SYSTEM IDENTIFICATION AND MODEL PREDICTIVE CONTROL FOR INTERACTING SERIES PROCESS WITH NONLINEAR DYNAMICS

This thesis discusses the empirical modeling using system identification technique and the implementation of a linear model predictive control with focus on interacting series processes. In general, a structure involving a series of systems occurs often in process plants that include processing sequences such as feed heat exchanger, chemical reactor, product cooling, and product separation. The study is carried out by experimental works using the gaseous pilot plant as the process. The gaseous pilot plant exhibits the typical dynamic of an interacting series process, where the strong interaction between upstream and downstream properties occurs in both ways. The subspace system identification method is used to estimate the linear model parameters. The developed model is designed to be robust against plant nonlinearities. The plant dynamics is first derived from mass and momentum balances of an ideal gas. To provide good estimations, two kinds of input signals are considered, and three methods are taken into account to determine the model order. Two model structures are examined. The model validation is conducted in open-loop and in closed-loop control system. Real-time implementation of a linear model predictive control is also studied. Rapid prototyping of such controller is developed using the available equipments and software tools. The study includes the tuning of the controller in a heuristic way and the strategy to combine two kinds of control algorithm in the control system. A simple set of guidelines for tuning the model predictive controller is proposed. Several important issues in the identification process and real-time implementation of model predictive control algorithm are also discussed. The proposed method has been successfully demonstrated on a pilot plant and a number of key results obtained in the development process are presented

Iterative learning control experimental results in twin-rotor device

This paper presents the results of applying the Iterative Learning Control algorithms to a Twin-Rotor Multiple-Input Multiple-Output System (TRMS) in order to achieve high performance in repetitive tracking of trajectories. The plant, which is similar to a prototype of helicopter, is characterized by its highly nonlinear and cross-coupled dynamics. In the first phase, the system is modelled using the Lagrangian approach and combining theoretical and experimental results. Thereafter, a hierarchical control architecture which combines a baseline feedback controller with an Iterative Learning Control algorithm is developed. Finally, the responses of the real device and a complete analysis of the learning behaviour are exposed.Postprint (published version

Wind Disturbance Suppression in Autopilot Design

Environmental conditions affects ship’s course. Hence, it affects velocity, and efficiency of fuel consumption, which is an important research topic nowadays. Therefore, it is important to take it into account in the design of ship’s autopilots. In this paper a method is proposed to compensate for wind’s influence, which is based on wavelet transform by introducing the so called wavelet anti-filter. The anti-filter is added to the feed-forward branch of the classic autopilot design scheme, which consists of feedback loop and PID controller. The anti-filter branch represents a modification of the classic scheme

Advanced control techniques for modern inertia based inverters

”In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques”--Abstract, page iv

Model Predictive Control Using Orthonormal Basis Filter

Proportional Integral Derivative (PID) controller is the most common controller that acts as standard tool in a process control industry. However, when interacting with Multiple Input and Multiple Output (MIMO) process, the interaction is difficult to be controlled by PID controller. Therefore, this project will focus on Model Predictive Control (MPC) that is one of optimization strategy that can control MIMO interaction by predicting the effect of potential control action. In this project, a mathematical model of Orthonormal Basis Filter (OBF) will be developed on the distillation column based on Wood-Berry model with a feedback control (a closed loop system). A simulation of MPC is done by using MATLAB coding while PID is simulated using SIMULINK. Based on the simulation, the performance of MPC and PID controller are evaluated by using the Integral Error Criteria: Integral Absolute Error (IAE), Integral of the Squared Error (ISE) and Integral of the time-weighted absolute error (ITAE) and also with total input variation. Lower integral error criteria and total input variation value indicate a better model accuracy and efficiency of controller for MIMO system

Flexible and robust control of heavy duty diesel engine airpath using data driven disturbance observers and GPR models

