A Decentralized Approach for Multi-Subsystem Co-Design Optimization Using Direct Collocation Method

2017 ◽  
Author(s):  
Tianchen Liu ◽  
Shapour Azarm ◽  
Nikhil Chopra
Keyword(s):  
Optimization Methods ◽  
Simultaneous Optimization ◽  
Dual Decomposition ◽  
Design Problems ◽  
Direct Collocation ◽  
Physical Plant ◽  
Finite Dimensional ◽  
Direct Collocation Method ◽  
Grid Points ◽  
And Control

Multi-subsystem co-design refers to the simultaneous optimization of physical plant and controller of a system decomposed into multiple interconnected co-design subsystems. In this paper, a new decentralized approach based on the direct collocation and decomposition-based optimization methods is formulated to solve multi-subsystem co-design problems. First, the problem is decomposed into physical plant and control parts. In the control part, the entire time horizon is discretized into subintervals and grid points. In this way, a continuous optimal control problem is converted into a finite dimensional nonlinear programming (NLP) problem. The optimality condition decomposition (OCD) method is employed to decompose and solve the converted NLP problem in a decentralized manner. Next, the dual decomposition method is used to optimize the plant part. Finally, the plant and control parts are connected by the gradients of Hamiltonian with respect to the plant variables. The proposed approach is applied to two examples. First, a numerical example is presented to illustrate the approach step-by-step. Then in the second example, a spring-mass-damper system is solved. For both examples, the solutions obtained by the proposed decentralized approach are compared against a centralized (all-in-one) approach.

scholarly journals Decentralized Multisubsystem Co-Design Optimization Using Direct Collocation and Decomposition-Based Methods

2020 ◽  
Vol 142 (9) ◽  
Author(s):  
Tianchen Liu ◽  
Shapour Azarm ◽  
Nikhil Chopra
Keyword(s):  
Optimization Problems ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Bilevel Optimization ◽  
Simultaneous Optimization ◽  
Computational Time ◽  
Nonlinear Program ◽  
Design Problems ◽  
Direct Collocation ◽  
Physical Plant

Abstract Multisubsystem co-design refers to the simultaneous optimization of physical plant and controller of a system decomposed into multiple interconnected subsystems. In this paper, two decentralized (multilevel and bilevel) approaches are formulated to solve multisubsystem co-design problems, which are based on the direct collocation and decomposition-based optimization methods. In the multilevel approach, the problem is decomposed into two bilevel optimization problems, one for the physical plant and the other for the control part. In the bilevel approach, the problem is decomposed into subsystem optimization subproblems, with each subproblem having the optimization model for physical plant and control parts together. In both cases, the entire time horizon is discretized to convert the continuous optimal control problem into a finite-dimensional nonlinear program. The optimality condition decomposition method is employed to solve the converted problem in a decentralized manner. Using the proposed approaches, it is possible to obtain an optimal solution for more generalized multisubsystem co-design problems than was previously possible, including those with nonlinear dynamic constraints. The proposed approaches are applied to a numerical and engineering example. For both examples, the solutions obtained by the decentralized approaches are compared with a centralized (all-at-once) approach. Finally, a scalable version of the engineering example is solved to demonstrate that using a simulated parallelization with and without communication delays, the computational time of the proposed decentralized approaches can outperform a centralized approach as the size of the problem increases.

Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 343-351 ◽  
Author(s):  
L. G. Caldas ◽  
L. K. Norford
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hvac Systems ◽  
Design Problems ◽  
Building Walls ◽  
Building Form ◽  
And Control ◽  
Future Work

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

Optimization of block layout design problems with unequal areas: A comparison of MILP and MINLP optimization methods

2005 ◽  
Vol 30 (1) ◽  
pp. 54-69 ◽  
Author(s):  
Ignacio Castillo ◽  
Joakim Westerlund ◽  
Stefan Emet ◽  
Tapio Westerlund
Keyword(s):  
Optimization Methods ◽  
Layout Design ◽  
Design Problems

On Measures of Coupling Between the Artifact and Controller Optimal Design Problems

2009 ◽  
Author(s):  
Diane L. Peters ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy
Keyword(s):  
System Optimization ◽  
Optimization Method ◽  
Ease Of Use ◽  
Control Problems ◽  
Design Problems ◽  
Sensitivity Equations ◽  
Smart Products ◽  
Two Measures ◽  
And Control ◽  
Artifact Design

Optimization of smart products requires optimizing both the artifact design and its controller. The presence of coupling between the design and control problems is an important consideration in choosing the system optimization method. Several measures of coupling have been proposed based on different viewpoints of the system. In this paper, two measures of coupling, a vector based on optimality conditions and a matrix derived from an extension of the global sensitivity equations, are shown to be related under certain conditions and to be consistent in their coupling determination. The measures’ physical interpretation and relative ease of use are discussed using the example of a positioning gantry. A further relation is derived between one measure and a modified sequential formulation that would give results sufficiently close to the true solutions.

Supporting Co-Design of Physical and Control Architectures of Mechatronic Systems

2011 ◽  
Author(s):  
Andre´s A. Alvarez Cabrera ◽  
Hitoshi Komoto ◽  
Tetsuo Tomiyama
Keyword(s):  
Control Structure ◽  
Mutual Influence ◽  
Early Stage ◽  
Physical Structure ◽  
Mechatronic Systems ◽  
Design Problems ◽  
Software And Hardware ◽  
Design Integration ◽  
And Control ◽  
Recent Tendency

There is a rather recent tendency to define the physical structure and the control structure of a system concurrently when designing the architecture of a product, i.e., to perform codesign. We argue that co-design can only be enabled when the mutual influence between physical system and control is made evident to the designer at an early stage. Though the idea of design integration is not new, to the best of our knowledge, there is no computer tooling that explicitly supports this activity by enabling co-design as stated before. In this paper the authors propose a method for co-design of physical and control architectures as a better approach to design mechatronic systems, allowing to exploit the synergy between software and hardware and detecting certain design problems at an early stage of design. The proposed approach is supported by a set of tools and demonstrated through an example case.

Simultaneous Optimization of Configuration and Control for a Passive SCR System

2018 ◽  
Author(s):  
Pingen Chen ◽  
Qinghua Lin
Keyword(s):  
Catalytic Reduction ◽  
Simultaneous Optimization ◽  
Computationally Efficient ◽  
System Architectures ◽  
Aftertreatment System ◽  
In Series ◽  
System Configurations ◽  
Scr System ◽  
And Control ◽  
Passive Scr

The configuration and control of aftertreatment systems have a significant impact on their functionalities and emission control performance. The traditional aftertreatment system configurations, i.e., connections from one aftertreatment subsystem to another subsystem in series, are simple but generally do not yield the optimal aftertreatment system performance. New aftertreatment configurations, in conjunction with new engine and aftertreatment control, can significantly improve engine efficiency and emission reduction performance. However, new configuration design requires human intuition and in-depth knowledge of engine and aftertreatment system design and control. The purpose of this study is to develop a general systematic and computationally-efficient method which enables automated and simultaneous optimization of passive selective catalytic reduction (SCR) system architectures and the associated non-uniform cylinder-to-cylinder combustion (NUCCC) controls based on a newly proposed highly reconfigurable passive SCR model structure and integer partition theory. The proposed method is general enough to account for passive SCR systems with two or more TWC stages. We demonstrate through this case study that the optimized passive SCR configuration, in conjunction with the optimized NUCCC control, can reduce the NH3 specific fuel consumption by up to 21.90%.

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.

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