Co-design of linear systems using Generalized Benders Decomposition

Chanekar, Prasad Vilas; Chopra, Nikhil; Azarm, Shapour

doi:10.1016/j.automatica.2017.12.009

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…(13) will result in V 0 ≤ −ηx ⊤ x < 0 i.e., V 0 < 0 if (11) holds. As A 0 c is Hurwitz, I + BB ⊤ B 2 ≻ 0, then (11) will hold true when the Hamiltonian of ( 11)…”

Section: A Computation Of Initial Stabilizing Controller Gainmentioning

confidence: 99%

“…, is hyperbolic i.e., H 0 has no purely imaginary eigenvalues [18], [19]. Next we derive a condition involving α, η for the existence of a solution to (11). Consider…”

Section: A Computation Of Initial Stabilizing Controller Gainmentioning

confidence: 99%

“…We can classify system performance improvement problems with different types of design variables as co-design problems for example: network actuator/sensor placement [7], structured/sparse controller design [8], [9], network edge modification [10] etc., to name a few. A detailed literature survey regarding co-design of linear systems can be read in [11], [12].…”

Section: Introductionmentioning

confidence: 99%

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Co-Design of Lipschitz Nonlinear Systems

Chanekar¹,

Chopra²

2022

Preprint

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Empirical experiences have shown that simultaneous (rather than conventional sequential) plant and controller design procedure leads to an improvement in performance and saving of plant resources. Such a simultaneous synthesis procedure is called as "co-design". In this letter we study the co-design problem for a class of Lipschitz nonlinear dynamical systems having a quadratic control objective and state-feedback controller. We propose a novel time independent reformulation of the co-design optimization problem whose constraints ensure stability of the system. We also present a gradient-based codesign solution procedure which involves system coordinate transformation and whose output is provably stable solution for the original system. We show the efficacy of the solution procedure through co-design of a single-link robot.

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“…(13) will result in V 0 ≤ −ηx ⊤ x < 0 i.e., V 0 < 0 if (11) holds. As A 0 c is Hurwitz, I + BB ⊤ B 2 ≻ 0, then (11) will hold true when the Hamiltonian of ( 11)…”

Section: A Computation Of Initial Stabilizing Controller Gainmentioning

confidence: 99%

“…, is hyperbolic i.e., H 0 has no purely imaginary eigenvalues [18], [19]. Next we derive a condition involving α, η for the existence of a solution to (11). Consider…”

Section: A Computation Of Initial Stabilizing Controller Gainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Co-Design of Lipschitz Nonlinear Systems

Chanekar¹,

Chopra²

2022

Preprint

Self Cite

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“…where X and Y represent the vehicle longitudinal and lateral positions, C lf and C lr represent the front and rear equivalent longitudinal cornering stiffness, C cr and C cr represent the front and rear equivalent lateral cornering stiffness, s f and s r denote the front and rear longitudinal slip rates, ψ denotes the yaw angle and V x and V y denote the longitudinal and lateral vehicle velocities. For ensuring control efficiency without sacrificing control performance, 27 model (1) is linearized to time-variant system model as…”

Section: Modellingmentioning

confidence: 99%

An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

Zhang

Zong

Chen

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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An adaptive-prediction-horizon model prediction control-based path tracking controller for a four-wheel independent control electric vehicle is designed. Unlike traditional model prediction control with fixed prediction horizon, this paper devotes to satisfy the varied path tracking demand by adjusting online the prediction horizon of model prediction control according to its effect on vehicle dynamic characteristics. Vehicle dynamic stability quantized with the vehicle sideslip-feature phase plane is preferentially considered in the prediction horizon adjustment. For stability during switching prediction horizon and for robustness during path tracking, the numerical problem inherent in the adaptive-prediction-horizon model prediction control is analysed and solved by introducing exponentially decreasing weight. Subsequently, the desired motion for path tracking with the four-wheel independent control electric vehicle is realized with a hierarchical control structure. Simulation results finally illustrate the effectiveness of the proposed method.

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“…Examples of the co-designing of geometric properties and controller tuning parameters for a four-bar system (using a Genetic Algorithm), parallel robots (using a differential evolution), and machine tools (using a multilevel decomposition) can be found in [2][3][4], respectively. Similarly, co-designing of controller tuning through the use of a Linear Quadratic Regulator (LQR) method and the geometrical properties of a pendulum system, a satellite altitude control, and an angular motor controlling a mass position was detailed in [5][6][7], respectively. More recently, ref.…”

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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Co-design of linear systems using Generalized Benders Decomposition

Cited by 7 publications

References 35 publications

Co-Design of Lipschitz Nonlinear Systems

Co-Design of Lipschitz Nonlinear Systems

An adaptive-prediction-horizon model prediction control for path tracking in a four-wheel independent control electric vehicle

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

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