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.

Modern power networks are being driven ever closer to both their physical and operational limits. As a result, control systems are being increasingly relied on to assure satisfactory system performance. Power system stabilizers (PSSs) are one example of such controllers. Their purpose is to increase system damping and they are typically designed using a model of the network that is valid during nominal operating conditions. The limitation of this design approach is that during off-nominal operating conditions, such as those triggered by daily load fluctuations, performance of the controller can degrade. The research presented in this report attempts to evaluate the possibility of employing an adaptive PSS as a means of avoiding the performance degradation precipitated by off-nominal operation. Conceptually, an adaptive PSS would be capable of identifying changes in the network and then adjusting its parameters to ensure suitable damping of the identified network. This work begins with a detailed look at the identification algorithm employed followed by a similarly detailed examination of the control algorithm that was used. The results of these two investigations are then combined to allow for a preliminary assessment of the performance that could be expected from an adaptive PSS. The results of this research suggest that an adaptive PSS is a possibility but further work is needed to confirm this finding. Testing using more complex network models must be carried out, details pertaining to control parameter tuning must be resolved and closed-loop time domain simulations using the adaptive PSS design remain to be performed.

Starting a few decades ago, the unmanned wheeled vehicle research has drawn lately more attention, especially for off-road environment. As the demand to use electric vehicles increased, the need to conceptualize the use of electrically driven vehicles in autonomous operations became a target. That is because in addition to the fact that they are more environmentally friendly, they are also easier to control. This also gives another reason to enhance further the energy economy of those unmanned electric vehicles. Off-road vehicles research was always challenging, but in the present work the nature of the off-road land is utilized to benefit from in order to enhance the energy consumption of those vehicles. An algorithm for energy consumption optimization for electrically driven unmanned wheeled vehicles is presented. The algorithm idea is based on the fact that in off-road conditions, when the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill could be utilized to reduce the energy consumption for moving uphill if the dimensions of the ditch/hole were known a distance ahead. Two manipulated variables are evaluated: the wheels DC motors supply voltage and the DC armature current. The developed algorithm is analysed and compared to the PID speed iii controller and to the open-loop control of DC motors. The developed predictive controller achieved encouraging results compared to the PID speed control and also compared to the open-loop control. Also, the use of the DC armature current as a manipulated variable showed more noticeable improvement over using the DC input voltage. Experimental work was carried out to validate the predictive control algorithm. A mobile robot with two DC motor driven wheels was deployed to overcome a ditch-like hindrance. The experimental results verified the simulation results. A parametric study for the predictive control is conducted. The effect of changing the downhill angle and the uphill angle as well as the size of the prediction horizon on the consumed electric energy by the DC motors is addressed. The simulation results showed that, when using the proposed approach, the larger the prediction horizon, the lower the energy consumption is.

An increased penetration of renewable energy into the electric power grid is desirable from an environmental standpoint as well as an economical one. However, renewable sources such as wind and solar energy are often variable and intermittent, and additionally, are non-dispatchable. Also, the locations with the highest amount of available wind or solar may be located in areas that are far from areas with high levels of demand, and these areas may be under the control of separate, individual entities. In this dissertation, a method that coordinates these areas, accounts for the variability and intermittency, reduces the impact of renewable energy forecast errors, and increases the overall social benefit in the system is developed. The approach for the purpose of integrating intermittent energy sources into the electric power grid is considered from both the planning and operations stages. In the planning stage, two-stage stochastic optimization is employed to find the optimal size and location for a storage device in a transmission system with the goal of reducing generation costs, increasing the penetration of wind energy, alleviating line congestions, and decreasing the impact of errors in wind forecasts. The size of this problem grows dramatically with respect to the number of variables and constraints considered. Thus, a scenario reduction approach is developed which makes this stochastic problem computationally feasible. This scenario reduction technique is derived from observations about the relationship between the variance of locational marginal prices corresponding to the power balance equations and the optimal storage size. Additionally, a probabilistic, or chance, constrained model predictive control (MPC) problem is formulated to take into account wind forecast errors in the optimal storage sizing problem. A probability distribution of wind forecast errors is formed and incorporated into the original storage sizing problem. An analytical form of this constraint is derived to directly solve the optimization problem without having to use Monte-Carlo simulations or other techniques that sample the probability distribution of forecast errors. In the operations stage, a MPC AC Optimal Power Flow problem is decomposed with respect to physical control areas. Each area performs an independent optimization and variable values on the border buses between areas are exchanged at each Newton-Raphson iteration. Two modifications to the Approximate Newton Directions (AND) method are presented and used to solve the distributed MPC optimization problem, both with the intention of improving the original AND method by improving upon the convergence rate. Methods are developed to account for numerical difficulties encountered by these formula- tions, specifically with regards to Jacobian singularities introduced due to the intertemporal constraints. Simulation results show convergence of the decomposed optimization problem to the centralized result, demonstrating the benefits of coordinating control areas in the IEEE 57- bus test system. The benefit of energy storage in MPC formulations is also demonstrated in the simulations, reducing the impact of the fluctuations in the power supply introduced by intermittent sources by coordinating resources across control areas. An overall reduction of generation costs and increase in renewable penetration in the system is observed, with promising results to effectively and efficiently integrate renewable resources into the electric power grid on a large scale.