A Problem Class With Combined Architecture, Plant, and Control Design Applied to Vehicle Suspensions

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Daniel R. Herber ◽  
James T. Allison

Here we describe a problem class with combined architecture, plant, and control design for dynamic engineering systems. The design problem class is characterized by architectures comprised of linear physical elements and nested co-design optimization problems employing linear-quadratic dynamic optimization. The select problem class leverages a number of existing theory and tools and is particularly effective due to the symbiosis between labeled graph representations of architectures, dynamic models constructed from linear physical elements, linear-quadratic dynamic optimization, and the nested co-design solution strategy. A vehicle suspension case study is investigated and a specifically constructed architecture, plant, and control design problem is described. The result was the automated generation and co-design problem evaluation of 4374 unique suspension architectures. The results demonstrate that changes to the vehicle suspension architecture can result in improved performance, but at the cost of increased mechanical complexity. Furthermore, the case study highlights a number of challenges associated with finding solutions to the considered class of design problems. One such challenge is the requirement to use simplified design problem elements/models; thus, the goal of these early-stage studies are to identify new architectures that are worth investigating more deeply. The results of higher-fidelity studies on a subset of high-performance architectures can then be used to select a final system architecture. In many aspects, the described problem class is the simplest case applicable to graph-representable, dynamic engineering systems.

Author(s):  
Daniel R. Herber ◽  
James T. Allison

Here we describe a problem class with combined architecture, plant, and control design for dynamic engineering systems. The design problem class is characterized by architectures comprised of linear physical elements and nested co-design optimization problems employing linear-quadratic dynamic optimization. The select problem class leverages a number of existing theory and tools and is particularly attractive due to the symbiosis between labeled graph representations of architectures, dynamic models constructed from linear physical elements, linear-quadratic dynamic optimization, and the nested co-design solution strategy. A vehicle suspension case study is investigated and a specifically constructed architecture, plant, and control design problem is described. The result was the automated generation and co-design problem evaluation of 4,374 unique suspension architectures. The results demonstrate that changes to the vehicle suspension architecture can result in improved performance, but at the cost of increased mechanical complexity. Furthermore, the case study highlights a number of challenges associated with finding solutions to the considered class of design problems.


2011 ◽  
Vol 403-408 ◽  
pp. 3758-3762
Author(s):  
Subhajit Patra ◽  
Prabirkumar Saha

In this paper, two efficient control algorithms are discussed viz., Linear Quadratic Regulator (LQR) and Dynamic Matrix Controller (DMC) and their applicability has been demonstrated through case study with a complex interacting process viz., a laboratory based four tank liquid storage system. The process has Two Input Two Output (TITO) structure and is available for experimental study. A mathematical model of the process has been developed using first principles. Model parameters have been estimated through the experimentation results. The performance of the controllers (LQR and DMC) has been compared to that of industrially more accepted PID controller.


2020 ◽  
Vol 10 (10) ◽  
pp. 3514 ◽  
Author(s):  
Adam Szabo ◽  
Tamas Becsi ◽  
Peter Gaspar

The paper presents the modeling and control design of a floating piston electro-pneumatic gearbox actuator and, moreover, the industrial validation of the controller system. As part of a heavy-duty vehicle, it needs to meet strict and contradictory requirements and units applying the system with different supply pressures in order to operate under various environmental conditions. Because of the high control frequency domain of the real system, post-modern control methods with high computational demands could not be used as they do not meet real-time requirements on automotive level. During the modeling phase, the essential simplifications are shown with the awareness of the trade-off between calculation speed and numerical accuracy to generate a multi-state piecewise-linear system. Two LTI control methods are introduced, i.e., a PD and an Linear-Quadratic Regulators (LQR) solution, in which the continuous control signals are transformed into discrete voltage solenoid commands for the valves. The validation of both the model and the control system are performed on a real physical implementation. The results show that both modeling and control design are suitable for the control tasks using floating piston cylinders and, moreover, these methods can be extended to electro-pneumatic cylinders with different layouts.


Author(s):  
Julie A. Reyer ◽  
Panos Y. Papalambros

Abstract In the design and optimization of artifacts requiring both mechanical and control design, the process is typically divided and performed in separate steps. The physical structure is designed first, a control strategy is selected, and the actual controller is then designed. This paper examines how this separation could affect the overall system design and how the combination of the separate problems into a single decision model could improve the overall design, using an electric DC motor as a case study. The combination is challenging since the two problems often have different design criteria and objectives and mathematical model properties. A Pareto analysis is suggested as a rigorous way to compare a variety of design scenaria.


Author(s):  
Benjamin Recht

This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provide nonasymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. The article concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.


Author(s):  
Sulaiman F. Alyaqout ◽  
Panos Y. Papalambros ◽  
A. Galip Ulsoy

System performance can significantly benefit from optimally integrating the design and control of engineering systems. To improve the robustness properties of systems, the present article introduces an approach that combines robust design with robust control and investigates the coupling between them. However, the computational cost of improving this robustness can often be high due to the need to solve a resulting minimax design and control optimization problem. To reduce this cost, sequential and iterative strategies are proposed and compared to an all-in-one strategy for solving the minimax problem. These strategies are then illustrated for a case-study: Robust design and robust control of a DC motor. Results show that the resulting strategies can improve the robustness properties of the DC motor. In addition, the coupling strength between robust design and robust control tends to increase as the applied level of uncertainty increases.


Sign in / Sign up

Export Citation Format

Share Document