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 Here u(t) = 𝜓(𝜅(t)) is the control input, and p(t) is the disturbance or demand term. The global T–S fuzzy model is then obtained as: ̇ 𝜉(t) = r ∑ i=1 hi(z(t)) (𝔸i 𝜉(t) + 𝔹i u(t) + m(t)) . (6) With z1 = 𝜉A/700 (Low/High); z2 = CO2 (Low/High); z3 = Terrestrial biosphere 𝜉T (Low/High); z4 = Sea surface 𝜉S/1000 (Low/High); z5 = Wind speed (Low/High); and z6 = Climate risk (Low/High), ten rules are obtained: • ℜ1: If z1 is Low and z2 is Low, then chance of high temperature. • ℜ2: If z1 is Low and z2 is Low, then chance of high snowfall. • ℜ3: If z1 is High and z3 is High, then increase of risk of species extinction temperature. • ℜ4: If z1 is Low and z3 is Low, then decrease of risk of species extinction temperature. • ℜ5: If z4 is High and z3 is Low, then increase in biodiversity. • ℜ6: If z4 is Low and z3 is High, then decrease in biodiversity. • ℜ7: If z5 is High and z4 is High, then structural damage in buildings & trees. • ℜ8: If z5 is Low and z4 is Low, then no effect. • ℜ9: If z5 is High and z6 is High, then increase energy sources. • ℜ10: If z5 is Low and z6 is Low, then decrease in power output. This framework ties the compartmental dynamics (Eqs (5)–(7)), membership functions (Fig 7, 8, 9, and 10, and the control law 𝜓(𝜅) into a unified T–S fuzzy representation of the carbon–climate system. Remark 2. The main contribution of this research has been the following: ● For compartmen- talized reservoirs (atmosphere, terrestrial biosphere, and surface ocean), T–S fuzzy logic is used.

● Integrating uncertainty and nonlinear dynamics into carbon feedback circuits. ● Fuzzy rule development for adaptive carbon cycle management (as demonstrated in Figs 8–10 and Table 1.) Figs 7, 8, 9, and 10 show the outcomes for each input, indicating the groupings created by the fuzzy approach and the functions for membership of the fuzzy plant. To reduce the amount of rules in the T–S fuzzy structure, we choose a limited number of groups for every input. Four subplots depicting fuzzy membership functions for environmental variables such as tem- perature, humidity, and radiation are included in Figs 7, 8, 9, and 10. The link between envi- ronmental parameters and their respective membership functions is depicted in each sub- plot. Every subplot adheres to a similar structure, such as using red stars (*) to symbolize data points. In other words, those indicate data points from the first group (e.g., ’Hot’, ’Dry’, or ’Low’). ’Cold’, ’Wet’, or ’High’ data points are represented by blue circles (o) in category two. Additionally, the membership functions for the first and second groups are shown by solid red and blue lines, respectively. Every graphic exhibits the fuzzy logic properties of soft transitions between two states (hot/cold, dry/wet, etc.). This is a basic feature of fuzzy logic that deals with overlapping con- ditions and uncertainty. Since it can be challenging to set exact thresholds, these fuzzy mem- bership functions are commonly used in heating, ventilation, and air conditioning (HVAC)

systems, climate management, and smart building automation. The system’s adaptable capa- bility, which is essential for practical applications, is demonstrated by the small fluctuation in crossover points between internal and external temperatures. Although there is a lot of noise PLOS One https://doi.org/10.1371/journal.pone.0333846 October 14, 2025 12/ 30