Control Proxy Functions for Sequential Design and Control Optimization

2011 ◽  
Vol 133 (9) ◽  
Author(s):  
Diane L. Peters ◽  
P. Y. Papalambros ◽  
A. G. Ulsoy
Keyword(s):  
Simultaneous Optimization ◽  
Sequential Optimization ◽  
Control Optimization ◽  
Smart Products ◽  
System Solution ◽  
Optimal System Design ◽  
Problem Formulations ◽  
And Control ◽  
Sequential Strategy ◽  
Selection Of

Optimal system design of “smart” products requires optimization of both the artifact and its controller. When the artifact and the controller designs are independent, the system solution is straightforward through sequential optimization. When the designs are coupled, combined simultaneous optimization can produce system-optimal results, but presents significant computational and organizational complexity. This paper presents a method that produces results comparable with those found with a simultaneous solution strategy, but with the simplicity of the sequential strategy. The artifact objective function is augmented by a control proxy function (CPF), representing the artifact’s ease of control. The key to successful use of this method is the selection of an appropriate CPF. Four theorems that govern the choice and evaluation of a CPF are given. Each theorem is illustrated using a simple mathematical example. Specific CPFs are then presented for particular problem formulations, and the method is applied to the optimal design and control of a micro-electrical mechanical system actuator.

scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
And Control ◽  
Selection Of

Abstract. Design of an optimal wind farm topology and wind farm control scheduling depends on the chosen metric. The objective of this paper is to investigate the influence of optimal wind farm control on the optimal wind farm layout in terms of power production. A successful fulfilment of this goal requires: 1) an accurate and fast flow model; 2) selection of the minimum set of design parameters that rules the problem; and 3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is a priori limited to wind turbine derating. A high-speed linearized CFD RANS solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disk method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space spanned by these (2N + Nd Ns N) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed in two subsets, which in turn define a nested set of optimization problems to achieve the fastest possible optimization procedure. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capabilities of the developed optimization platform is demonstrated on the Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control in the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

scholarly journals Integrated wind farm layout and control optimization

2020 ◽  
Vol 5 (4) ◽  
pp. 1551-1566
Author(s):  
Mads M. Pedersen ◽  
Gunner C. Larsen
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Design Space ◽  
Wind Farm Layout ◽  
Control Optimization ◽  
Mean Wind Speed ◽  
The Individual ◽  
Full Design ◽  
And Control ◽  
Selection Of

Abstract. The objective of this paper is to investigate the joint optimization of wind farm layout and wind farm control in terms of power production. A successful fulfilment of this goal requires the following: (1) an accurate and fast flow model, (2) selection of the minimum set of design parameters that rules or governs the problem, and (3) selection of an optimization algorithm with good scaling properties. For control of the individual wind farm turbines with the aim of wind farm production optimization, the two most obvious strategies are wake steering based on active wind turbine yaw control and wind turbine derating. The present investigation is limited to wind turbine derating. A high-speed linearized computational fluid dynamics (CFD) Reynolds-averaged Navier–Stokes (RANS) solver models the flow field and the crucial wind turbine wake interactions inside the wind farm. The actuator disc method is used to model the wind turbines, and utilizing an aerodynamic model, the design space of the optimization problem is reduced to only three variables per turbine – two geometric and one carefully selected variable specifying the individual wind turbine derating setting for each mean wind speed and direction. The full design space is spanned by these (2N+NdNsN) parameters, where N is the number of wind farm turbines, Nd is the number of direction bins, and Ns is the number of mean wind speed bins. This design space is decomposed into two subsets, which in turn define a nested set of optimization problems to achieve a significantly faster optimization procedure compared to a direct optimization based on the full design space. Following a simplistic sanity check of the platform functionality regarding wind farm layout and control optimization, the capability of the developed optimization platform is demonstrated on a Swedish offshore wind farm. For this particular wind farm, the analysis demonstrates that the expected annual energy production can be increased by 4 % by integrating the wind farm control into the design of the wind farm layout, which is 1.2 % higher than what is achieved by optimizing the layout only.

