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Model-free adaptive predictive control for cell culture: Decoupling environmental dynamics and compensating
Muhang Li1, Ran Tang1, ZiYao Liao1
1Center of Ultra-precision Optoelectronic Instrument engineering, Harbin Institute of Technology, Harbin 150080, China; Key Lab of Ultra-precision Intelligent Instrumentation (Harbin Institute of Technology), Ministry of Industry and Information Technology, Harbin 150080, China.
ISA Transactions
|February 17, 2026
Summary
This study introduces a novel adaptive control method for bioreactors, improving dissolved oxygen and pH regulation. The approach enhances system stability and disturbance rejection for better bioprocess control.
Area of Science:
- Biotechnology
- Control Engineering
- Process Systems Engineering
Background:
- Bioreactor control systems struggle with coupled variables, parameter changes, and disturbances.
- Precise regulation of dissolved oxygen (DO) and pH is critical for bioprocess efficiency.
Purpose of the Study:
- To develop a dynamic disturbance-compensation model-free adaptive predictive control method.
- To enhance the stability and controllability of bioreactor systems.
Main Methods:
- Established a dynamic linearization model using input-output data.
- Formulated a control method with adaptive, time-varying control gains.
- Designed online adaptive learning mechanisms for DO and pH error characteristics.
Main Results:
- The proposed method demonstrated superior disturbance rejection capabilities.
- Lyapunov analysis confirmed uniformly ultimately bounded tracking error.
- Simulations and experiments validated enhanced system stability and controllability.
Conclusions:
- The novel adaptive control method effectively addresses challenges in bioreactor regulation.
- This approach significantly improves the performance of bioreactor systems.
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