Control parameters of classical control system are expected to be online tuned and optimized by intelligent methods, in order to improve performance and help engineers reduce a lot of repetitive work in dangerous and harmful working environments. Main ideas and works of this paper are as follows: Firstly, change ratio based expert PID control method (EA-PID) is proposed to expand range of control parameters. Expert rule table (ERT) of expert PID control method (E-PID) is replaced by change ratio table (CRT) of EA-PID. Adjusted parameters of EA-PID are the results of multiplying change ratios in current adjusting cycle and control parameters in previous adjusting cycle. Secondly, NARX prediction-based NARX-E-PID and NARX-EA-PID are proposed. The NARX neural network is designed as a time series predictor to predict the output of the control system, then control parameters are adjusted according to the predicted output. Thirdly, comparative simulations of all the above methods are implemented to verify the improved effects. Finally, theoretical analysis is provided to ensure the stability of control systems. Effect are as follows: Firstly, comparative simulations verify that the improved methods have faster control speed, smaller steady-state error, less overshoot, and better ability of anti-interference. Secondly, theoretical analysis shows that the unstable control systems with adjusted parameters can be changed into a stable system by stability judgment in each adjusting cycle.

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.