Soft lightweight robots provide an inherently safe solution to using robots in unmodeled environments by maintaining safety without increasing cost through expensive sensors. Unfortunately, many practical problems still need to be addressed before soft robots can become useful in real world tasks. Unlike traditional robots, soft robot geometry is not constant but can change with deflation and reinflation. Small errors in a robot's kinematic model can result in large errors in pose estimation of the end effector. This error, coupled with the inherent compliance of soft robots and the difficulty of soft robot joint angle sensing, makes it very challenging to accurately control the end effector of a soft robot in task space. However, this inherent compliance means that soft robots lend themselves nicely to coordinated multi-arm manipulation tasks, as deviations in end effector pose do not result in large force buildup in the arms or in the object being manipulated. Coordinated, multi-arm manipulation with soft robots is the focus of this thesis. We first developed two tools enabling multi-arm manipulation with soft robots: (1) a hybrid servoing control scheme for task space control of soft robot arms, and (2) a general base placement optimization for the robot arms in a multi-arm manipulation task. Using these tools, we then developed and implemented a simple multi-arm control scheme. The hybrid servoing control scheme combines inverse kinematics, joint angle control, and task space servoing in order to reduce end effector pose error. We implemented this control scheme on two soft robots and demonstrated its effectiveness in task space control. Having developed a task space controller for soft robots, we then approached the problem of multi-arm manipulation. The placement of each arm for a multi-arm task is non-trivial. We developed an evolutionary optimization that finds the optimal arm base location for any number of user-defined arms in a user-defined task or workspace. We demonstrated the utility of this optimization in simulation, and then used it to determine the arm base locations for two arms in two real world coordinated multi-arm manipulation tasks. Finally, we developed a simple multi-arm control scheme for soft robots and demonstrated its effectiveness using one soft robot arm, and one rigid robot with low-impedance torque control. We placed each arm base in the pose determined by the base placement optimization, and then used the hybrid servoing controller in our multi-arm control scheme to manipulate an object through two desired trajectories.

Temperature is a very important process parameter in Friction Stir Welding (FSW), but until lately active control of temperature has not been practiced. Recently, temperature control via a PID controller has proven to be effective. Model Predictive Control (MPC) is a control method that holds promise, but has not been attempted in FSW before. Two different model forms are developed for MPC and are evaluated. The first is a simple first-order plus dead time (FOPDT) model. The second is the Hybrid Heat Source model and is more complex; it combines the heat source method and a 1D discretized thermal model of the FSW tool. Model parameters were determined by fitting model predictions to actual weld data. The models were evaluated for their performance in modeled and unmodeled disturbances once the process was already at a quasi steady state condition and also were evaluated for control immediately after plunge. The FOPDT based MPC controller has very good performance and was comparable in performance to previously proven and well-tuned PID controllers. For small modeled disturbances the FOPDT controller settled within 1°C of the setpoint in 10s with almost no oscillations and only 2°C of overshoot. For large unmodeled disturbances, the FOPDT controller settled within 1°C of the setpoint in 30s with no oscillations and 16°C of overshoot. For the same disturbances, the PID servo controller settled in 30s with no oscillations and 9°C of overshoot, and the PID regulator controller settled in 15s but had almost a full oscillation and 13°C of overshoot.The Hybrid Heat Source MPC controller and the PID regulator controller were also able to control temperature within 5°C of the setpoint immediately after the plunge during the highly transient portion of the weld, which previously had been assumed to be too difficult to control. The PID regulator controller had a high degree of variability between the two runs (a settling time of 10s and 30s, and .5 and 4.5 oscillations before settling), but settled quickly and once settled was able to hold the temperature within 2°C of the setpoint. The HHS MPC controller on the other hand had far fewer oscillations (0 and 1 oscillation) before settling, but could only hold the temperature within 5°C of the setpoint. Both of these controllers performed far better than the FOPDT MPC and PID servo controllers.

