Mobile systems, such as ground robots and electric vehicles, inherently operate in stochastic environments where load demands are largely unknown. Onboard energy storage, most commonly an electrochemical battery system, can significantly constrain operation. As such, mission planning and control of mobile systems can benefit from a priori knowledge about battery dynamics and constraints, especially the rate-capacity and recovery effects. To help overcome overly conservative predictions common with most existing battery remaining run-time algorithms, a prediction scheme was proposed. For characterization of a priori unknown power loads, an unsupervised Gaussian mixture routine identifies/clusters the measured power loads, and a jump-Markov chain characterizes the load transients. With the jump-Markov load forecasts, a model-based particle filter scheme predicts battery remaining run-time. Monte Carlo simulation studies demonstrate the marked improvement of the proposed technique. It was found that the increase in computational complexity from using a particle filter was justified for power load transient jumps greater than 13.4% of total system power. A multivariable reliability method was developed to assess the feasibility of a planned mission. The probability of mission completion is computed as the reliability integral of mission time exceeding the battery run-time. Because these random variables are inherently dependent, a bivariate characterization was necessary and a method is presented for online estimation of the process correlation via Bayesian updating. Finally, to abate transient shutdown of mobile systems, a model predictive control scheme is proposed that enforces battery terminal voltage constraints under stochastic loading conditions. A Monte Carlo simulation study of a small ground vehicle indicated significant improvement in both time and distance traveled as a result. For evaluation of the proposed methodologies, a laboratory terrain environment was designed and constructed for repeated mobile system discharge studies. The test environment consists of three distinct terrains. For each discharge study, a small unmanned ground vehicle traversed the stochastic terrain environment until battery exhaustion. Results from field tests with a Packbot ground vehicle in generic desert terrain were also used. Evaluation of the proposed prediction algorithms using the experimental studies, via relative accuracy and [alpha]-[lambda] prognostic metrics, indicated significant gains over existing methods. / text

This work considers how various integrated energy systems can be managed in order to provide economic or energetic benefits. Energy systems can gain additional degrees of freedom by incorporating some form of energy storage (in this work, thermal energy storage), and the increasing penetration of smart grid technologies provides a wealth of data for both modeling and management. Data used for the system models here come primarily from the Pecan Street Smart Grid Demonstration Project in Austin, Texas, USA. Other data are from the Austin Energy Mueller Energy Center and the University of Texas Hal C. Weaver combined heat and power plant. Systems considered in this work include thermal energy storage, chiller plants, combined heat and power plants, turbine inlet cooling, residential air conditioning, and solar photovoltaics. These systems are modeled and controlled in integrated environments in order to provide system benefits. In a district cooling system with thermal energy storage, combined heat and power, and turbine inlet cooling, model-based optimization strategies are able to reduce peak demand and decrease cooling electricity costs by 79%. Smart grid data are employed to consider a system of 900 residential homes in Austin. In order to make the system model tractable for a model predictive controller, a reduced-order home modeling strategy is developed that maps thermostat set points to air conditioner electricity consumption. When the model predictive controller is developed for the system, the system is able to reduce total peak demand by 9%. Further work with the model of 900 residential homes presents a modified dual formulation for determining the optimal prices that produce a desired result in the residential homes. By using the modified dual formulation, it is found that the optimal pricing strategy for peak demand reduction is a critical peak pricing rate structure, and that those prices can be used in place of centralized control strategies to achieve peak reduction goals. / text

Cyber-Physical Systems (CPS) are systems of collaborating computational elements controlling physical entities via communication. Such systems involve control processes of physical entities and computational processes. The control complexities originated from the physical dynamics and systematic constraints are difficult for traditional control approaches (e.g., PID control) to handle without an exponential increase in design/test etc. costs. Model predictive control (MPC) predicts and produces optimized control inputs based on its predictive model according to a cost function under given constraints. This control scheme has some attractive features for CPSs: it handles constraints systematically, and generates behavior prediction with respective control inputs simultaneously. However, MPC approaches are computationally intensive, and the computation burden generally grows as a predictive model more closely approximates a nonlinear plant (in order to achieve more accurate behavior). The computational burden of predictive methods can be addressed through model reduction at the cost of higher divergence between prediction and actual behavior. This work introduces a metric called uncontrollable divergence, and proposes a mechanism using the metric to select the model to use in the predictive controller (assuming that a set of predictive models are available). The metric reveals the divergence between predicted and true states caused by return time and model mismatch. More precisely, a map of uncontrollable divergence plotted over the state space gives the criterion to judge where a specific model can outperform others. With this metric and the mechanism, this work designs a controller that switches at runtime among a set of predictive controllers in which respective models are deployed. The resulting controller is a hybrid predictive controller. In addition to design and runtime tools, this work also studies stability conditions for hybrid model predictive controllers in two approaches. One is average dwell time based, and it does not rely on the offline computation that studies the system properties. The other one uses a reference Lyapunov function instead of multiple Lyapunov functions derived from multiple predictive controllers. This approach implicitly depends on the offline numerical solutions of certain systematic properties. The term "boundedness" is preferable in this context since it accepts numerical error and approximations. Two examples, vertical takeoff and landing aerial vehicle control and ground vehicle control, are used to demonstrate the approach of hybrid MPC.

Arbetet har utförts på Saab Bofors Dynamics i Karlskoga och dess syfte var att undersöka om det är möjligt att använda modellbaserad prediktionsreglering, MPC, vid slutfasstyrning av en viss typ av robot. Som referensram används linjärkvadratisk reglering, LQ, eftersom denna reglermetod har undersökts tidigare och visat sig fungera bra vid slutfasstyrning, dock för en annan typ av robot. Anledningen till att man vill undersöka om det är möjligt att använda MPC är att styrlagen enkelt tar hand om begränsningar på systemet på ett direkt och intuitivt sätt. Styrlagarnas uppgift är att styra en robot i dess slutfas då det finns krav och önskemål på roboten som bör vara uppfyllda. Till exempel finns det begränsningar på styrsignalen samt önskemål om att träff ska ske i en viss träffpunkt och även med en viss träffvinkel. För att utvärdera resultaten undersöks och jämförs de två styrlagarnas prestanda och robusthet. För att kunna utvärdera styrlagarnas egenskaper och jämföra dem implementeras de båda i en befintlig detaljerad simuleringsmiljö, som har utvecklats på Saab Bofors Dynamics i Karlskoga. De prestanda och robusthetstester som har utförts uppvisar små skillnader på de två styrlagarna och slutsatsen blir därmed att det är möjligt att använda modellbaserad prediktionsreglering vid slutfasstyrning av en viss typ av robot eftersom det sedan tidigare är känt att linjärkvadratisk reglering är en bra styrlag att använda. För att se vilken av de två styrlagarna som är bäst vid slutfasstyrning av en viss typ av robot behöver det göras vissa ändringar och mer detaljerade undersökningar utföras.

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

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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.

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.