Functional Electrical Stimulation (FES) is a technique that applies electrical currents to nervous tissue in order to actively induce muscle contraction. Recent research has shown that FES provides a promising treatment to restore functional tasks due to paralysis caused by spinal cord injury, head injury, and stroke, to mention a few. Therefore, the overarching goal of this research work is to develop FES controllers to enable patients with movement-disorder to control their limbs in a desired manner and, in particular, to aid Parkinson's patients to suppress hand tremor. In our effort to develop strategies for muscle stimulation control, we first implement a model-based control technique assuming that all the states are measurable. The Hill-type muscle model coupled with a simplified 2DoF model of the arm is used to study the performance of our proposed adaptive sliding mode controller for simulation purpose. However, in the more practical situations, human limb dynamics are extremely complicate and it is inadequate to use model based controllers, especially considering there are still technical limitations that allow in vivo measurements of muscle activity. To tackle these challenges, we have developed output feedback adaptive control approaches for a class of unknown multi-input multi-output systems. Such control strategies are first developed for linear systems, and then extended to the nonlinear case. The proposed controllers, supported by experimental results, require minimum knowledge of the system dynamics and avoid many restrictive assumptions typically found in the literature. Therefore, we expect that the results introduced in this dissertation can provide a solution for a wide class of nonlinear uncertain systems, with focus on practical issues such as partial state measurement and the presence of mismatched uncertainties. / Ph. D.

In this master’s thesis, a turbine model and a common control structure for theturbocharger for SI and CI-engines is developed. To design the control structure,simulations are done on an existing diesel engine model with VGT. In order tobe able to make simulations for engines with a wastegated turbine, the model isextended to include mass flow and turbine efficiency for that configuration. Thedeveloped model has a mean absolute relative error of 3.6 % for the turbine massflow and 7.4 % for the turbine efficiency. The aim was to control the intake manifoldpressure with good transients and to use the same control structure for VGTand wastegate. By using a common structure, development and calibration timecan be reduced. The non-linearities have been reduced by using an inverted turbinemodel in the control structure, which consists of a PI-controller with feedforward.The controller can be tuned to give a fast response for CI engines and a slowerresponse but with less overshoot for SI engines, which is preferable.

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

the 18th World Congress The International Federation of Automatic Control, Milano (Italy), August 28 - September 2, 2011

The implementation of automated systems in manufacturing industries increases efficiency, precision, and safety by reducing human intervention, errors, and waste. Variable loads can cause several problems for automation systems. One of the most significant challenges is maintaining the stability and precision of the production process despite changing load conditions. These variable loads can lead to unstable systems or failures, causing an increase in errors, reduced efficiency, and lower product quality. It is essential to design control systems that can adapt to changing load conditions and maintain stable and precise operation under all circumstances. To address this problem, this thesis presents an adaptive controller based on load identification and gain scheduling, to replace the standard FOC consisting of regular PI-controllers. The load estimator is used to estimate the external load with relatively small RMSD values, while the ain scheduler adjusts the controller gains based on the estimated load. Other controllers are also explored, such as an angular velocity error-based adaptive controller. The results shows that both proposed controllers perform better than the standard controller when the system is subject to variable external loads, however, the load estimator paired with the gain scheduled PI-controller performs best. / Automatiseringen, inom framförallt tillverkningsindustrin, ökar effektivitet, precision och säkerheten genom att minska den mänskliga faktorn, fel och kassationer. System som utsätts för variabel belastning kan orsaka flera olika problem för automationssystem. En av de största utmaningarna är att bibehålla stabilitet och precision i produktionsprocessen trots förändrade belastningsförhållanden. Dessa variabla belastningar kan leda till instabila system eller fel, vilket ökar felmängden, minskar effektiviteten och sänker produktkvaliteten. Därför är det viktigt att utforma styrsystem som kan anpassa sig till förändrade lastförhållanden och samtidigt upprätthålla stabil och precis drift under oavsett förutsättningar. För att lösa detta problem presenterar denna avhandling en adaptiv regulator baserad på lastidentifiering och gain-scheduling, för att ersätta den vanliga FOC som består av klassiska PI-regulatorer. Lastestimatorn används för att uppskatta den externa lasten med ett relativt litet RMSD, medan gain-scheduling justerar regulatorns förstärkning baserat på den uppskattade belastningen. Andra regulatorer utforskas också, såsom en adaptiv regulator baserad på fel i vinkelhastighet. Resultaten visar att båda föreslagna regulatorer presterar bättre än standardregulatorn när systemet utsätts för variabla externa belastningar, men att lastestimatorn tillsammans med gainscheduled PI-regulatorer presterar bäst.

