The actualization of real-time economically optimal process operation requires proper integration of real-time optimization (RTO) and dynamic control. This dissertation addresses the integration problem and provides a formal design technique that properly integrates RTO and model predictive control (MPC) under parametric uncertainties. The task is posed as an adaptive extremum-seeking control (ESC) problem in which the controller is required to steer the system to an unknown setpoint that optimizes a user-specified objective function. The integration task is first solved for linear uncertain systems. Then a method of determining appropriate excitation conditions for nonlinear systems with uncertain reference setpoint is provided. Since the identification of the true cost surface is paramount to the success of the integration scheme, novel parameter estimation techniques with better convergence properties are developed. The estimation routine allows exact reconstruction of the system's unknown parameters in finite-time. The applicability of the identifier to improve upon the performance of existing adaptive controllers is demonstrated. Adaptive nonlinear model predictive controllers are developed for a class of constrained uncertain nonlinear systems. Rather than relying on the inherent robustness of nominal MPC, robustness features are incorporated in the MPC framework to account for the effect of the model uncertainty. The numerical complexity and/or the conservatism of the resulting adaptive controller reduces as more information becomes available and a better uncertainty description is obtained. Finally, the finite-time identification procedure and the adaptive MPC are combined to achieve the integration task. The proposed design solves the economic optimization and control problem at the same frequency. This eliminates the ensuing interval of "no-feedback" that occurs between economic optimization interval, thereby improving disturbance attenuation. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2008-08-08 12:30:47.969

No description available.

No description available.

This thesis proposes methods and solutions to improve the choice and the optimal use of renewable energies in buildings. The heating load assessment is transformed into a control problem where the regulator calculates the optimal heating load of the building. The proposed regulator for this aim is Model Predictive Programming (MPP), which is obtained by modifying Model Predictive Control (MPC). The required information by MPP is a low order building model and data records of the local weather. Therefore, we propose a modelling method in which the detailed model of the building is projected on a reduced order model having its structure obtained from physical knowledge. For the control of the multi source system, we proposed a Building Energy Management System (BEMS) which is divided in two parts: the first for the building temperature control and the second for the source control. For building thermal control we utilize MPC, for which we propose a new cost function because the classical one does not minimize the energy consumption. The proposed cost function permits to maintain the thermal comfort with minimal energy consumption. We formulate this function such that it can be optimized by using Linear Programming (LP) algorithm. To be able to use LP we give a solution to linearization of the building model based on the physical knowledge, which permits to use the model on the entire operating range. For the source control, we propose a solution which takes into account the command given by MPC in order to use the energy resources more effectively. The proposed control system is evaluated and compared with two PID based BEMS, against comfort and energetic criteria. The evaluation is performed in emulation on a reference detached house. The obtained results show that the proposed control system always maintains the thermal comfort in the building, reduces the energy consumption and the wear and tear of the hydraulic and heat pumps from the heating system.

Quasi-keyhole, including plasma keyhole and double-sided welding, is a novel approach proposed to operate the keyhole arc welding process. It can result in a high quality weld, but also raise higher demand of the operator. A computer control system to detect the keyhole and control the arc current can improve the performance of the welding process. To this effect, developing automatic pipe welding, instead of manual welding, is a hot research topic in the welding field. The objective of this research is to design an automatic quasi-keyhole pipe welding system that can monitor the keyhole and control its establishment time to track the reference trajectory as the dynamic behavior of welding processes changes. For this reason, an automatic plasma welding system is proposed, in which an additional electrode is added on the back side of the workpiece to detect the keyhole, as well as to provide the double-side arc in the double-sided arc welding mode. In the automatic pipe welding system the arc current can be controlled by the computer controller. Based on the designed automatic plasma pipe welding system, two kinds of model predictive controller − linear and bilinear − are developed, and an optimal algorithm is designed to optimize the keyhole weld process. The result of the proposed approach has been verified by using both linear and bilinear model structures in the quasi-keyhole plasma welding (QKPW) process experiments, both in normal plasma keyhole and double-sided arc welding modes.

A multivariable control technique is proposed for a type of nonlinear system with parameter intervals. The control is based upon the feedback linearization scheme called Generic Model Control, and alters the control calculation by utilizing parameter intervals, employing an adaptive step, averaging control predictions, and applying an interval problem solution. The proposed approach is applied in controlling both a linear and a nonlinear arc welding system as well in other simulations of scalar and multivariable systems.