Diesel engine airpath control is crucial for modern engine development due to increasingly stringent emission regulations. This thesis aims to develop and validate a exible and robust control approach to this problem for speci cally heavy-duty engines. It focuses on estimation and control algorithms that are implementable to the current and next generation commercial electronic control units (ECU). To this end, targeting the control units in service, a data driven disturbance observer (DOB) is developed and applied for mass air ow (MAF) and manifold absolute pressure (MAP) tracking control via exhaust gas recirculation (EGR) valve and variable geometry turbine (VGT) vane. Its performance bene ts are demonstrated on the physical engine model for concept evaluation. The proposed DOB integrated with a discrete-time sliding mode controller is applied to the serial level engine control unit. Real engine performance is validated with the legal emission test cycle (WHTC - World Harmonized Transient Cycle) for heavy-duty engines and comparison with a commercially available controller is performed, and far better tracking results are obtained. Further studies are conducted in order to utilize capabilities of the next generation control units. Gaussian process regression (GPR) models are popular in automotive industry especially for emissions modeling but have not found widespread applications in airpath control yet. This thesis presents a GPR modeling of diesel engine airpath components as well as controller designs and their applications based on the developed models. Proposed GPR based feedforward and feedback controllers are validated with available physical engine models and the results have been very promisin

Fractional transformation-based decentralized robust control of a coupled-tank system for industrial applications

Petrochemical and dairy industries, waste management, and paper manufacturing fall under the category of process industries where flow and liquid control are essential. Even when liquids are mixed or chemically treated in interconnected tanks, the fluid and flow should constantly be observed and controlled, especially when dealing with nonlinearity and imperfect plant models. In this study, we propose a nonlinear dynamic multiple-input multiple-output (MIMO) plant model. This model is then transformed through linearization, a technique frequently utilized in the analysis and modeling of fractional processes, and decoupling for decentralized fixed-structure H-infinity robust control design. Simulation tests based on MATLAB and SIMULINK are subsequently executed. Numerous assessments are conducted to evaluate tracking performance, external disturbance re jection, and plant parameter fluctuations to gauge the effectiveness of the proposed model. The objective of this work is to provide a framework that anticipates potential outcomes, paving the way for implementing a reliable controller synthesis for MIMO-connected tanks in real-world scenarios.This research was partially funded by FONDECYT grant number 1200525 (V.L.) from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.)

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

A STUDY OF MODEL-BASED CONTROL STRATEGY FOR A GASOLINE TURBOCHARGED DIRECT INJECTION SPARK IGNITED ENGINE

To meet increasingly stringent fuel economy and emissions legislation, more advanced technologies have been added to spark-ignition (SI) engines, thus exponentially increase the complexity and calibration work of traditional map-based engine control. To achieve better engine performance without introducing significant calibration efforts and make the developed control system easily adapt to future engines upgrades and designs, this research proposes a model-based optimal control system for cycle-by-cycle Gasoline Turbocharged Direct Injection (GTDI) SI engine control, which aims to deliver the requested torque output and operate the engine to achieve the best achievable fuel economy and minimum emission under wide range of engine operating conditions. This research develops a model-based ignition timing prediction strategy for combustion phasing (crank angle of fifty percent of the fuel burned, CA50) control. A control-oriented combustion model is developed to predict burn duration from ignition timing to CA50. Using the predicted burn duration, the ignition timing needed for the upcoming cycle to track optimal target CA50 is calculated by a dynamic ignition timing prediction algorithm. A Recursive-Least-Square (RLS) with Variable Forgetting Factor (VFF) based adaptation algorithm is proposed to handle operating-point-dependent model errors caused by inherent errors resulting from modeling assumptions and limited calibration points, which helps to ensure the proper performance of model-based ignition timing prediction strategy throughout the entire engine lifetime. Using the adaptive combustion model, an Adaptive Extended Kalman Filter (AEKF) based CA50 observer is developed to provide filtered CA50 estimation from cyclic variations for the closed-loop combustion phasing control. An economic nonlinear model predictive controller (E-NMPC) based GTDI SI engine control system is developed to simultaneously achieve three objectives: tracking the requested net indicated mean effective pressure (IMEPn), minimizing the SFC, and reducing NOx emissions. The developed E-NMPC engine control system can achieve the above objectives by controlling throttle position, IVC timing, CA50, exhaust valve opening (EVO) timing, and wastegate position at the same time without violating engine operating constraints. A control-oriented engine model is developed and integrated into the E-NMPC to predict future engine behaviors. A high-fidelity 1-D GT-POWER engine model is developed and used as the plant model to tune and validate the developed control system. The performance of the entire model-based engine control system is examined through the software-in-the-loop (SIL) simulation using on-road vehicle test data