Diabetes is a growing chronic disease and a worldwide problem. Without any available cure in sight for the public other methods needs to be applied to increase the life quality of diabetic patients. Artificial Pancreas (AP), a concept of having a closed loop system to control the glucose level on Type 1 Diabetes (T1D) patients has been introduced and is under development. In this thesis, Model Predictive Control (MPC) has been re implemented from scratch in MATLAB/SIMULINK with associated Kalman filter and prediction function. It was implemented in the latest version of the UVA/Padova Simulator which is a tool approved by FDA for simulating diabetes treatment in order to speed up the AP development. Different MPC cost functions where tested together with integral action on a simplified system using a linear approximation of a population model. It was implemented and tuned with a new simulation tuning method using Genetic Algorithm (GA). It showed that the quadratic cost function without integral action was the best with respect to performance and time efficiency. 3 hours was the best prediction horizon and was used for the individualized tuning using the University of Virginia (UVA)/Padova simulator. For the individualized MPC, models identified by the University of Padova were used. These simulations showed that an individualized model could be used for improved T1D treatment compared to an average population model even though the results were mixed. Almost all of the patients got improved treatment with the closed treatment and non hypoglycemic event occurred. The identification of better models is a great challenge for the future development of the AP MPC due to the excitation problems.

<p>Energy management has become a major concern in the past two decades with the increasing energy prices, overutilization of natural resources and increased carbon emissions. According to the department of Energy the industrial sector solely consumes 22.4% of the energy produced in the country [1]. This calls for an urgent need for the industries to design and implement energy efficient practices by analyzing the energy consumption, electricity data and making use of energy efficient equipment. Although, utility companies are providing incentives to consumer participating in Demand Response programs, there isn’t an active implementation of energy management principles from the consumer’s side. Technological advancements in controls, automation, optimization and big data can be harnessed to achieve this which in other words is referred to as “Smart Manufacturing”. In this research energy management techniques have been designed for two SEU (Significant Energy Use) equipment HVAC systems, Compressors and load shifting in manufacturing environments using control and optimization.</p> <p>The addressed energy management techniques associated with each of the SEUs are very generic in nature which make them applicable for most of the industries. Firstly, the loads or the energy consuming equipment has been categorized into flexible and non-flexible loads based on their priority level and flexibility in running schedule. For the flexible loads, an optimal load scheduler has been modelled using Mixed Integer Linear Programming (MILP) method that find carries out load shifting by using the predicted demand of the rest of the plant and scheduling the loads during the low demand periods. The cases of interruptible loads and non-interruptible have been solved to demonstrate load shifting. This essentially resulted in lowering the peak demand and hence cost savings for both “Time-of-Use” and Demand based price schemes. </p> <p>The compressor load sharing problem was next considered for optimal distribution of loads among VFD equipped compressors running in parallel to meet the demand. The model is based on MILP problem and case studies was carried out for heavy duty (>10HP) and light duty compressors (<=10HP). Using the compressor scheduler, there was about 16% energy and cost saving for the light duty compressors and 14.6% for the heavy duty compressors</p> <p>HVAC systems being one of the major energy consumer in manufacturing industries was modelled using the generic lumped parameter method. An Electroplating facility named Electro-Spec was modelled in Simulink and was validated using the real data that was collected from the facility. The Mean Absolute Error (MAE) was about 0.39 for the model which is suitable for implementing controllers for the purpose of energy management. MATLAB and Simulink were used to design and implement the state-of-the-art Model Predictive Control for the purpose of energy efficient control. The MPC was chosen due to its ability to easily handle Multi Input Multi Output Systems, system constraints and its optimal nature. The MPC resulted in a temperature response with a rise time of 10 minutes and a steady state error of less than 0.001. Also from the input response, it was observed that the MPC provided just enough input for the temperature to stay at the set point and as a result led to about 27.6% energy and cost savings. Thus this research has a potential of energy and cost savings and can be readily applied to most of the manufacturing industries that use HVAC, Compressors and machines as their primary energy consumer.</p><br>

