Supervised model predictive control of large‐scale electricity networks via clustering methods

Abstract There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

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Computational Techniques for Model Predictive Control of Large-Scale Systems with Continuous-Valued and Discrete-Valued Inputs

Journal of Applied Mathematics ◽

10.1155/2013/615060 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9

Author(s):

Koichi Kobayashi ◽

Kunihiko Hiraishi

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Large Scale ◽

Computational Techniques ◽

Continuous Variables ◽

Current Time ◽

Large Scale Systems ◽

Virtual Control ◽

Online Computation ◽

Computation Load

We propose computational techniques for model predictive control of large-scale systems with both continuous-valued control inputs and discrete-valued control inputs, which are a class of hybrid systems. In the proposed method, we introduce the notion of virtual control inputs, which are obtained by relaxing discrete-valued control inputs to continuous variables. In online computation, first, we find continuous-valued control inputs and virtual control inputs minimizing a cost function. Next, using the obtained virtual control inputs, only discrete-valued control inputs at the current time are computed in each subsystem. In addition, we also discuss the effect of quantization errors. Finally, the effectiveness of the proposed method is shown by a numerical example. The proposed method enables us to reduce and decentralize the computation load.

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Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation

Volume 3: Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy ◽

10.1115/dscc2014-5954 ◽

2014 ◽

Author(s):

Haopeng Zhang ◽

Qing Hui

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Control Strategy ◽

Large Scale ◽

Optimization Problems ◽

Optimal Solution ◽

Nonlinear Problems ◽

Pso Algorithm ◽

Network Resource ◽

Multiagent Coordination

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.

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