Optimal Design and Control of an Electric DC Motor

Reyer, Julie A.; Papalambros, Panos Y.

doi:10.1115/detc99/dac-8599

Cited by 8 publications

(3 citation statements)

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“…This approach, also known as codesign, consists in tuning both controller gains and physical parameters influencing the open-loop plant dynamics. It has been applied to a variety of domains such as chemistry [9], robotics [10] and powertrains [11] [12]. In aerospace, the method has mainly been used in preliminary design for different purposes such as mass reduction of large flexible structures [13], reduction of avionics requirements for satellite attitude control [14], optimization of an airborne-wind-energy system [15] and aircraft control surfaces sizing under handling qualities constraints [16] [17].…”

Section: B Plant-controller Optimizationmentioning

confidence: 99%

Concurrent Airframe-Controller Optimization of a Guided Projectile Fitted with Lifting Surfaces

Riss

Roussel

Laroche

2022

AIAA SCITECH 2022 Forum

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Innovative long-range guided projectiles concepts require precise flight path tracking in order to achieve maximum range and terminal accuracy. For such airframes featuring large lifting surfaces, vertical maneuvering performance relies on pitch attitude control.Thus, a challenging problem arises from the conflict between performance requirements and actuator limitations. The projectile static stability should be tailored to limit controls deflections and actuator bandwidth. This work proposes a plant-controller optimization (PCO) procedure to tune the projectile static stability and the controller gains in a single step. This methodology is applied to actuator usage minimization under control performance constraint. The optimal airframe-controller design shows superior actuator roll-off characteristics compared to the outcome of the traditional "design then control" methodology. The codesign also proves to be much less computationally intensive, which confirms the relevance of the approach.

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Section: B Plant-controller Optimizationmentioning

confidence: 99%

Concurrent Airframe-Controller Optimization of a Guided Projectile Fitted with Lifting Surfaces

Riss

Roussel

Laroche

2022

AIAA SCITECH 2022 Forum

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“…[9,10] used the H 2 /H ∞ method to obtain the controller tuning parameters. PID values can also be directly optimized, as shown in [11] for a DC motor. In [12], an example is shown in which the co-design of mechanical design variables and controller parameters was performed for a hybrid power train system used in a hydraulic excavator.…”

Section: Introductionmentioning

confidence: 99%

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

et al. 2021

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For complex systems, it is not easy to obtain optimal designs for the hardware architecture and control configurations. Every design aspect influences the final performance, and often the interactions of the different components cannot be clearly determined in advance. In this work, a novel co-design optimization method was applied that allows the optimal placement and selection of actuators and sensors to be performed simultaneously with the determination of the control architecture and associated controller tuning parameters. This novel co-design method was applied to a state-space model of a downscaled active car suspension laboratory setup. This setup mimics a car driving over a specific road surface while active components in the suspension have to increase the driver’s comfort by counteracting unwanted vibrations. The result of this co-design optimization methodology is a Pareto front that graphically represents the trade-off between the maximum performance and the total implementation cost; the co-design results were validated with measurements of the physical active car suspension setup. The obtained controller tuning parameters are compared herein with existing controller tuning methods to demonstrate that the co-design method is able to determine optimal controller tuning parameters.

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“…• Iterative: The iterative approach fully optimizes the plant design for a given controller, then optimizes the controller design for a fixed plant, then repeats the cycle (see [14], [15]).…”

Section: Introductionmentioning

confidence: 99%

Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

Baheri

Vermillion

2019

Journal of Dynamic Systems, Measurement, and Control

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We present a novel data-driven nested optimization framework that addresses the problem of coupling between plant and controller optimization. This optimization strategy is tailored towards instances where a closed-form expression for the system dynamic response is unobtainable and simulations or experiments are necessary. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-toevaluate objective function, is employed to solve a nested optimization problem. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to determine the best subsequent control or plant parameters. The proposed framework differs from the majority of co-design literature where there exists a closedform model of the system dynamics. Furthermore, we utilize the idea of Batch Bayesian Optimization at the plant optimization level to generate a set of plant designs at each iteration of the overall optimization process, recognizing that there will exist economies of scale in running multiple experiments in each iteration of the plant design process. We validate the proposed framework for Altaeros' Buoyant Airborne Turbine (BAT). We choose the horizontal stabilizer area, longitudinal center of mass relative to center of buoyancy (plant parameters), and the pitch angle set-point (controller parameter) as our decision variables. Our results demonstrate that these plant and control parameters converge to their respective optimal values within only a few iterations. Figure 1: Altaeros Buoyant Airborne Turbine (BAT), Image Credit: [1]

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Optimal Design and Control of an Electric DC Motor

Cited by 8 publications

References 0 publications

Concurrent Airframe-Controller Optimization of a Guided Projectile Fitted with Lifting Surfaces

Concurrent Airframe-Controller Optimization of a Guided Projectile Fitted with Lifting Surfaces

Optimal Hardware and Control Co-Design Applied to an Active Car Suspension Setup

Combined Plant and Controller Design Using Batch Bayesian Optimization: A Case Study in Airborne Wind Energy Systems

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