An Adaptive Dual MPC Scheme based on Output Error Models Parameterized using Generalized Orthonormal Basis Filters. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Kumar, Kunal; Patwardhan, Sachin C.; Noronha, Santosh
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 9077
• Page End: 9082
• Publication Date: 2017-07
• Subjects: Dual control -- MPC -- Adaptive Control -- Predictive Control -- Orthogonal Basis Filters -- Recursive Parameter Estimation; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1644
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: A significant fraction of industrial MPC schemes employ linear prediction models. Closed loop performance of a linear model based MPC scheme can deteriorate over a period of time if the prediction model is not updated to account for the changing operating conditions. A possible remedy to this problem is on-line update of the model parameters under the closed loop conditions. An effective way of handling this problem is through dual control, which directs the plant output towards a reference setpoint and simultaneously injects probing signals into the plant to get information-rich data. In this work, an adaptive dual MPC scheme is developed for controlling MIMO systems based on output error models (OE) parameterized using generalized orthogonal basis filters (GOBF). A nominal model is initially developed using o-ine identification exercise. The Fourier coe¢cients of GOBF-OE models are then updated online using recursive least squares algorithm. Similar to Kumar et al. [2015], the MPC formulation is modified to include terms that are sensitive to the parameter covariance and are capable of injecting probing perturbations into the system as and when required. A distinguishing feature of the proposed work is the use of state space realizations of GOBF networks for model development and prediction. Simulation studies using the benchmark quadruple tank system (Johansson [2000]) reveal that the proposed approach provides su¢cient degrees of freedom to excite the plant in closed loop for generating information rich data for model parameter estimation.