Design of global climate control based on fuzzy systems with concept of carbon emissions

PLOS One Design of global climate control based on fuzzy systems Consequently, the controller’s timing is determined only by 𝜅max , which determines when it turns off and at what speed it operates. Controllers with steep slopes can react quickly at low values of 𝜅max, while those with steep slopes at higher values of 𝜅max react slowly. In contrast to original model, we assume that the air–ocean flow (𝜆A→S) and the air–biosphere flow (𝜆A→T) concentrate at 200 and 90 PgC year –1, respectively. The idea that natural sinks are saturated is consistent with previous studies. Controlling fossil fuel emissions can avoid this congestion by allowing emissions to be controlled based on a known demand p. By allowing only a small percentage 𝜓 of the demand p, the authors implement an output feedback control scheme. The auxiliary variable 𝜅 is defined as follows: ̇ 𝜓(t) = y(t) – 𝜓(𝜅)).p(t) (13) where y(t) = 𝜆S→D – 𝜆D→S Carbon flux to the deep ocean is the amount of carbon that leaves the system. y(t) is a measure of the net outflow from the system, which feeds the variable 𝜅. After that, drain by a controlled input whose size is based on the virtual pool’s content via a monotonic and continuous–time differentiable at first input. A function with no constraints 𝜓 is on the other hand. This arrangement prevents congestion 𝜓 as it maintains the total mass constant while generating a physically closed virtual system. Congestion control can be used as an additional goal in a realistic scenario based on the freedom to choose. In terms of some desired prop-

erties, it may be optimized. Since no assumption of optimality is necessary for this example, such optimization is obviously outside its purview. In instances where 𝜅max assumes minimal values, the controller exhibits a pronounced gra- dient, indicating its capacity for rapid response; conversely, as the parameter attains greater magnitudes, the controller demonstrates a more gradual reaction. In the same way, the con- vergence ratio of all trajectories has been shown in Figs 21, 22, and 23. The influence of vary- ing 𝜅max values on the regulatory framework and its efficacy in preventing the buildup of atmospheric carbon within the context of thermal energy storage can be observed in Fig 24. Fig 21. Convergence ratio of 𝜉A(t) and ∥ 𝜉A(t) ∥. https://doi.org/10.1371/journal.pone.0333846.g021 PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 24/ 30