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Current data-driven predictive control (DDPC) methods heavily rely on datacollected in open-loop operation with elaborate design of inputs. However, dueto safety or economic concerns, systems may have to be under feedback control,where only closed-loop data are available. In this context, it remainschallenging to implement DDPC using closed-loop data. In this paper, we proposea new DDPC method using closed-loop data by means of instrumental variables(IVs). By drawing from closed-loop subspace identification, the use of twoforms of IVs is suggested to address the closed-loop issues caused by feedbackcontrol and the correlation between inputs and noise. Furthermore, a new DDPCformulation with a novel IV-inspired regularizer is proposed, where a balancebetween control cost minimization and weighted least-squares data fitting canbe made for improvement of control performance. Numerical examples andapplication to a simulated industrial furnace showcase the improved performanceof the proposed DDPC based on closed-loop data.

Data-Driven Predictive Control Using Closed-Loop Data: An Instrumental Variable Approach