Sampling and controlling faster than the computational delay

Buchstaller, Dominic; Kerrigan, Eric C.; Constantinides, George A.

doi:10.1049/iet-cta.2010.0440

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…On the other hand there is a fundamental constraint for MPC design problems: in order to implement the controller in realtime, the optimization algorithm execution time has to be smaller than the sampling time of the system. There are certain exceptions to this rule [19], which are not considered in this work.…”

Section: Formulating the Design Problem As An Optimization Problemmentioning

confidence: 96%

Automatic Software and Computing Hardware Codesign for Predictive Control

Khusainov

Kerrigan

Constantinides

2019

IEEE Trans. Contr. Syst. Technol.

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Model Predictive Control (MPC) is a computationally demanding control technique that allows dealing with multiple-input and multiple-output systems, while handling constraints in a systematic way. The necessity of solving an optimization problem at every sampling instant often (i) limits the application scope to slow dynamical systems and/or (ii) results in expensive computational hardware implementations. Traditional MPC design is based on manual tuning of software and computational hardware design parameters, which leads to suboptimal implementations. This paper proposes a framework for automating the MPC software and computational hardware co-design, while achieving the optimal trade-off between computational resource usage and controller performance. The proposed approach is based on using a multiobjective optimization algorithm, namely BiMADS. Two test studies are considered: Central Processing Unit (CPU) and Field-Programmable Gate Array (FPGA) implementations of fast gradient-based MPC. Numerical experiments show that optimization-based design outperforms Latin Hypercube Sampling (LHS), a statistical sampling-based design exploration technique.

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Section: Formulating the Design Problem As An Optimization Problemmentioning

confidence: 96%

Automatic Software and Computing Hardware Codesign for Predictive Control

Khusainov

Kerrigan

Constantinides

2019

IEEE Trans. Contr. Syst. Technol.

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“…is the exponential function [51], [52]. The communication architecture for sensing the DER states is described in the next section.…”

Section: B Discretisation Of the Der State-space Modelmentioning

confidence: 99%

Distributed State Estimation Using RSC Coded Smart Grid Communications

Rana

2015

IEEE Access

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Recently, the renewable distributed energy resources (DERs) have become more and more popular due to carbonfree energy sources and environment-friendly electricity generation. Unfortunately, these power generation patterns are mostly intermittent in nature and distributed over the electrical grid which creates challenging problems in the reliability of the smart grid. Thus, smart grid has a strong requisite for an efficient communication infrastructure to facilitate estimating the DER states. In contrast to the traditional methods of centralised state estimation, we propose a distributed approach to microgrid state estimation based on the concatenated coding structure. In this framework, the DER state is treated as a dynamic outer code and the recursive systematic convolutional (RSC) code is seen as a concatenated inner code for protection and redundancy in the system states. Furthermore, in order to properly monitor the intermittent energy source from any place, this paper proposes a distributed state estimation method. Particularly, the outputs of the local state estimation are treated as measurements which are fed into the master fusion station. At the end, the global state estimation can be obtained by combining local state estimations with corresponding weighting factors. The weighting factors can be calculated by inspiring the covariance intersection method. The simulation results show that the proposed method is able to estimate the system state properly.

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“…Intra-delay sampling can be implemented by physically replicating the solver and/or by using data pipelining, so that computation of a new solution is started before termination of previous one. Disturbance rejection capabilities of a controller can be improved as a result of the increased sampling rate [35].…”

Section: A Processorsmentioning

confidence: 99%

Computer architectures to close the loop in real-time optimization

Kerrigan

Constantinides

Suardi

et al. 2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as 'fast' optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other.

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Sampling and controlling faster than the computational delay

Cited by 8 publications

References 3 publications

Automatic Software and Computing Hardware Codesign for Predictive Control

Automatic Software and Computing Hardware Codesign for Predictive Control

Distributed State Estimation Using RSC Coded Smart Grid Communications

Computer architectures to close the loop in real-time optimization

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