This paper proposes an online type control parameter tuning method for a predictive pi controller. predictive pi controller is based on a picontroller with a Smith predictor, and it is effective for a controlled obje...
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ISBN:
(纸本)9789897583803
This paper proposes an online type control parameter tuning method for a predictive pi controller. predictive pi controller is based on a picontroller with a Smith predictor, and it is effective for a controlled object with large dead-time. Control performance of the predictive pi controller strongly depends on control parameters. Recently, some data-driven controller tuning methods have been proposed. The methods directly calculate suitable parameters from one or some sets of operating data. In addition, almost controlled processes are time-variant. In this paper, a data-driven self-tuning predictive pi controller is proposed. The effectiveness of the proposed scheme is evaluated by a simulation example.
The accelerated particle swarm optimisation (APSO) is an improved variant of the PSO algorithm that guarantees convergence through the use of only global best to update both velocity and position of particles. However...
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The accelerated particle swarm optimisation (APSO) is an improved variant of the PSO algorithm that guarantees convergence through the use of only global best to update both velocity and position of particles. However, like its predecessor, the APSO is also prone to being trapped in local minima. Therefore, this paper proposes two hybrid algorithms synergizing the social ability of the APSO and the exploitative ability of both spiral dynamic algorithm (SDA) and Adaptive SDA(ASDA). The exploration phase of the proposed algorithms APSO-SDA and APSO-ASDA, will be achieved through the APSO algorithm. The exploration phase solutions of the APSO are then fed to the SDA and ASDA to achieve the exploitation phase. The proposed algorithms have been evaluated with benchmark function and have also been used to tune a filtered predictive proportional-integral (FPpi) controller for WirelessHART networked control systems (WHNCS). The results obtained from Friedman's rank test show that the proposed APSO-SDA and APSO-ASDA outperformed their constituent algorithms. Time domain analysis of the FPpicontroller also show that the APSO-SDA and APSO-ASDA outperformed the APSO, SDA and ASDA in terms of settling times and overshoot.
In this work, a relay based automatic tuning method is proposed for the predictivepi control scheme. The proposed method comprises of two steps, namely process model identification by utilizing the standard relay fee...
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In this work, a relay based automatic tuning method is proposed for the predictivepi control scheme. The proposed method comprises of two steps, namely process model identification by utilizing the standard relay feedback test and ultimate gain determination by once applying again the test of standard relay feedback. First Order Plus Dead-Time model is identified from the first relay feedback test information. By using that model, the dynamic part of the controller is designed and augmented with the process. Once again, feedback test for relay is performed for process augmented with dynamic part of the controller to find the ultimate loop gain. Then, the controller gain is tuned for the user specified gain margin specification. Simulated outputs are exhibited to show the performance and easiness of the proposed automatic tuning method. Further, real-time validation on two-tank non-interacting system is presented. The proposed relay based method is more suitable for industrial process control applications due to the autotuning feature, easiness and performance.
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