The main topic of this thesis is model predictive control (MPC) of an unstable fighter aircraft. When flying it is important to be able to reach, but not exceed the aircraft limitations and to consider the physical boundaries on the control signals. MPC is a method for controlling a system while considering constraints on states and control signals by formulating it as an optimization problem. The drawback with MPC is the computational time needed and because of that, it is primarily developed for systems with a slowly varying dynamics. Two different methods are chosen to speed up the process by making simplifications, approximations and exploiting the structure of the problem. The first method is an explicit method, performing most of the calculations offline. By solving the optimization problem for a number of data sets and thereafter training a neural network, it can be treated as a simpler function solved online. The second method is called fast MPC, in this case the entire optimization is done online. It uses Cholesky decomposition, backward-forward substitution and warm start to decrease the complexity and calculation time of the program. Both methods perform reference tracking by solving an underdetermined system by minimizing the weighted norm of the control signals. Integral control is also implemented by using a Kalman filter to observe constant disturbances. An implementation was made in MATLAB for a discrete time linear model and in ARES, a simulation tool used at Saab Aeronautics, with a more accurate nonlinear model. The result is a neural network function computed in tenth of a millisecond, a time independent of the size of the prediction horizon. The size of the fast MPC problem is however directly affected by the horizon and the computational time will never be as small, but it can be reduced to a couple of milliseconds at the cost of optimality.

Real-time optimization (RTO) is a supervisory strategy in the hierarchical industrial process automation architecture in which economically optimal set-point targets are computed for the lower level advanced control system, which is typically model predictive control (MPC). Due to highly volatile market conditions, recent developments have considered transforming the conventional steady-state RTO to dynamic RTO (DRTO) to permit economic optimization during transient operation. Published DRTO literature optimizes plant input trajectories without taking into account the presence of the plant control system, constituting an open-loop DRTO (OL-DRTO) approach. The goal of this research is to develop a design framework for a DRTO system that optimizes process economics based on a closed-loop response prediction. We focus, in particular, on DRTO applied to a continuous process operation regulated under constrained MPC. We follow a two-layer DRTO/MPC configuration due to its close tie to the industrial process automation architecture. We first analyze the effects of optimizing MPC closed-loop response dynamics at the DRTO level. A rigorous DRTO problem structure proposed in this thesis is in the form of a multilevel dynamic optimization problem, as it embeds a sequence of MPC optimization subproblems to be solved in order to generate the closed-loop prediction in the DRTO formulation, denoted here as a closed-loop DRTO (CL-DRTO) strategy. A simultaneous solution approach is applied in which the convex MPC optimization subproblems are replaced by their necessary and sufficient, Karush-Kuhn-Tucker (KKT) optimality conditions, resulting in the reformulation of the original multilevel problem as a single-level mathematical program with complementarity constraints (MPCC) with the complementarities handled using an exact penalty formulation. Performance analysis is carried out, and process conditions under which the CL-DRTO strategy significantly outperforms the traditional open-loop counterpart are identified. The multilevel DRTO problem with a rigorous inclusion of the future MPC calculations significantly increases the size and solution time of the economic optimization problem. Next, we identify and analyze multiple closed-loop approximation techniques, namely, a hybrid formulation, a bilevel programming formulation, and an input clipping formulation applied to an unconstrained MPC algorithm. Performance analysis based on a linear dynamic system shows that the proposed approximation techniques are able to substantially reduce the size and solution time of the rigorous CL-DRTO problem, while largely retaining its original performance. Application to an industrially-based case study of a polystyrene production described by a nonlinear differential-algebraic equation (DAE) system is also presented. Often large-scale industrial systems comprise multi-unit subsystems regulated under multiple local controllers that require systematic coordination between them. Utilization of closed-loop prediction in the CL-DRTO formulation is extended for application as a higher-level, centralized supervisory control strategy for coordination of a distributed MPC system. The advantage of the CL-DRTO coordination formulation is that it naturally considers interaction between the underlying MPC subsystems due to the embedded controller optimization subproblems while optimizing the overall process dynamics. In this case, we take advantage of the bilevel formulation to perform closed-loop prediction in two DRTO coordination schemes, with variations in the coordinator objective function based on dynamic economics and target tracking. Case study simulations demonstrate excellent performance in which the proposed coordination schemes minimize the impact of disturbance propagation originating from the upstream subsystem dynamics, and also reduce the magnitude of constraint violation through appropriate adjustment of the controller set-point trajectories. / Thesis / Doctor of Philosophy (PhD)

