Local learning‐based model‐free adaptive predictive control for adjustment of oxygen concentration in syngas manufacturing industry. Issue 12 (1st August 2016) - Hou, Zhongsheng; Liu, Shida; Yin, Chenkun
Abstract : During the united gas improvement (UGI) gasification process in the syngas industry, the oxygen‐enriched technique plays an important role, since the obtained oxygen‐enriched air with a high oxygen concentration can enhance the production efficiency of the syngas. However, satisfactory control performance for the oxygen concentration of the oxygen‐enriched air is hard to achieve because an accurate dynamical model of the oxygen concentration control process by the first principles is fairly difficult to obtain due to strong non‐linearity and unknown disturbances in practice. A novel data‐driven control method called compact‐form‐dynamic‐linearisation‐based model‐free adaptive predictive control approach combined with the local learning (LL‐CFDL‐MFAPC) is proposed to address the control problem. In LL‐CFDL‐MFAPC, the online and offline input–output measurement data of the plant are fully and simultaneously utilised during the control process, and the design of the controller is model free by means of compact‐form‐dynamic‐linearisation technique. Moreover, the controller has strong robustness because the prediction mechanism participates in control design and only the input/output measurement data are used. The stability and convergence of LL‐CFDL‐MFAPC are guaranteed by theoretical analysis under several reasonable assumptions, and simulation experiments using real data collected from a practical UGI gasifier verify that the oxygen concentration control problem can be effectively addressed by the proposed method.