The future of the electric grid requires advanced control technologies to reliably integrate high level of renewable generation and residential and small commercial distributed energy resources (DERs). Flexible loads are known as a vital component of future power systems with the potential to boost the overall system efficiency. Recent work has expanded the role of flexible and controllable energy resources, such as energy storage and dispatchable demand, to regulate power imbalances and stabilize grid frequency. This leads to the DER aggregators to develop concepts such as the virtual energy storage system (VESS). VESSs aggregate the flexible loads and energy resources and dispatch them akin to a grid-scale battery to provide flexibility to the system operator. Since the level of flexibility from aggregated DERs is uncertain and time varying, the VESSs’ dispatch can be challenging. To optimally dispatch uncertain, energy-constrained reserves, model predictive control offers a viable tool to develop an appropriate trade-off between closed-loop performance and robustness of the dispatch. To improve the system operation, flexible VESSs can be formulated probabilistically and can be realized with chance-constrained model predictive control. The large-scale deployment of flexible loads needs to carefully consider the existing regulation schemes in power systems, i.e., generator droop control. In this work first, we investigate the complex nature of system-wide frequency stability from time-delays in actuation of dispatchable loads. Then, we studied the robustness and performance trade-offs in receding horizon control with uncertain energy resources. The uncertainty studied herein is associated with estimating the capacity of and the estimated state of charge from an aggregation of DERs. The concept of uncertain flexible resources in markets leads to maximizing capacity bids or control authority which leads to dynamic capacity saturation (DCS) of flexible resources. We show there exists a sensitive trade-off between robustness of the optimized dispatch and closed-loop system performance and sacrificing some robustness in the dispatch of the uncertain energy capacity can significantly improve system performance. We proposed and formulated a risk-based chance constrained MPC (RB-CC-MPC) to co-optimize the operational risk of prematurely saturating the virtual energy storage system against deviating generators from their scheduled set-point. On a fast minutely timescale, the RB-CC-MPC coordinates energy-constrained virtual resources to minimize unscheduled participation of ramp-rate limited generators for balancing variability from renewable generation, while taking into account grid conditions. We show under the proposed method it is possible to improve the performance of the controller over conventional distributionally robust methods by more than 20%. Moreover, a hardware-in-the-loop (HIL) simulation of a cyber-physical system consisting of packetized energy management (PEM) enabled DERs, flexible VESSs and transmission grid is developed in this work. A predictive, energy-constrained dispatch of aggregated PEM-enabled DERs is formulated, implemented, and validated on the HIL cyber-physical platform. The experimental results demonstrate that the existing control schemes, such as AGC, dispatch VESSs without regard to their energy state, which leads to unexpected capacity saturation. By accounting for the energy states of VESSs, model-predictive control (MPC) can optimally dispatch conventional generators and VESSs to overcome disturbances while avoiding undesired capacity saturation. The results show the improvement in dynamics by using MPC over conventional AGC and droop for a system with energy-constrained resources.

We consider a new approach to digital human simulation, using Model Predictive Control (MPC). This approach permits a virtual human to react online to unanticipated disturbances that occur in the course of performing a task. In particular, we predict the motion of a virtual human in response to two different types of real world disturbances: impulsive and sustained. This stands in contrast to prior approaches where all such disturbances need to be known a priori and the optimal reactions must be computed off line. We validate this approach using a planar 3 degrees of freedom serial chain mechanism to imitate the human upper limb. The response of the virtual human upper limb to various inputs and external disturbances is determined by solving the Equations of Motion (EOM). The control input is determined by the MPC Controller using only the current and the desired states of the system. MPC replaces the closed loop optimization problem with an open loop optimization allowing the ease of implementation of control law. Results presented in this thesis show that the proposed controller can produce physically realistic adaptive simulations of a planar upper limb of digital human in presence of impulsive and sustained disturbances.

<p>In the pulp industry, digesters are used to disolve lignin in wood chips. The concentration of lignin is measured and is called the Kappa number. In this thesis, the question of whether an online Kappa sensor, taking samples from the mid-phase of the digester, is useful or not is analyzed. For the samples to be useful, there has to be a relationship between the measured Kappa at the mid- phase and the measured Kappa in the blowpipe at the bottom of the digester. An ARX model of the lower part of the digester has been estimated. Despite a lot of noise, it seems that it might be possible to use the mid-phase samples and for this model predict the blowpipe flow Kappa signal. It is concluded that the mid-phase samples should be further improved to be more useful. The mid-phase samples have also been used in another ARX model, this time to LP-filter these values without time loss. </p><p>Another important issue has been to examine if the existing controller is good or not. In order to be able to compare it with other controllers, a simulator has been created in MATLAB - Simulink. Test results from this simulator show that the existing controller's use of the mid-phase Kappa samples improves its performance. For a simplified digester model, the existing controller has also been compared with an MPC controller. This test shows that the MPC controller is significantly better. Hence, the conclusion in this thesis is that it might be interesting to study MPC further using a more advanced model.</p>