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Zero energy houses and (near) zero energy buildings are among the most ambitious targets of society moving towards an energy efficient built environment. The "zero" energy consumption is most often judged on a yearly basis and should thus be interpreted as yearly net zero energy. The fully self sustainable, i.e. off-grid, home poses a major challenge due to the dynamic nature of building load profiles, ambient weather condition and occupant needs. In current practice, the off-grid status is accomplishable only by relying on backup generators or utilizing a large energy storage system. The research develops a risk based holistic system design method to guarantee a match between onsite sustainable energy generation and energy demand of systems and occupants. Energy self-sufficiency is the essential constraint that drives the design process. It starts with information collection of occupants' need in terms of life style, risk perception, and budget planning. These inputs are stated as probabilistic risk constraints that are applied during design evolution. Risk expressions are developed based on the relationships between power unavailability criteria and "damages" as perceived by occupants. A power reliability assessment algorithm is developed to aggregate the system underperformance causes and estimate all possible power availability outcomes of an off-grid house design. Based on these foundations, the design problem of an off-grid house is formulated as a stochastic programming problem with probabilistic constraints. The results show that inherent risks in weather patterns dominate the risk level of off-grid houses if current power unavailability criteria are used. It is concluded that a realistic and economic design of an off-grid house can only be achieved after an appropriate design weather file is developed for risk conscious design methods. The second stage of the research deals with the potential risk mitigation when an intelligent energy management system is installed. A stochastic model based predictive controller is implemented to manage energy allocation to sub individual functions in the off-grid house during operation. The controller determines in real time the priority of energy consuming activities and functions. The re-evaluation of the risk indices show that the proposed controller helps occupants to reduce damages related to power unavailability, and increase thermal comfort performance of the house. The research provides a risk oriented view on the energy self-sufficiency of off-grid solar houses. Uncertainty analysis is used to verify the match between onsite sustainable energy supply and demand under dynamic ambient conditions in a manner that reveals the risks induced by the fact that new technologies may not perform as well as expected. Furthermore, taking occupants' needs based on their risk perception as constraints in design evolution provides better guarantees for right sized system design.

<div>Efficient engine operation is a fundamental control problem in automotive applications. Robust control algorithms are necessary to achieve satisfactory, safe engine performance</div><div>at all operating conditions while reducing emissions. This thesis develops a framework for control architecture design to enable robust air handling system management.</div><div><br></div><div>The first work in the thesis derives a simple physics-based, control-oriented model for turbocharged lean burn engines which is able to capture the critical engine dynamics that are</div><div>needed to design the controller. The control-oriented model is amenable for control algorithm development and includes the impacts of modulation to any combination of four actuators: throttle valve, bypass valve, fuel rate, and wastegate valve. The controlled outputs: engine speed, differential pressure across throttle and air-to-fuel ratio are modeled as functions of selected states and inputs. Two validation strategies, open-loop and closed-loop are used to validate the accuracy of both nonlinear and linear versions of the control-oriented model. The relative gain array is applied to the linearized engine model to understand the degree of interactions between plant inputs and outputs as well as the best input-output pairing as a function of frequency. With strong evidence of high degree of coupling between inputs and outputs, a coordinated multiple-input multiple-output (MIMO) controller is hypothesized to perform better than a single-input single-output (SISO) controller. A framework to design robust model-based H1 MIMO controllers for any given linear plant, while considering state and output multiplicative uncertainties as well as actuator bandwidths is developed. The framework also computes the singular structure value, μ for the uncertain closed-loop system to quantify robustness, both in terms of stability and performance. The multi-tracking control problem targets engine speed, differential pressure across throttle as well as air-to-fuel ratio to achieve satisfactory engine performance while also preventing compressor surge and reducing engine emissions. A controller switching methodology using slow-fast controller decomposition and hysteresis at switching points is proposed to smoothly switch control authority between several MIMO controllers. The control design approach is applied to a truth-reference GT-Power engine model to evaluate the closed-loop controller performance. The engine response obtained using the robust MIMO controller is compared with that obtained using a state-of-the-art benchmark controller to evaluate the additional benefits of the MIMO controller.</div><div><br></div><div><div>In the second study, a robust 2-degree of freedom controller that commands eBooster speed to control air-to-fuel ratio, and a robust MIMO coordinated controller to control gas</div><div>exchange process in a diesel engine with electrified air handling architecture are developed. The MIMO controller simultaneously controls engine speed, mass fraction of the recirculated exhaust gas as well as air-to-fuel ratio. The actuators available for control in the engine include the exhaust gas recirculation valve, exhaust throttle valve, fuel injection rate, eBooster speed, eBooster bypass valve. To design the robust eBooster controller, the input-output relationship between eBooster speed and air-to-fuel ratio is estimated using system identification techniques. The robust MIMO controller is synthesized using a physics-based mean value control-oriented engine model that accurately represents the high-fidelity GT-Power model. In the first control strategy, the robust eBooster controller is added to an already existing stock engine control unit while in the second control strategy, the stock engine control unit is replaced with the multiple-input multiple-output controller. The two control architectures are tested under different operating conditions to evaluate the controller performance. Simulation results with the control architectures developed in the thesis are compared to a baseline engine configuration, where the engine operates without eBooster. Although it is observed that both these control algorithms significantly improve engine performance as compared to the baseline configuration, MIMO controller provides the best engine performance overall.</div></div>