The lymphatic system is crucial for normal physiologic function, performing such basic functions as maintaining tissue fluid balance, trafficking immune cells, draining interstitial proteins, as well as transporting fat from the intestine to the blood. To perform these functions properly, downstream vessels (known as collecting lymphatics) actively pump like the heart to dynamically propel lymph from the interstitial spaces of the body to the blood vasculature. However, despite the fact that lymphatics are so important, there exists very little knowledge regarding the details of this active pumping. Specifically, it is known that external mechanical loading such as fluid shear stress and circumferential stress due to transmural pressure affect pumping response; however, anything other than simple, static relationships remain unknown. Because mechanical environment has been implicated in lymphatic diseases such as lymphedema, understanding these dynamic relationships between lymphatic pumping and mechanical loading during normal function are crucial to grasp before these pathologies can be unraveled. For this reason, this thesis describes several tools developed to study lymphatic function in response to the unique mechanical loads these vessels experience both in vitro and ex vivo. Moreover, this work investigates how shear stress sensitivity is affected by transmural pressure and how the presence of dynamic shear independently affects lymphatic contractile function.

In this master’s thesis we study how Model Predictive Control (MPC) can be fitted into an existing control system to handle state constraints. We suggest the use of reference governing based on the predictive control methodology. The platform for the survey is Saabs unmanned helicopter Skeldar. We develop and investigate different Reference Governor(RG) formulations that can be used together with the already existing stabilizing control system. These different setups show various features regarding model predictive control. One setup is complemented with a pre-filter to prevent aggressive actuator control in response to set-point changes, while the other is developed to handle this in the MPC framework. We also show that one of these RGs can be extended to guarantee stability and convergence. Implementation and real time requirements are also considered in this thesis. For this two different QP-solvers have been used for online solving of the optimization problem that arises from the MPC formulations. For evaluation and analysis the solutions are implemented in an advanced simulation environment developed at Saab and in a hardware-in-the-loop avionics test rig for the Skeldar system.

A two-level optimal coordination control approach for energy storage and conventional generation consisting of advanced frequency control and stochastic optimal dispatch is proposed to deal with the real power balancing control problem introduced by variable renewable energy sources (RESs) in power systems. In the proposed approach, the power and energy constraints on energy storage are taken into account in addition to the traditional power system operational constraints such as generator output limits and power network constraints. The advanced frequency control level which is based on the robust control theory and the decentralized static output feedback design is responsibl e for the system frequency stabilization and restoration, whereas the stochastic optimal dispatch level which is based on the concept of stochastic model predictive control (SMPC) determines the optimal dispatch of generation resources and energy storage under uncertainties introduced by RESs as well as demand. In the advanced frequency control level, low-order decentralized robust frequency controllers for energy storage and conventional generation are simultaneously designed based on a state-space structure-preserving model of the power system and the optimal controller gains are solved via an improved linear matrix inequality algorithm. In the stochastic optimal dispatch level, various optimization decomposition techniques including both primal and dual decompositions together with two different decomposition schemes (i.e. scenario-based decomposition and temporal-based decomposition) are extensively investigated in terms of convergence speed due to the resulting large-scale and computationally demanding SMPC optimization problem. A two-stage mixed decomposition method is conceived to achieve the maximum speedup of the SMPC optimization solution process. The underlying control design philosophy across the entire work is the so-called time-scale matching principle, i.e. the conventional generators are mainly responsible to balance the low frequency components of the power variations whereas the energy storage devices because of their fast response capability are employed to alleviate the relatively high frequency components. The performance of the proposed approach is tested and evaluated by numerical simulations on both the WECC 9-bus system and the IEEE New England 39-bus system.

Current robot manipulation and control paradigms have largely been developed for static or highly structured environments such as those common in factories. For most techniques in robot trajectory generation, such as heuristic-based geometric planning, this has led to putting a high cost on contact with the world. This approach and methodology can be prohibitive to robots operating in many unmodeled and dynamic environments. This dissertation presents work on using haptic based feedback (torque and tactile sensing) to formulate a controller for robot manipulation in clutter. We define “clutter” as any environment in which we expect the robot to make both incidental and purposeful contact while maneuvering and manipulating. The controllers developed in this dissertation take the form of single or multi-time step Model Predictive Control (a form of optimal control which incorporates feedback) which attempts to regulate contact forces at multiple locations on a robot arm while reaching to a goal. The results and conclusions in this dissertation are based on extensive testing in simulation (tens of thousands of trials) and testing in realistic scenarios with real robots incorporating tactile sensing. The approach is novel in the sense that it allows contact and explicitly incorporate the contact and predictive model of the robot arm in calculating control effort at every time step. The expected broader impact of this research is progress towards a new foundation of reactive feedback controllers that will include a higher likelihood of success in many constrained and dynamic scenarios such as reaching into containers without line of sight, maneuvering in cluttered search and rescue situations or working with unpredictable human co-workers.