joint motion error is more suitable for optimal synthesis of pose repeatability. In practice, due to the safety factor, the test results of pose repeatability are better than the results of optimal synthesis of pose repeatability.

This method makes robot design more reasonable and reduces the development cost of robots.

This work is the first time to optimize the orientation repeatability of collaborative robots. Because the pose repeatability of most robots is tested by the ISO 9283 standard, so this method which is based on this standard is more suitable for the performance requirements of robot products.

The paper aims to present a learning environment for optimal synthesis of voltage regulator circuits (LEOS‐VRC) using PSPICE simulator.

LEOS‐VRC supports a database with voltage regulator circuits edited as projects in PSPICE compatible format and a methodology for optimal synthesis. The methodology is based on the estimation of multiple voltage regulator circuits' realizations over a given specification, through comparative study in PSPICE, using a set of predefined specific electrical characteristics, which values are determined from simulation waveforms. LEOS‐VRC allows integrating the voltage regulator circuit in a power supply system through adding transformer, rectifier and control stages. Both linear and switch‐mode power supplies are considered.

The methodology and examples proposed illustrate the efficiency of LEOS‐VRC for teaching and self‐education in the area of power supply circuit design.

In future LEOS‐VRC database will be enlarged with new voltage regulator circuit topologies and new controller circuits.

LEOS‐VRC is suitable for students in electronics and designers of power supply circuits.

With LEOS‐VRC students become familiar with multisolution synthesis. By analyzing the complex behaviour of the power supply system and applying comparative study and optimization criteria, they can make a motivated selection of an optimal voltage regulator design solution for a concrete application.

This study is aimed to develop a novel intuitionistic fuzzy P-graph with Gaussian membership function to help decision-makers deal with complex process network systems.

Two fuzzy P-graph case studies of the cogeneration system were selected, and relevant data were collected, including the structure and flow sequence of the system, and the rate of material and product transitions between the operating units. Gaussian function membership was set according to the restriction of fuzzy upper and lower bounds. Then the α-cut was used to obtain different upper and lower bound restrictions of each membership degree. After finding the optimal and suboptimal solutions for different membership degrees, the results of non-membership and hesitation were calculated.

The proposed method will help the decision maker consider the risk and provide more feasible solutions to choose the optimal and suboptimal solutions based on their own or through experience. The proposed model in this study has more flexibility in operation and decision making.

This study is the first to propose a novel intuitive fuzzy P-graph and demonstrates the effectiveness and flexibility of the method by two case studies of the cogeneration system. However, the addition of hesitation can increase the error tolerance of the system. Even for the solutions with a high degree of membership, optimal and suboptimal solutions still exist for the decision maker to select. Since decision makers expect the higher achievement of the target requirements; thus, it is important to have more feasible solutions with a high degree of membership.

The purpose of this paper is to present a state‐of‐the‐art review of dimensional tolerance synthesis and to demonstrate the evolution of tolerance synthesis from product to process‐oriented strategy, as well as to compare the same for single stage and multistage manufacturing systems (MMS). The main focus is in delineating the different approaches, methods and techniques used with critical appraisal of their uses, applicability and limitations, based on which future research directions and a generic methodology are proposed.

Starting with issues in tolerancing research, the review demonstrates the critical aspects of product and process‐oriented tolerance synthesis. The aspects considered are: construction of tolerance design functions; construction of optimization functions; and use of optimization methods. In describing the issues of process‐oriented tolerance synthesis, a comparative study of single and multistage manufacturing has been provided.

This study critically reviews: the relationship between the tolerance variables and the variations created through manufacturing operations; objective functions for tolerance synthesis; and suitable optimization methods based upon the nature of the tolerance variables and the design functions created.

This study is limited to dimensional tolerance synthesis problems and evolution of process‐oriented tolerance synthesis to counteract dimensional variation problems in assembly manufacturing.

The paper provides a comprehensive and step‐by‐step approach of review of dimensional tolerance synthesis.

This paper aims to present the optimal design procedure of a symmetrical 2-DOF parallel planar robot with flexible joints by considering several performance criteria based on the workspace size, dynamic dexterity and energy of the control.

Consequently, the optimal design consists in determining the dimensional parameters to maximize the size of the workspace, maximize the dynamic dexterity and minimize the energy of the control action. The design criteria are derived from the kinematics, dynamics, elastodynamics and the position control law of the robot. The analysis of the design criteria is performed by means of the design space and atlases.

