Abstract
Autotuning and online tuning of control parameters in control processes (OTP) are widely used in practice, such as in chemical production and industrial control processes. Better performance (such as dynamic speed and steady-state error) and less repeated manual-tuning workloads in bad environments for engineers are expected. The main works are as follows: Firstly, a change ratio for expert system and fuzzy-reasoning-based OTP methods is proposed. Secondly, a wavelet neural-network-based OTP method is proposed. Thirdly, comparative simulations are implemented in order to verify the performance. Finally, the stability of the proposed methods is analyzed based on the theory of stability. Results and effects are as follows: Firstly, the proposed control parameters of online tuning methods of artificial-intelligence-based classical control (AI-CC) systems had better performance, such as faster speed and smaller error. Secondly, stability was verified theoretically, so the proposed method could be applied with a guarantee. Thirdly, a lot of repeated and unsafe manual-based tuning work for engineers can be replaced by AI-CC systems. Finally, an upgrade solution AI-CC, with low cost, is provided for a large number of existing classical control systems.
Highlights
The proportion integration differentiation (PID) control method is widely used in practical control processes
(2) MIC, relay autotuner-based autotuning control method (RAC), gain scheduling control (GSC), and artificial-intelligence-based classical control (AI-CC) are based on classical PID methods, where the tuning process is the optimization process of Kp, Ki, Kd
Comparing artificial intelligence (AI)-CC methods with the other intelligent control methods such as predictive control [16,17] and adaptive control [18,19], AI-CC methods have the following advantages: (1) Theoretical analysis can be implemented based on a large number of classical control theories, (2) a large number of practical applications are based on classical control and the upgrade work for these existing control systems is valuable, and (3) the output of AI methods are the control parameters, so the model of the control system becomes simple and the upgrade work becomes simple in practice
Summary
The proportion integration differentiation (PID) control method is widely used in practical control processes. The existing autotuning and online tuning methods of control parameters are as follows:. Soltesz [5] proposed an automatic tuning strategy based on an experiment, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. Differences among the above three methods are as follows: (1) MIC and RAC are autotuning methods, which can automatically adjust the parameters more accurately; the adjustment processes of MIC and RAC are not production processes. GSC, AIC, and AI-CC methods are online-tuning methods, where the adjustment processes can directly be the production processes. (2) MIC, RAC, GSC, and AI-CC are based on classical PID methods, where the tuning process is the optimization process of Kp , Ki , Kd. ADC and PDC have many forms of control models, some of which are not based on adjusting PID parameters. The production process is the final process of both methods of RAC and AI-CC, while RAC has a unique relay-based autotuning process
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