Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy

Bin-Karim, Shamir; Bafandeh, Alireza; Baheri, Ali; Vermillion, Chris

doi:10.1109/tcst.2017.2779428

Cited by 19 publications

(12 citation statements)

References 29 publications

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“…The Gaussian process modeling is presented in the following. Gaussian Process Model: The Gaussian process (GP) is a non-parametric probabilistic approach used to define a prior probability distributions over latent functions directly, which has been extensively applied in wind forecasting [15], [29]. As we will show, GP can also be utilized to predict the ocean velocity.…”

Section: A Statistical Ocean Current Shear Profile Characterizationmentioning

confidence: 99%

“…∆P = P atm + P HS (15) where η pump denotes the pump efficiency (0.75), P atm is atmospheric pressure (101 kPa), P HS is hydrostatic pressure (P HS = ρ.g.z), and g = 9.81 m/s 2 is gravity. Then the P empty B is rewritten as:…”

Section: B Mathematical Model Of Output Power Of Octmentioning

confidence: 99%

“…MPC is a powerful method for optimizing the objective function by predicting the future actions. The MPC-based algorithm has been used for maximizing the output power of an airborne system [15], maximizing the output power of an autonomous underwater vehicle [37], and minimizing the operational cost of wind turbine considering the fault-tolerant method [38]. In this paper, MPC method is used in order to maximize the output power of the OCT system.…”

Section: B Mpc-based Optimization Designmentioning

confidence: 99%

“…A fault-tolerant MPC controller is designed for wind turbines to maximize output power and minimize fatigue at the same time [14]. An airborne wind energy system employs MPC-based techniques to set the optimal altitude [15], in which future wind speeds are predicted and output power is optimized by changing the location of the system to optimal velocities. In another close study [16], performance of the MPC method and a Lyapunov-based controller are compared for optimizing the output power of an airborne wind energy system.…”

Section: Introductionmentioning

confidence: 99%

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Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

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This paper presents a novel spatiotemporal optimization approach for maximizing the output power of an ocean current turbine (OCT) under uncertain ocean velocities. In order to determine output power, ocean velocities and the power consumed and generated by an OCT system are modeled. The stochastic behavior of ocean velocities is a function of time and location, which is modeled as a Gaussian process. The power of the OCT system is composed of three parts, including generated power, power for maintaining the system at an operating depth, and power consumed for changing the water depth to reach the maximum power. Two different algorithms, including model predictive control (MPC) as a model-based method and reinforcement learning (RL) as a learning-based method, are proposed to design the optimization structure, and comparative studies are presented. On one hand, the MPC based controller is faster in finding the optimal water depth, while the RL is also computationally feasible considering the required time for changing operating depth. On the other hand, the cumulative energy production of the RL algorithm is higher than the MPC method, which verifies that the learning-based RL algorithm can provide a better solution to address the uncertainties in renewable energy systems. Results verify the efficiency of both presented methods in maximizing the total power of an OCT system, where the total harnessed energy after 200 hours shows an over 18% increase compared to the baseline.

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Section: A Statistical Ocean Current Shear Profile Characterizationmentioning

confidence: 99%

Section: B Mathematical Model Of Output Power Of Octmentioning

confidence: 99%

Section: B Mpc-based Optimization Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Tang¹

2020

Preprint

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“…The vast energy resource from high-altitude winds has attracted the attention of numerous research and commercial ventures over the past two decades ( [1,2,3,4,5]). Todate, many organizations in the AWE community have focused on optimizing operating altitude ( [6,7]) and crosswind motion to maximize power output, while a limited number of studies have focused on combined plant and control system designs, which have been shown in [8] to be coupled.…”

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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Integrated path planning and control through proximal policy optimization for a marine current turbine

Hasankhani

Tang

VanZwieten

2023

Applied Ocean Research

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Spatiotemporal Optimization Through Gaussian Process-Based Model Predictive Control: A Case Study in Airborne Wind Energy

Cited by 19 publications

References 29 publications

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

Ocean Current Turbine Power Maximization: A Spatiotemporal Optimization Approach

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

Integrated path planning and control through proximal policy optimization for a marine current turbine

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