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Discusses a new approach to solving optimal designing and control problems in aircraft gas‐turbine engine components. This approach is a combination of optimal designing problems with optimal control problems, allowing the formation of a single problem of optimal designing of controllable systems. The solving of this problem would involve simultaneous optimization of both design parameters and control laws. Allows the making of technically correct and substantiated decisions, taking into consideration several efficiency criteria for gas‐turbine engine components; a specific feature being the determination of a set of competitive optimal solutions in terms of different efficiency criteria values. Demonstrates the effectiveness of this approach by an example of multicriteria design optimization of a controllable axial flow compressor. Presents the results of a search of compressor blade rows geometrical parameters sets and of compressor stator blades control laws which are Edgeworth‐Pareto optimal for four operating modes. Shows a possibility of increasing compressor efficiency considerably by choosing the most preferable design parameters set and implementing in airborne digital control system a number of control laws optimal for different operating modes.

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.

Purpose — This study proposes an optimal survey design method for multi-day and multi-period panels that maximizes the statistical power of the parameter of interest under the conditions that non-linear changes in response to a policy intervention over time can be expected.

Design/methodology/approach — The proposed method addresses balances among sample size, survey duration for each wave and frequency of observation. Higher-order polynomial changes in the parameter are also addressed, allowing us to calculate optimal sampling designs for non-linear changes in response to a given policy intervention.

Findings — One of the most important findings is that variation structure in the behaviour of interest strongly influences how surveys are designed to maximize statistical power, while the type of policy to be evaluated does not influence it so much. Empirical results done by using German Mobility Panel data indicate that not only are more data collection waves needed, but longer multi-day periods of behavioural observations per wave are needed as well, with the increase in the non-linearity of the changes in response to a policy intervention.

Originality/value — This study extends previous studies on sampling designs for travel diary survey by dealing with statistical relations between sample size, survey duration for each wave, and frequency of observation, and provides the numerical and empirical results to show how the proposed method works.

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.

This work seeks to present the theoretical study considerations and the characteristics of a general design methodology in optimal time for electronic systems using numerical methods and optimal control theory. Through this, the design problem of a system is formulated in terms of optimal control in minimal time.

This general design methodology includes the traditional design strategy (TDS), and the modified traditional design strategy (MTDS), where the model of the system is part of the optimization procedure but an objective function of the optimization process is constructed such as includes the traditional objective function and some penalty functions that feign the model of the system. Many special control functions are introduced artificially to generalize the methodology and produce several design trajectories for the same optimization process – the first and final trajectories correspond to TDS and MTDS, respectively. The combination of these trajectories produce an infinite number of design strategies, some of these are quasi‐optimal in time and only one is optimal in time.

Qualitative and numeric results of this iterative process are generated in a personal computer in a C++ language elaborated with a visual C++ graphic user interface. An algorithm is constructed to form an optimal in time design strategy switching from a MTDS subset to a TDS subset. Results of measured times are analyzed, showing that there is a control input U, such that the objective function is minimized in a minimum time.

These ideas are proposed using method of gradient optimization and special acceleration effect.

The purpose of this paper is to present a tri-optimization approach to optimize design solutions regarding the building shape and envelope properties considering their implications on thermal comfort, visual comfort and building energy consumption (EN). The optimization approach has been applied to obtain the optimal design solutions in five typical cities across all climatic regions of China.

The method comprises a tri-optimization process with nine main steps to optimize the three objectives (thermal comfort, visual comfort and building EN). The design variables considered are four types of building shape (pyramid, rectangular, cylindrical and dome shape) and different envelope properties (insulation thickness [INS] of external walls/roof, window type [WT] and window-to-envelop surface area ratio [WESR]). The optimization is performed by using the Taguchi and constraint limit method.

The results show that the optimal design solutions for all climatic regions favor cylindrical shape and triple-layer low-E glazing window. The highest insulation level of 150 mm is preferred in three climatic regions, and the INS of 90 mm is preferred in the other two climate regions. In total, 10% WESR is preferred in all climatic regions, except the mild region. When the constraint limit of lighting intensity requirement by Leadership in Energy and Environmental Design (LEED) is applied, the rectangular shape building is the optimal solution for those with 10% WESR.

The method proposed in the paper is innovative in that it optimizes three different objectives simultaneously in building design with better accuracy and calculation speed.

Building designers can easily follow the proposed design guide in their practice which effectively bridges the gap between theory and practice. The optimal design solutions can provide a more comfortable living environment and yet less EN, which can help achieve the sustainability requirement of green buildings.

The solutions presented in the paper can serve as a useful guide for practical building designers which creates economic and commercial impact. In addition, the theory and practical examples of the study can be used by building regulators to improve the energy-efficient building design standard in China.

The research is the first attempt that adopts tri-optimization approach to generate the optimal solutions for building shape and envelope design. The tri-optimization approach can be used by building designers to generate satisfactory design solutions from the architectural viewpoint and meanwhile to find combinations of the building shape and envelope properties that lead to design solutions with optimal building performance.

