Decomposition-Based MDSDO for Co-Design of Large-Scale Dynamic Systems

2018 ◽  
Author(s):  
Mohammad Behtash ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Dynamic Systems ◽  
Large Scale ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Strategy ◽  
Design Algorithm ◽  
System Solution ◽  
New Formulation

Conventional sequential methods are not bound to yield optimal solutions for design of physical systems and their corresponding control systems. However, by managing the interactions, combined physical and control system design (co-design) can produce superior optimal results. Existing Co-design methods are practical for moderate-scale systems; whereas, they can be impractical or impossible to use when applied to large-scale systems and consequently may limit our determination of an optimal solution. This work addresses this issue by developing a novel decomposition-based version of a co-design algorithm to optimize such large-scale dynamic systems. The new formulation implements a decomposition-based optimization strategy known as Analytical Target Cascading (ATC) to a co-design method known as Multidisciplinary Dynamic System Design Optimization (MDSDO) of a large-scale dynamic system. In addition, a new consistency measure was also established to manage time-dependent linking variables. Results substantiate the ability of the new formulation in identifying the optimal dynamic system solution.

System Design Method of Complex Product Based on Heterogeneous Language Information Environment

2020 ◽  
Author(s):  
Guan Wang ◽  
Yusheng Liu ◽  
Xiaoping Ye ◽  
Jianjun Zhao
Keyword(s):  
System Design ◽  
Programming Model ◽  
Design Method ◽  
Cloud Model ◽  
Information Environment ◽  
Directional Drilling ◽  
Preference Information ◽  
Performance Constraints ◽  
Multi Objective Programming ◽  
System Solution

Abstract In the product design process, early system design often plays an important role. Although the existing system design methods are very effective, the preference information of different stakeholders and the subjective uncertainty of their existence have not been processed well. Because different stakeholders have different backgrounds and different personalities, the language preferences are expressed differently. Therefore, it is difficult to select a system scheme that meets the performance requirements and is approved by various stakeholders. To solve this problem, this paper proposes a system design method based on heterogeneous language information environment. First, the theoretical system schemes set is constructed through the morphological matrix, and the multi-objective programming model is constructed through the performance indicators to select the system schemes set that optimizes each performance objective and meets the performance constraints. Then, a trapezoidal asymmetric cloud model is used as an intermediate for heterogeneous language information to integrate each stakeholder’s preference information expressed by their favorite linguistic term set to select the best system solution. The effectiveness of the method is verified by a system design example of a horizontal directional drilling machine (HDDM).

scholarly journals Development of Fuzzy Observer Gain Design Algorithm for Ship Path Estimation Based on AIS Data

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Chin-Lin Pen ◽  
Wen-Jer Chang ◽  
Yann-Horng Lin
Keyword(s):  
Dynamic Systems ◽  
Design Method ◽  
Fuzzy Model ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Automatic Identification ◽  
Identification System ◽  
Design Algorithm ◽  
Fuzzy Observer ◽  
Takagi Sugeno

This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations.

scholarly journals A Dynamic Optimization Strategy for the Operation of Large Scale Seawater Reverses Osmosis System

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Aipeng Jiang ◽  
Jian Wang ◽  
Wen Cheng ◽  
Changxin Xing ◽  
Shu Jiangzhou
Keyword(s):  
Finite Element ◽  
Dynamic Model ◽  
Large Scale ◽  
Optimal Solution ◽  
Differential Algebraic Equations ◽  
Optimal Operation ◽  
Algebraic Equations ◽  
Optimization Strategy ◽  
Storage Tanks

In this work, an efficient strategy was proposed for efficient solution of the dynamic model of SWRO system. Since the dynamic model is formulated by a set of differential-algebraic equations, simultaneous strategies based on collocations on finite element were used to transform the DAOP into large scale nonlinear programming problem named Opt2. Then, simulation of RO process and storage tanks was carried element by element and step by step with fixed control variables. All the obtained values of these variables then were used as the initial value for the optimal solution of SWRO system. Finally, in order to accelerate the computing efficiency and at the same time to keep enough accuracy for the solution of Opt2, a simple but efficient finite element refinement rule was used to reduce the scale of Opt2. The proposed strategy was applied to a large scale SWRO system with 8 RO plants and 4 storage tanks as case study. Computing result shows that the proposed strategy is quite effective for optimal operation of the large scale SWRO system; the optimal problem can be successfully solved within decades of iterations and several minutes when load and other operating parameters fluctuate.

System Design for Large-Scale Water Control Based on PLC

2013 ◽  
Vol 846-847 ◽  
pp. 176-179
Author(s):  
Xin Hui Yang
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
System Design ◽  
Large Scale ◽  
Design Method ◽  
Control Process ◽  
Normal Operation ◽  
Water Control ◽  
Actual Control

Traditional algorithms for large-scale water control system need to realize control according to mathematical model which makes the parameters setting difficult. At the same time, certain defects in the control effects reduce the safety and the reliability of the system, thus it is hardly to satisfy the normal operation of the water control system. In order to avoid the defects in traditional algorithms, this paper proposes a design method for large-scale water control system bases on PLC and applies it in the actual control process. The results show that the proposed algorithm can effectively improve the accuracy of the large-scale water control system.

scholarly journals Design of Dynamic Systems Using Surrogate Models of Derivative Functions

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Anand P. Deshmukh ◽  
James T. Allison
Keyword(s):  
Dynamic System ◽  
System Design ◽  
Dynamic Systems ◽  
Surrogate Model ◽  
Time Derivative ◽  
Surrogate Modeling ◽  
The State ◽  
Unique Challenge ◽  
Derivative Function ◽  
Computationally Expensive