The main objective of this thesis was to develop appropriate system protection schemes against two important causes of failure in power systems, namely, long-term voltage instability and cascade tripping of overloaded transmission lines, mainly due to overloading. To this purpose a distributed undervoltage load shedding scheme against voltage instability, and a centralized protection meant to alleviate line overload are proposed. The former, through the chosen system protection scheme characteristics, has the ability to adjust its actions to the disturbance location and severity. This behavior is achieved without resorting to a dedicated communication network. The distributed controllers do not exchange information, but are rather informed of their respective actions through voltage measurements. Neither do the controllers require a model of the system. This and the absence of communication makes the protection scheme simple and reliable. The other protection scheme, inspired of model predictive control, is aimed at bringing the currents in the overloaded lines below their limits in the time interval left by protections, while accounting for constraints on control changes. Its closed-loop nature allows to compensate for model uncertainties and measurement noise. In order to tune the proposed system protection schemes parameters and validate their performance it was preferred to detect plausible cascading event scenarios. To this purpose, an algorithm meant to identify such complex sequences has been developed. It encompasses hidden failures and the resulting system response. The tests performed on small systems as well as on a real-life one confirm not only that proposed protection schemes appropriately deal with the problems for which they were designed, but also that they cooperate satisfactorily for combined voltage and thermal problems that are beyond their individual capabilities.

The automotive industry is required to deal with increasingly stringent legislationfor greenhouse gases. Hybrid Electric Vehicles, HEV, are gaining acceptance as thefuture path of lower emissions and fuel consumption. The increased complexityof multiple prime movers demand more advanced control systems, where futuredriving conditions also becomes interesting. For a plug-in Hybrid Electric Vehicle,PIHEV, it is important to utilize the comparatively inexpensive electric energybefore the driving cycle is complete, this for minimize the cost of the driving cycle,since the battery in a PIHEV can be charged from the grid. A strategy with lengthinformation of the driving cycle from a global positioning system, GPS, couldreduce the cost of driving. This by starting to blend the electric energy with fuelearlier, a strategy called blended driving accomplish this by distribute the electricenergy, that is charged externally, with fuel over the driving cycle, and also ensurethat the battery’s minimum level reaches before the driving cycle is finished. Astrategy called Charge Depleting Charge Sustaining, CDCS, does not need lengthinformation. This strategy first depletes the battery to a minimum State of Charge,SOC, and after this engages the engine to maintain the SOC at this level. In thisthesis, a variable SOC reference is developed, which is dependent on knowledgeabout the cycle’s length and the current length the vehicle has driven in the cycle.With assistance of a variable SOC reference, is a blended strategy realized. Thisis used to minimize the cost of a driving cycle. A comparison between the blendedstrategy and the CDCS strategy was done, where the CDCS strategy uses a fixedSOC reference. During simulation is the usage of fuel minimized; and the blendedstrategy decreases the cost of the driving missions compared to the CDCS strategy.To solve the energy management problem is a model predictive control used. Thedesigned control system follows the driving cycles, is charge sustaining and solvesthe energy management problem during simulation. The system also handlesmoderate model errors. / Fordonsindustrin måste hantera allt strängare lagkrav mot utsläpp av emissioneroch växthusgaser. Hybridfordon har börjat betraktas som den framtida vägenför att ytterligare minska utsläpp och användning av fossila bränslen. Den ökadekomplexiteten från flera olika motorer kräver mera avancerade styrsystem. Begränsningarfrån motorernas energikällor gör att framtida förhållanden är viktigaatt estimera. För plug-in hybridfordon, PIHEV, är det viktigt att använda denvvijämförelsevis billiga elektriska energin innan fordonet har nått fram till slutdestinationen.Batteriets nuvarande energimängd mäts i dess State of Charge, SOC.Genom att utnyttja information om hur långt det är till slutdestinationen från ettGlobal Positioning System, GPS, blandar styrsystemet den elektriska energin medbränsle från början, detta kallas för blandad körning. En strategi som inte hartillgång till hur långt fordonet ska köras kallas Charge Depleting Charge Sustaining,CDCS. Denna strategi använder först energin från batteriet, för att sedanbörja använda förbränningsmotorn när SOC:s miniminivå har nåtts. Strategin attanvända GPS informationen är jämförd med en strategi som inte har tillgång tillinformation om körcykelns längd. Blandad körning använder en variabel SOC referens,till skillnad från CDCS strategin som använder sig av en konstant referenspå SOC:s miniminivå. Den variabla SOC referensen beror på hur långt fordonethar kört av den totala körsträckan, med hjälp av denna realiseras en blandad körning.Från simuleringarna visade det sig att blandad körning gav minskad kostnadför de simulerade körcyklerna jämfört med en CDCS strategi. En modellbaseradprediktionsreglering används för att lösa energifördelningsproblemet. Styrsystemetföljer körcykler och löser energifördelningsproblemet för de olika drivkällorna undersimuleringarna. Styrsystemet hanterar även måttliga modellfel.

