Indirect Adaptive Model Predictive Control Research Articles

Indirect adaptive model predictive control and its application to uncertain linear systems

SummaryWe consider constrained systems that are represented by uncertain models with unknown constant or slowly varying parameters. We propose an indirect adaptive model predictive control (IAMPC) approach where the prediction model can be adjusted during controller operation by a separately designed estimator that satisfies only a minimal set of assumptions. The controller guarantees constraint satisfaction despite the uncertainty in the parameters by means of a robust control invariant set, and input‐to‐state stability with respect to the estimation error by means of an appropriately designed method for adjusting the IAMPC prediction model and cost function based on the evolution of the parameter estimate. The controller has minimal computational overhead with respect to a nominal MPC and for the special case of uncertain linear systems, we obtain a constructive design procedure for the IAMPC which only solves quadratic programs during closed‐loop control.

IA-MPC supervised by fuzzy logic: an application to a mechatronics system

Depending strongly on the parameter tuning, the performances of model predictive control can be improved using the online process identification. Such an approach can't always ensure optimal results when the process parameters change with time. In this paper, an indirect adaptive model predictive control supervised by fuzzy logic is proposed to solve this issue. The predictive controller parameters are computed on line regarding measurable performance criteria and inequality constraints on control and output signals. A performance comparison in terms of overshoot, stability and rise time is carried out to highlight the relevance of this approach.

Data–Driven Indirect Adaptive Model Predictive Control

Kertas kerja ini membincangkan tentang reka bentuk Pengawal Ramalan Model Suai menggunakan kaedah Pengenalpastian Model Keadaan Ruang Sub–ruang bagi proses enapcemar teraktif. Penggunaan teknik Pengenalpastian Model Keadaan Ruang Sub–ruang di dalam kaedah kawalan tingkat gelangsar suai dibincangkan di mana pengenalpastian sub–ruang dalam talian menggunakan algoritma N4SID di perkenalkan bersama dengan rekabentuk Pengawal ramalan model. Pembangunan N4SID dalam talian di dalam kertas kerja ini menggunakan pengemaskini QR di mana gabungan di antara teknik kemaskini dan kemasbawah membolehkan pengadaptasi tingkap gelangsar. Di sini, untuk setiap langkah masa, bagi setiap data baru akan dimasukkan ke faktor R manakala data yang lama dibuang. Begitu juga, strategi bagi uraian nilai tunggal diperkenalkan ke dalam Pengawal Ramalan Model Suai tak langsung untuk masukan tambahan kawalan bagi sistem terkekang tak lelurus. Beberapa kajian simulasi bagi parameter kawalan berlainan di dalam pengawal/pengenalpastian algoritma dilaksanakan. Bagi reka bentuk Pengawal Ramalan Model Suai tak langsung, pengiraan masa yang terlibat dengan menggunakan pendekatan uraian nilai tunggal kurang berbanding dengan kaedah perancangan kuadratik dan keputusan yang memberangsangkan ini adalah sumbangan utama di dalam kertas kerja ini. Kata kunci: Pengawal suai; proses enapcemar teraktif; pengawal ramalan model; pengenalpastian sub–ruang This paper explores the design of Adaptive Model Predictive Control (AMPC) using Subspace State–space Model Identification (SMI) techniques for an activated sludge process. The implementation of SMI techniques in the adaptive sliding window control methods are discussed where the online subspace identification using Numerical State–space Subspace System Identification (N4SID) algorithm is proposed along with Model Predictive Control (MPC) design method. The online N4SID algorithm developed in this study makes use of the QR–updating where the combination of update and down date techniques enables sliding window adaptation. Here, at each time step, for the new experimental data added into R factor, the oldest data are removed. Also, the Singular Value Decomposition (SVD–based) strategy is proposed into Indirect AMPC (IAMPC) for the control increment input constrained nonlinear system. Several simulation studies for different control parameters in control/identification algorithm are performed. For the IAMPC control design, the computational times involved using an SVD approach shows less burdensome compared to Quadratic Programming (QP) method and such an interesting result is considered as one of the main contribution in this paper. Key words: Adaptive control; activated sludge process; model predictive control; subspace identification