Optimization of dynamic systems often requires system simulation. Several important classes of dynamic system models have computationally expensive time derivative functions, resulting in simulations that are significantly slower than real time. This makes design optimization based on these models impractical. An efficient two-loop method, based on surrogate modeling, is presented here for solving dynamic system design problems with computationally expensive derivative functions. A surrogate model is constructed for only the derivative function instead of the simulation response. Simulation is performed based on the computationally inexpensive surrogate derivative function; this strategy preserves the nature of the dynamic system, and improves computational efficiency and accuracy compared to conventional surrogate modeling. The inner-loop optimization problem is solved for a given derivative function surrogate model (DFSM), and the outer loop updates the surrogate model based on optimization results. One unique challenge of this strategy is to ensure surrogate model accuracy in two regions: near the optimal point in the design space, and near the state trajectory in the state space corresponding to the optimal design. The initial evidence of method effectiveness is demonstrated first using two simple design examples, followed by a more detailed wind turbine codesign problem that accounts for aeroelastic effects and simultaneously optimizes physical and control system design. In the last example, a linear state-dependent model is used that requires computationally expensive matrix updates when either state or design variables change. Results indicate an order-of-magnitude reduction in function evaluations when compared to conventional surrogate modeling. The DFSM method is expected to be beneficial only for problems where derivative function evaluation expense, and not large problem dimension, is the primary contributor to solution expense (a restricted but important problem class). The initial studies presented here revealed opportunities for potential further method improvement and deeper investigation.

A Decomposition-Based Optimization Algorithm for Combined Plant and Control Design of Interconnected Dynamic Systems

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Mohammad Behtash ◽  
Michael J. Alexander-Ramos
Keyword(s):  
Dynamic System ◽  
Dynamic Systems ◽  
Optimization Algorithm ◽  
Hybrid Electric Vehicle ◽  
Integrated Design ◽  
Design Methods ◽  
System Level ◽  
Optimal Solutions ◽  
Optimization Strategy ◽  
And Control

Abstract Strong coupling of the physical and control parts within complex dynamic systems should be addressed by integrated design approaches that can manage such interactions. Otherwise, the final solution will be suboptimal or even infeasible. Combined design and control (co-design) methods can tackle this issue by managing the mentioned interactions and can result in superior optimal solutions. Current co-design methods are applicable to simplified non-interconnected systems; however, these methods might be impractical or even impossible to apply to real-world interconnected dynamic systems, hindering designers from obtaining the system-level optimal solutions. This work addresses this issue by developing an optimization algorithm which combines a decomposition-based optimization strategy known as analytical target cascading (ATC) with a co-design-centric formulation of multidisciplinary dynamic system design optimization (MDSDO). Considering the time-dependent linking variables among the dynamic systems’ components, a new consistency measure has also been proposed to manage such quantities in the optimization process. Finally, a plug-in hybrid electric vehicle powertrain, representative of an interconnected dynamic system, has been studied to validate the new algorithm’s results against the conventional all-at-once (AAO) MDSDO. Although the numerical results from the ATC-MDSDO slightly deviate from those in the AAO-MDSDO, this method can play a crucial role as a benchmark when the AAO solution is unattainable or a distributed design paradigm is required.

Optimal Design of Complex Systems Using Decomposition Methods: An Example Application to Electromechanical Control

1988 ◽  
Author(s):  
T. W. McLain ◽  
J. C. Free ◽  
A. Teng
Keyword(s):  
Control System ◽  
System Design ◽  
Dynamic Systems ◽  
Large Scale ◽  
Decomposition Methods ◽  
Optimization Techniques ◽  
Control System Design ◽  
Response Characteristics ◽  
Dc Motors ◽  
Actuator Control

Abstract The utility of the classical optimization and decomposition methods were examined with respect to actuator/control system design. A method of integrating actuator design with control system design to obtain optimal transient response was explored and found to be effective. A test problem consisting of an electromechanical actuator was decomposed and optimized following the strategy proposed by Sobieski [1,2]. A comparison of optimization results from decomposed and undecomposed actuator/control system models was made. The results from the sample problems showed decomposition to be a potentially valuable tool in the optimization of large scale dynamic systems. Methods of discrete optimization techniques to select catalog values for dc motors and gears were examined. In the problems tested, a variation of an exhaustive search was found to be the most reliable method for handling discrete variables since a relatively small number of combinations was involved. A sucessful method for evaluating and optimizing the time response characteristics of dynamic systems was also developed.

scholarly journals A Metaheuristic Algorithm for VLSI Floorplanning Problem

2019 ◽  
Vol 8 (2S5) ◽  
pp. 249-254
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Search Algorithm ◽  
Design Method ◽  
Harmony Search ◽  
Optimal Solution ◽  
Harmony Search Algorithm ◽  
Chip Area ◽  
Optimum Solution ◽  
Vlsi Floorplanning

Floorplanning plays an important role within the physical design method of very large Scale Integrated (VLSI) chips. It’s a necessary design step to estimate the chip area before the optimized placement of digital blocks and their interconnections. Since VLSI floorplanning is an NP-hard problem, several improvement techniques were adopted to find optimal solution. In this paper, a hybrid algorithm which is genetic algorithm combined with music-inspired Harmony Search (HS) algorithm is employed for the fixed die outline constrained floorplanning, with the ultimate aim of reducing the full chip area. Initially, B*-tree is employed to come up with the first floorplan for the given rectangular hard modules and so Harmony Search algorithm is applied in any stages in genetic algorithm to get an optimum solution for the economical floorplan. The experimental results of the HGA algorithm are obtained for the MCNC benchmark circuits

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