Abstract :
On the basis of logistic (Verhulst) model describing obstruction to a growth of population under environment and resources restriction, the most extensively used urban water demand function, namely, urban average population water demand model was established. For the sake of the extraordinary significance of efficiency and effect of investment in water industry, an urban water supply function was formed based on neoclassical economic theory of investment. Through transforming the nonlinear water demand state function from implicit containing time variable to time-varying linear explicit containing time variable, and considering the stochastic noise disturbance in the process of system operation, the nonlinear urban water demand and supply management dynamic optimization system was transformed into stochastic time-varying linear quadratic optimal control system to be dealt with. By means of reasonable drawing up urban water supply investment decision, the total square of biased deviation of urban water supply and demand can be minimized. Through choosing performance index weighting coefficients when adjusting system parameter on a certain condition, the solution of the Riccati matrix differential equation can be a constant matrix and the system controller design can be simplified.
Keywords :
Riccati equations; control system synthesis; differential equations; investment; logistics; matrix algebra; optimal control; optimisation; stochastic systems; water supply; Riccati matrix differential equation; constant matrix; demand management investment; dynamic optimization system; logistic model; neoclassical economic theory; stochastic optimal control; stochastic time-varying linear quadratic optimal control system; system controller design; urban average population water demand; urban water supply; Industrial economics; Investments; Logistics; Optimal control; Stochastic processes; Stochastic resonance; Stochastic systems; Supply and demand; Time varying systems; Water resources; demand and supply management; dynamic optimization; investment; stochastic optimal control; water resources; weighting coefficients;