1384310

11374

Generic Model Control (GMC) is a control algorithm capable of using nonlinear process model directly. Parameters in GMC controllers are easily tuned, and measurable disturbances can be compensated e€ectively. However, the existence of large modeling errors and unmeasurable disturbances will make the performance of GMC deteriorate. In this paper, based on the theory of Strong Tracking Filter (STF), a new approach to Adaptive Generic Model Control (AGMC) is proposed. Two AGMC schemes are developed. The ®rst is a parameter-estimation-based AGMC. After introducing a new concept of Input Equivalent Disturbance (IED), another AGMC scheme called IED-estimation-based AGMC is further proposed. The unmeasurable disturbance and structural process/model mismatches can be e€ectively overcome by the second AGMC scheme. The laboratory experimental results on a three-tank-system demonstrate the e€ectiveness of the proposed AGMC approach.

337

Nonlinear processes , Strong tracking ®lter , Adaptive control , Generic model control

Studia Iranica

350

350