Downing, Cameron P. D.Counsell, John M.2024-10-282024-10-282024-10-18Downing, C. P. D., & Counsell, J. M. (2024, 1-4 July). Integrated design of optimisation and inverse dynamics control for home heating systems. 10th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 236-241), Valletta, Malta. https://doi.org/10.1109/CoDIT62066.2024.1070812910.1109/CoDIT62066.2024.10708129http://hdl.handle.net/10034/629097This conference paper is not available on ChesterRep© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper presents a novel controller design methodology for multi-input multi-output linear time invariant systems using a realisable system’s inverse dynamics whilst simultaneously aiming to optimize the systems performance. This is very useful for energy system type controller design problems, especially heating systems as described in this paper. The new methodology, OPTimal Inverse Control (OPTIC) integrates the challenge to minimize a cost function and introduce it into the feedback control solution, thus allowing for simultaneous fast acting stable and non-interacting feedback control with cost function minimisation to provide a robust and optimal solution. The cost function presented acts to minimise weighted proportions of Carbon Intensity (c I ) of the energy supply system and the Cost of the Tariff (c Tar ) depending on the policy weighing potential fuel poverty against net zero carbon heating solutions. It is described how the OPTIC method is applied to a heating system control problem using a home heating dynamic model Inverse Dynamics based Energy Assessment and Simulation (IDEAS). IDEAS calibrated with UK’s Part L building regulations models a home’s building physics, heating demand using a standard type of occupancy over a whole year. This allows the OPTIC method to control the heating system to reach the required home comfort whilst continuously heading towards the minimum cost function value at all times, minute by minute instead of a much slower period of optimisation that is achieved by other complementary methods, like General Predictive Control (GPC).https://creativecommons.org/licenses/by-nc-nd/4.0/Heating systemsIntegrated opticsAdaptive opticsOptical pumpingThermal stabilityConference Contribution2576-35552024-10-28