摘要：AbstractAdaptive model predictive control (AMPC) schemes can prove beneficial in the control of non-linear time varying systems due to their ability to maintain closed-loop performance in the face of large transitions in operating conditions. In this work, two non-cooperative distributed adaptive MPC (dAMPC) schemes are proposed that are based on ARX models parameterized using generalized orthonormal basis filters (GOBF). Poles of the GOBF models estimated through offline estimation are kept fixed and only the Fourier coefficients are updated recursively online thereby avoiding drift in the pole locations. The system under consideration is decomposed into a number of subsystems coupled through the inputs. A separate local AMPC controller is implemented for each subsystem. Each local controller optimizes its input trajectories assuming the input trajectories of its neighbors fixed to the last computed values, and then shares the computed inputs with the neighbors. We consider both sequential and iterative distributed control strategies for the dAMPC development. The efficacy of the proposed dAMPC schemes is demonstrated using simulation studies on an octuple tank process. When the system is subjected to large transitions in operating points, the proposed dAMPC schemes generate closed loop performances comparable to a centralized adaptive MPC scheme while significantly reducing the average computation time.
关键词：KeywordsModel predictive controldistributed controladaptive controlorthonormal basis filtersrecursive least square estimation
