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

2018 ◽  
Author(s):  
Daniel R. Herber ◽  
James T. Allison
Keyword(s):  
Dynamic Optimization ◽  
Design Problem ◽  
Control Design ◽  
Design Solution ◽  
Vehicle Suspension ◽  
Linear Quadratic ◽  
Problem Class ◽  
Physical Elements ◽  
And Control

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.

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
Keyword(s):  
Dynamic Optimization ◽  
Design Problem ◽  
Control Design ◽  
Vehicle Suspension ◽  
Engineering Systems ◽  
Linear Quadratic ◽  
Problem Class ◽  
Physical Elements ◽  
And Control

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.

Modeling and Control of a Complex Interacting Process

2011 ◽  
Vol 403-408 ◽  
pp. 3758-3762
Author(s):  
Subhajit Patra ◽  
Prabirkumar Saha
Keyword(s):  
Storage System ◽  
Linear Quadratic Regulator ◽  
Model Parameters ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Dynamic Matrix ◽  
Liquid Storage ◽  
Efficient Control ◽  
And Control

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.

scholarly journals Control Design and Validation for Floating Piston Electro-Pneumatic Gearbox Actuator

2020 ◽  
Vol 10 (10) ◽  
pp. 3514 ◽  
Author(s):  
Adam Szabo ◽  
Tamas Becsi ◽  
Peter Gaspar
Keyword(s):  
Piecewise Linear ◽  
Control Design ◽  
Continuous Control ◽  
Control Methods ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Control Frequency ◽  
Heavy Duty Vehicle ◽  
High Control ◽  
And Control

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.

Optimal Design and Control of an Electric DC Motor

1999 ◽  
Author(s):  
Julie A. Reyer ◽  
Panos Y. Papalambros
Keyword(s):  
Control Strategy ◽  
Decision Model ◽  
Control Design ◽  
Dc Motor ◽  
Physical Structure ◽  
Pareto Analysis ◽  
Design And Optimization ◽  
Overall Design ◽  
And Control

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.

scholarly journals A Tour of Reinforcement Learning: The View from Continuous Control

2019 ◽  
Vol 2 (1) ◽  
pp. 253-279 ◽  
Author(s):  
Benjamin Recht
Keyword(s):  
Reinforcement Learning ◽  
Linear Quadratic Regulator ◽  
Continuous Control ◽  
Linear Quadratic ◽  
Uncertain Environments ◽  
Optimization And Control ◽  
The Cost ◽  
Merging Techniques ◽  
And Control

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.

Modeling and Control Design of a Powertrain Simulation Testbed for Earthmoving Vehicles

1999 ◽  
Author(s):  
Eko A. Prasetiawan ◽  
Rong Zhang ◽  
Andrew G. Alleyne ◽  
Tsu-Chin Tsao
Keyword(s):  
First Principles ◽  
Control Design ◽  
System Response ◽  
Prime Mover ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Initial Control ◽  
Displacement Pump ◽  
Variable Displacement ◽  
And Control

Abstract A nonlinear model of an Earthmoving Vehicle Powertrain Simulator is developed using both first principles as well as I/O data. The model is an interconnection of subsystem models, which consist of a prime mover, a variable-displacement pump, proportional flow valves, and fixed-displacement motor models. In addition, a typical drive or tractive load of an earthmoving vehicle is also presented and implemented in the system as one of the loads. Efforts have been directed to develop a simple model in order to cater to developing and testing powertrain controllers as well as studying the dynamic behavior of such systems. An initial control design approach utilizing the Linear Quadratic technique is also presented. The controller is aimed at regulating load speeds in presence of a step load disturbance. The simulation results of the compensated system response are presented.

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