Abstract
For solving the problems of closed-loop optimization on controller parameters of multiple-controller single-output thermal engineering system, this paper proposes a recurrent optimization method that is based on the particle swarm computing and closed-loop simulation (PSO-RCO). It consists of a set of closed-loop identification, simulation, and optimization functions that are organized in a recurrent working flow. The working flow makes one controller tuned at a time whilst others keep their values. It ends after several rounds of overall optimizations. Such a recurrently alternative tuning can greatly speed up the convergence of controller parameters to reasonable values. Verifications on practical data from a superheated steam temperature control system show that the optimized control system performance is greatly improved by reasonable controller parameters and practicable control action. With the advantage of not interfering system operation and the potential supporting on big data identification method, the PSO-RCO is a promising method for control system optimization.
Highlights
Proportional-Integral-Derivative (PID) control is the most classic control strategy and still the mainstream in thermal engineering control at present [1]
Due to the characteristics of thermal engineering system, such as large range of load variation, long-term continuous operation, nonlinear dynamics, and multiple disturbances, the control performance of PID controller may become worse in operation, resulting in large fluctuation and deviation of process states and jeopardizing the safe and economic conditions of the process
The closed-loop optimization method works on closed-loop control system and does not interfere with process states, it could be a more suitable way for controller parameter optimization
Summary
Proportional-Integral-Derivative (PID) control is the most classic control strategy and still the mainstream in thermal engineering control at present [1]. In order to solve the aforementioned problems, this paper proposes a PSO-based recurrent closed-loop optimization method (PSO-RCO) for multiple controller parameters in a single-output thermal engineering process. Because superheated steam temperature is a critical quantity on unit lifetime, efficiency and load following capability, frequent tuning on its control parameter adapting to circumstances is in need [13]. It is a persuasive example for verifying the PSO-RCO method. A controller parameter optimization approach based on closed-loop identification and simulation is proposed for thermal engineering processes.
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