Nested Optimization of an Elevator and Its Gain-Scheduled LQG Controller

2002 ◽  
Author(s):  
Hosam K. Fathy ◽  
Scott A. Bortoff ◽  
G. Scott Copeland ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
Ride Comfort ◽  
Optimization Problems ◽  
Torque Ripple ◽  
Guide Rail ◽  
Control Optimization ◽  
Drive Motor ◽  
Rail Irregularity ◽  
Optimal System Design ◽  
And Control ◽  
Gain Scheduled

This paper studies the combined optimization of an elevator’s design (plant) and LQG controller for ride comfort. Elevator dynamics and primary vibration sources (drive motor torque ripple and guide rail irregularity) are modeled using an object-oriented language. The resulting model is nonlinear. Elevator vibrations are minimized with respect to both the design and the LQG controller. LQG gains are scheduled versus cab mass and height for robustness. Sequential plant/control optimization produces an optimal ride only when the torque ripple is the dominant disturbance. Otherwise, passive vibration reduction decreases the controller’s authority over the vibrations, hence coupling the plant and control optimization problems. Combined plant/controller optimization, using a nested strategy, mitigates this coupling and finds the correct optimal system design.

Freezing Degrees of Freedom During Motor Learning: A Systematic Review

Motor Control ◽  
2020 ◽  
Vol 24 (3) ◽  
pp. 457-471 ◽  
Author(s):  
Anderson Nascimento Guimarães ◽  
Herbert Ugrinowitsch ◽  
Juliana Bayeux Dascal ◽  
Alessandra Beggiato Porto ◽  
Victor Hugo Alves Okazaki
Keyword(s):  
Motor Learning ◽  
Control Strategy ◽  
Degrees Of Freedom ◽  
Control Strategies ◽  
System Solution ◽  
Dart Throwing ◽  
The Central Nervous System ◽  
Enormous Variation ◽  
And Control ◽  
Selection Of

According to Bernstein, the central nervous system solution to the human body’s enormous variation in movement choice and control when directing movement—the problem of degrees of freedom (DF)—is to freeze the number of possibilities at the beginning of motor learning. However, different strategies of freezing DF are observed in literature, and the means of selection of the control strategy during learning is not totally clear. This review investigated the possible effects of the class and objectives of the skill practiced on DF control strategies. The results of this review suggest that freezing or releasing the DF at the beginning of learning does not depend on the class (e.g., discrete skill class: football kick, dart throwing; continuous skill class: athletic march, handwriting) or objective of the skill (e.g., balance, velocity, and accuracy), in isolation. However, an interaction between these two skill elements seems to exist and influences the selection of the DF control strategy.

MODEL OF SOCIAL POTENTIAL OF CHILDHOOD IN THE RUSSIAN REGIONS: THE RESULTS OF EXPERIMENTS

2019 ◽  
pp. 152-162
Author(s):  
A.G. Filipova ◽  
A.V. Vysotskaya
Keyword(s):  
Third Wave ◽  
Russian Regions ◽  
The Third ◽  
Control Factors ◽  
Social Potential ◽  
System Input ◽  
Optimal Values ◽  
Fixed Ideal ◽  
And Control ◽  
Selection Of

The article presents the results of mathematical experiments with the system «Social potential of childhood in the Russian regions». In the structure of system divided into three subsystems – the «Reproduction of children in the region», «Children’s health» and «Education of children», for each defined its target factor (output parameter). The groups of infrastructure factors (education, health, culture and sport, transport), socio-economic, territorial-settlement, demographic and en-vironmental factors are designated as the factors that control the system (input parameters). The aim of the study is to build a model îf «Social potential of childhood in the Russian regions», as well as to conduct experiments to find the optimal ratio of the values of target and control factors. Three waves of experiments were conducted. The first wave is related to the analysis of the dynam-ics of indicators for 6 years. The second – with the selection of optimal values of control factors at fixed ideal values of target factors. The third wave allowed us to calculate the values of the target factors based on the selected optimal values of the control factors of the previous wave.

scholarly journals Fuel metering and control optimization based on combustion state diagnosis

2020 ◽  
Vol 594 ◽  
pp. 012013
Author(s):  
X H Wang ◽  
H T Chen ◽  
X X Zhu ◽  
J L Zhang ◽  
W L Liu ◽  
...  
Keyword(s):  
Control Optimization ◽  
And Control ◽  
State Diagnosis
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