Parameters setting of a Data-Driven Model-Free Adaptive Controller for a coupled Two-Tank System

The Data Driven Control (DDC) methods represent a segment relatively new in the control theory domain. Model-Free Adaptive Control (MFAC) belongs to the DDC class and employs only data acquired from the process to control it. Using these data, an equivalent dynamic linearized model is developed applying the concept named pseudo-partial derivative (PPD). Among the linearization techniques, one often utilized is the Compact Form Dynamic Linearization (CFDL). This paper presents the parameters setting of the MFAC-CFDL algorithm for the efficient level control of a coupled two-tank system described by a Hammerstein model. Two sets of parameters were considered: (i) the weighting and step factors of the control law and of the PPD estimation algorithm and (ii) the initial conditions of input, output, and PPD. After determining the two sets of parameters, the performances obtained were compared with those generated by the level control with a nonlinear PID.