Finally, the multi-objective design optimization derived from the optimal design procedure is solved by using multi-objective genetic algorithms, and the results are analyzed to assess the validity of the proposed approach.

An alternative approach to the design of a planar parallel robot with flexible joints that permits determining the structural parameters by considering kinematic, dynamic and control operational performance.

Introduces papers from this area of expertise from the ISEF 1999 Proceedings. States the goal herein is one of identifying devices or systems able to provide prescribed performance. Notes that 18 papers from the Symposium are grouped in the area of automated optimal design. Describes the main challenges that condition computational electromagnetism’s future development. Concludes by itemizing the range of applications from small activators to optimization of induction heating systems in this third chapter.

The purpose of this paper is to develop the multi-objective optimization approach and its numerical implementation to synthesise the model-base control for the part curing at autoclave processing, which supplies the stability and uniformity of the structure and mechanical properties of the material within the cured composite part.

The approach includes conversion of the cured part and mold geometry from their computer-aided design (CAD) to computer-aided engineering (CAE) representation, a finite element (FE) formulation of the coupled forward heat transfer/thermal kinetic problem with the parameters of prepreg, which should be determined by the thermal analysis, and, finally, a mapping of the area of 4D design space (thermal control parameters) to 2D objective space, whose coordinates are the maximum deviations of degree of cure and temperature within the cured part calculated at each call of the FE model.

The present modeling and optimization approach to the cure process control of the prepreg with thermosetting resin, as well as the means of visualizing optimization results, allow providing insight into complex curing phenomena, estimating the best achievable quality indicators of manufactured composite parts, finding satisfactory parameters of the control law and deciding considering all manufacturing constraints.

The research can be effectively used to optimize the cure process control for a wide class of polymeric composite parts, even with a complex geometry, but it requires the exact conversion of the geometry of the modeled part from the CAD to CAE environment, which implies the need for excluding all topological imperfections of original CAD model to eliminate the possible formation of void elements and other reasons that do not allow the correct FE meshing. Because thermal, rheological and kinetics parameters, which include the governing equations of cure process, depend on the reinforcing fibers, and especially on the resin properties, the thermal testing for the new modeled prepreg needs to be performed.

Computer implementation of the proposed approach and numerical method for model-based optimal control synthesis for composite part cure process can be used in aircraft, rotorcraft, ship and automotive technologies at the design of manufacturing process of the large composite parts with complex shape.

This will allow much better quality for large-scale composite parts, excluding very expensive, time-, energy- and material-consuming multiple cure process testing.

This is first time the problem of optimal control synthesis for curing the large-scale composite parts of complex shape was solved.

The purpose of this paper is to present a novel Kriging meta-model assisted method for multi-objective optimal tolerance design of the mechanical assemblies based on the operating conditions under both systematic and random uncertainties.

In the proposed method, the performance, the quality loss and the manufacturing cost issues are formulated as the main criteria in terms of systematic and random uncertainties. To investigate the mechanical assembly under the operating conditions, the behavior of the assembly can be simulated based on the finite element analysis (FEA). The objective functions in terms of uncertainties at the operating conditions can be modeled through the Kriging-based metamodeling based on the obtained results from the FEA simulations. Then, the optimal tolerance allocation procedure is formulated as a multi-objective optimization framework. For solving the multi conflicting objectives optimization problem, the multi-objective particle swarm optimization method is used. Then, a Shannon’s entropy-based TOPSIS is used for selection of the best tolerances from the optimal Pareto solutions.

The proposed method can be used for optimal tolerance design of mechanical assemblies in the operating conditions with including both random and systematic uncertainties. To reach an accurate model of the design function at the operating conditions, the Kriging meta-modeling is used. The efficiency of the proposed method by considering a case study is illustrated and the method is verified by comparison to a conventional tolerance allocation method. The obtained results show that using the proposed method can lead to the product with a more robust efficiency in the performance and a higher quality in comparing to the conventional results.

The proposed method is limited to the dimensional tolerances of components with the normal distribution.

The proposed method is practically easy to be automated for computer-aided tolerance design in industrial applications.

In conventional approaches, regardless of systematic and random uncertainties due to operating conditions, tolerances are allocated based on the assembly conditions. As uncertainties can significantly affect the system’s performance at operating conditions, tolerance allocation without including these effects may be inefficient. This paper aims to fill this gap in the literature by considering both systematic and random uncertainties for multi-objective optimal tolerance design of mechanical assemblies under operating conditions.

Optimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.

Design of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.

In the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.

The originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.