This paper aims to describe main ideas and demonstrates results of the research activities carried out by the authors in the field of optimal design concepts for induction heater for surface hardening. The main goal of the research studies is the application of different optimization methods and numerical finite element method (FEM) codes for field analysis to solve the optimal design problem that is mathematically formulated in terms of the one of the most important optimization criteria for surface hardening technology, e.g. maximum temperature uniformity within the hardening surface layer.

Evolutionary algorithm based on Adaptive Gaussian Process-Assisted Differential Evolution for MEMS Design Optimization (AGDEMO) and alternance method of parametric optimization based on optimal control theory are applied as effective tools for the practice-oriented problem for optimization of induction heater design based on non-linear coupled electromagnetic and temperature field analysis. Different approaches are used for combining FEM codes for interconnected field analysis and optimization algorithms into automated optimization procedure.

Optimization procedures are tested and investigated for optimal design problem solution on the examples of induction hardening of steel cylindrical billet.

Solved problems are based on the design of practical industrial applications. The developed optimization procedures are planned to be applied to the wide range of real-life problems of the optimal design of different electromagnetic devices and systems.

This paper describes main ideas and results of the research activities carried out by the authors in the field of optimal design of induction heaters for hardening based on numerical coupled electromagnetic and temperature field analysis. The implementation of the automated procedure that combines a numerical FEM code for coupled field analysis with an optimization algorithm and its subsequent application for designing induction heaters makes the proposed approach specific and original. This paper also demonstrates that different optimization strategies used (evolutionary algorithm based on AGDEMO and alternance method of optimal control theory) are effective for real-life industrial applications for optimization of induction heaters design.

The purpose of this paper is to present systematic optimal design procedures for the Gough-Stewart platforms used as engineering motion simulators.

Three systematic optimal design procedures are proposed to solve the engineering design problems for the Gough-Stewart platform used as motion simulators. In these systematic optimal design procedures, two contradicting design optimality criteria with good representations of performances of the Gough-Stewart platforms are chosen as the objective functions. In addition, the two objective function optimization problems are solved by using the multi-objective evolutionary algorithms.

In the systematic optimal design procedures, multiple compromised design solutions are found by using Elitist Non-Dominated Sorting Genetic Algorithm version II in the primary design stage, and many candidates can be used in the secondary design stage for higher decisions. Two higher decision methods have been presented to choose the final solutions.

This paper proposes three systematic optimal design procedures to solve the practical design problems of the Gough-Stewart platforms used as motion simulators, which are very important for the engineering designers.

In this paper, on the basis of a previously developed approach to circuit optimization, the main element of which is the control vector that changes the form of the basic equations, the structure of the control vector is determined, which minimizes CPU time.

The circuit optimization process is defined as a controlled dynamic system with a special control vector. This vector serves as the main tool for generalizing the problem of circuit optimization and produces a huge number of different optimization strategies. The task of finding the best optimization strategy that minimizes processor time can be formulated. There is a need to find the optimal structure of the control vector that minimizes processor time. A special function, which is a combination of the Lyapunov function of the optimization process and its time derivative, was proposed to predict the optimal structure of the control vector. The found optimal positions of the switching points of the control vector give a large gain in CPU time in comparison with the traditional approach.

The optimal positions of the switching points of the components of the control vector were calculated. They minimize processor time. Numerical results are obtained for various circuits.

The Lyapunov function, which is one of the main characteristics of any dynamic system, is used to determine the optimal structure of the control vector, which minimizes the time of the circuit optimization process.

Applying discrete linear optimal control to robot manipulators faces two challenging problems, namely nonlinearity and uncertainty. This paper aims to overcome nonlinearity and uncertainty to design the discrete optimal control for electrically driven robot manipulators.

Two novel discrete optimal control approaches are presented. In the first approach, a control-oriented model is applied for the discrete linear quadratic control while modeling error is estimated and compensated by a robust time-delay controller. Instead of the torque control strategy, the voltage control strategy is used for obtaining an optimal control that is free from the manipulator dynamics. In the second approach, a discrete optimal controller is designed by using a particle swarm optimization algorithm.

The first controller can overcome uncertainties, guarantee stability and provide a good tracking performance by using an online optimal algorithm whereas the second controller is an off-line optimal algorithm. The first control approach is verified by stability analysis. A comparison through simulations on a three-link electrically driven robot manipulator shows superiority of the first approach over the second approach. Another comparison shows that the first approach is superior to a bounded torque control approach in the presence of uncertainties.

The originality of this paper is to present two novel optimal control approaches for tracking control of electrically driven robot manipulators with considering the actuator dynamics. The novelty is that the proposed control approaches are free from the robot's model by using the voltage control strategy. The first approach is a novel discrete linear quadratic control design supported by a time-delay uncertainty compensator. The second approach is an off-line optimal design by using the particle swarm optimization.