1. Real Time Nonlinear Model Predictive Control Strategy for Multivariable Coupled Tank System Kayode Owa Kayode Owa Supervisor - Sanjay Sharma University of Plymouth University of Plymouth UKACC PhD Presentation Showcase

2. Slide 2 Introduction  Most chemical processes are multivariable and have strong nonlinear dynamics.  Linear models and conventional controllers are not sufficient to handle these processes.  This creates challenges in developing nonlinear multi input multi output (MIMO) models and advance control strategies.  Background and motivation for research  Process dynamics change over time, equipment degrade and valves do wear out.  Original mathematical models tend to mismatch with the real plant.  Models are limited to small range of operations.

3. UKACC PhD Presentation Showcase Slide 3  Research methodology  System identification – use raw data for modelling  Wavelet activated neural network nonlinear model  Online real time optimisation using genetic algorithm (GA)  Nonlinear model predictive control (NMPC) strategy  Simulation and Real time practical implementation  Current status  Real time practical implementation stage for abnormal conditions  Contribution to knowledge  Novel approach using WNN-NMPC for coupled tank system (CTS)

4. UKACC PhD Presentation Showcase Slide 4 NMPC Strategy Results sSimulation Results sReal time Results (a) ANN (b) WNN (a) ANN mse=0.0049 ace=82.41 mse=0.0036 ace=55.01 mse=0.0046 ace=78.26 mse=0.0022 ace=65.92 mse=mean squared error, unit is m 2 ace=average controller energy, unit is v 2

5. UKACC PhD Presentation Showcase Slide 5 Conclusion  The proposed wavelet neural network (WNN) NMPC strategy is more efficient than ANN in MIMO case.  Real time optimisation (RTO) of the controller actions is achieved using GA.  Future works will check the robustness of this approach for abnormal conditions of plants dynamic.