Fast Nonconvex Model Predictive Control for Commercial Refrigeration

Hovgard, Tobias Gybel; Larsen, Lars Finn Sloth; Jørgensen, John Bagterp; Boyd, Stephen

doi:10.3182/20120823-5-nl-3013.00082

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…In this paper, the normalΔt serves as the ‘replanning frequency’, or, the time after which the a re-optimization of a trajectory is carried out. It is possible to improve the computational speed and convergence of the algorithm with a warm start , which uses the trajectory computed in a previous instance as the initial guess for the trajectory in the next instance [73].…”

Section: Methodsmentioning

confidence: 99%

Finite-horizon, energy-efficient trajectories in unsteady flows

2022

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Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between trajectories optimized over a finite-time horizon and finite-time Lyapunov exponents of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield energy-efficient trajectories. We also explore these connections on a three-dimensional flow and ocean flow data from the Gulf of Mexico. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.

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Section: Methodsmentioning

confidence: 99%

Finite-horizon, energy-efficient trajectories in unsteady flows

2022

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“…The sampling time step is ∆t, the discretization of dτ . It is possible to improve the computational speed and convergence of the algorithm with a warm start, which uses the trajectory computed in a previous instance as the initial guess for the trajectory in the next instance [70].…”

Section: Model Predictive Controlmentioning

confidence: 99%

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Krishna,

Song,

Brunton

2021

Preprint

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Intelligent mobile sensors, such as uninhabited aerial or underwater vehicles, are becoming prevalent in environmental sensing and monitoring applications. These active sensing platforms operate in unsteady fluid flows, including windy urban environments, hurricanes, and ocean currents. Often constrained in their actuation capabilities, the dynamics of these mobile sensors depend strongly on the background flow, making their deployment and control particularly challenging. Therefore, efficient trajectory planning with partial knowledge about the background flow is essential for teams of mobile sensors to adaptively sense and monitor their environments. In this work, we investigate the use of finite-horizon model predictive control (MPC) for the energy-efficient trajectory planning of an active mobile sensor in an unsteady fluid flow field. We uncover connections between the finite-time optimal trajectories and finite-time Lyapunov exponents (FTLE) of the background flow, confirming that energy-efficient trajectories exploit invariant coherent structures in the flow. We demonstrate our findings on the unsteady double gyre vector field, which is a canonical model for chaotic mixing in the ocean. We present an exhaustive search through critical MPC parameters including the prediction horizon, maximum sensor actuation, and relative penalty on the accumulated state error and actuation effort. We find that even relatively short prediction horizons can often yield nearly energy-optimal trajectories. These results are promising for the adaptive planning of energy-efficient trajectories for swarms of mobile sensors in distributed sensing and monitoring.

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“…In order to limit the size of the optimization problem in each step, a sample time of 15 mins was chosen for predictions of the next 24 hours [34]. A low-complexity MPC was developed for building cooling systems with thermal energy storage.…”

Section: Fig 1 Schematic Diagram Of An Automotive A/c-r System With Cargomentioning

confidence: 99%

Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems

et al. 2016

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This paper presents several robust model predictive controllers that improve the temperature performance and minimize energy consumption in an automotive air-conditioning/refrigeration (A/C-R) system with a three-speed and continuously-varying compressor. First, a simplified control-oriented model of the A/C-R system is briefly introduced. Accordingly, a discrete Model Predictive Controller (MPC) is designed based on the proposed model for an A/C-R system with a three-speed compressor. A proper

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Fast Nonconvex Model Predictive Control for Commercial Refrigeration

Cited by 4 publications

References 23 publications

Finite-horizon, energy-efficient trajectories in unsteady flows

Finite-horizon, energy-efficient trajectories in unsteady flows

Finite-Horizon, Energy-Optimal Trajectories in Unsteady Flows

Optimal energy-efficient predictive controllers in automotive air-conditioning/refrigeration systems

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