Friction stir welding has become a popular forging technique used in many applications. The Swedish Nuclear Fuel and Waste Management Company (SKB) evaluates this method to seal the 5 cm thick copper canisters that will contain the spent nuclear fuel. To produce repetitive, high quality welds, the process must be controlled, and today a cascade controller is used to keep the desired stir zone temperature. In this thesis, the control system is extended to also include a plunge depth controller. Two different approaches are evaluated; the first attempt is a decentralized solution where the cascaded temperature controller is kept, and the second approach uses a non-linear model predictive controller for both depth and temperature. Suitable models have been derived and used to design the controllers; a simpler model for the decentralized control and a more extensive, full model used in the non-linear model predictive controller that relates all the important process variables. The two controller designs are compared according to important performance measures, and the achieved increase in performance with the more complex non-linear model predictive controller is evaluated. The non-linear model predictive controller has not been implemented on the real process. Hence, simulations of the closed loop systems using the full model have been used to compare and evaluate the control strategies. The decentralized controller has been implemented on the real system. Two welds have been made using plunge depth control with excellent experimental results, confirming that the decentralized controller design proposed in this thesis can be successfully used. Even though the controller manages to regulate the plunge depth with satisfying performance, simulations indicate that the non-linear model predictive controller achieves even better closed loop performance. This controller manages to compensate for the cross-connections between the process variables, and the resulting closed loop system is almost decoupled. Further research will reveal which control design that will finally be used. / ''Friction stir welding'' har blivit en populär svetsmetod inom många olika tillämpningar. På Svensk Kärnbränslehantering AB (SKB) undersöks möjligheten att använda metoden för att försegla de 5 cm tjocka kopparkapslarna som kommer innehålla det använda kärnbränslet. För att kunna producera repeterbara svetsar utav hög kvalité krävs det att processen regleras. Idag löses detta med en temperaturregulator som reglerar svetszonens temperatur. I detta examensarbete utökas styrsystemet med en regulator för svetsdjupet. Två olika lösningar har utvärderats; först en decentraliserad lösning där temperatur-regulatorn behålls och sedan en lösning med en olinjär modellprediktiv reglering (MPC) som reglerar både djup och temperatur. Passande modeller har tagits fram och har använts för att designa regulatorerna; en enklare modell för den decentraliserade regulatorn och en utökad, komplett modell som används i den olinjära MPC:n och som beskriver alla viktiga variabler i processen. Viktiga prestandamått har jämförts för de båda regulatorstrukturerna och även prestandaökningen med den olinjära MPC:n har utvärderats. Då denna regulator inte har implementerats på den verkliga processen har simuleringar av den kompletta modellen använts för att jämföra och utvärdera regulatorstrukturerna. Den decentraliserade regulatorn har implementerats och testats på processen. Två svetsar har gjorts och de har givit utmärkta resultat, vilket visar att regulatorstrukturen som presenteras i rapporten fungerar bra för reglering av svetsdjupet. Trots att den implementerade regulatorn klarar av att reglera svetsdjupet med godkänt resultat, så visar simuleringar att den olinjära MPC:n ger ännu bättre reglerprestanda. Denna regulator kompenserar för korskopplingar i systemet och resulterar i ett slutet system som är nästan helt frikopplat. Ytterligare forskning kommer avgöra vilken av strategierna som kommer att användas i slutprodukten.