In the last decades, the quadrotor has been used in many areas, and deigning an effective flight control algorithm for the quadrotor has attracted great interests in both control and robotics communities. This thesis focuses on the flight control of the quadrotor by using different methods: The extend Kalman filter (EKF) based linear quadratic regulator (LQR) method and learning-based model predictive control (LBMPC) method. Chapter 4 investigates the flight control of a quadrotor subject to the model uncertainties and external disturbances. We propose a LQR based tracking algorithm. However, the designed LQR controller is hard to be implemented because of the existing noises in the measured states. A modified EKF is then designed for the online estimation of the position, velocity and motor dynamics by using the measured outputs. From the experimental testing results, it is shown that the proposed EKF-based LQR control method solves the tracking problem of the quadrotor with less tracking errors than only using the LQR method. In Chapter 5, the tracking control problem of the quadrotor subject to external disturbances and physical constraints is studied. A model predictive control (MPC) based algorithm is proposed. To reduce the computational load, a modified prior barrier interior-point method is used to solve the quadratic programming (QP) problem. Nevertheless, the achievable flight performance by using the standard MPC algorithm is affected by external disturbances. A LBMPC algorithm is proposed for the disturbance rejection. From the simulation results, it is shown that using the proposed LBMPC algorithm have less tracking errors than applying the standard MPC algorithm. / Graduate

To meet the increasing demand on control precision in industrial forklifts, physical modelling of the lifting system has been combined with parameter estimations from data. A number of different controllers have been evaluated in terms of their ability to achieve a load independent lifting speed. The model and controller performance as well as stability properties were evaluated in simulations, and the most promising controller was implemented on the real system. Especially the electric motor turned out to be difficult to model, and therefore experimental data was used to approximate some parts of it. This, along with some friction parameters that had to be estimated caused a slight loss in model generality. An observer (Extended Kalman filter) was used to estimate the unknown states, including the velocity of the forks. The simulated performance of the MPC controller was slightly better than the PID controller, except for a bigger overshoot when starting from a turned off motor. The PID controller also handles model errors better, because of its integral action. Due to the simplicity in relation to performance, only the PID controller was implemented on the forklift. The model turned out to perform well, but not well enough to estimate the lifting height accurately. The PID controller worked as intended and it could therefore be concluded that a more advanced control algorithm, such as an MPC controller, is not necessary for this system.

<p>Traditional oil analysis methods - e.g. acidity and viscosity measurements - have been used to monitor lubricant conditions. These methods can detect when the useful life of a lubricant is over but fall short when trying to gain insight on how long a lubricant in current use could last. This makes it difficult to make proactive decisions and estimate oil drain periods. Lubricants do not start to degrade until the antioxidants, which prevent from oxidation, have depleted to a certain level where they no longer can protect the base oil from degradation. During the degradation process insoluble contaminants form that can lead to sludge and varnish.</p><p>Four engine oils were oxidized using oxygen pressurized vessels and four hydraulic oils were oxidized with turbine oil stability test (TOST). At different stages of oxidation, sample aliquots were withdrawn and analysed. A blend of engine oil and biodiesel was also tested as well as a mixture of hydraulic oil and water. Samples of engine oils were also tested from a rig test running at SCANIA’s facilities in Södertälje, Sweden. The samples were evaluated with Remaining Useful Life Evaluation Routine (RULER) and Membrane Patch Colorimetry (MPC). RULER is a voltammetric method that measures the antioxidant level in a lubricant sample and MPC measure the insoluble contaminants by spectrophotometric analysis. Results from these analyses were compared to conventional methods such as acid number, viscosity, and Fourier Transform Infrared spectroscopy (FTIR).</p><p>Results from the MPC-analyses showed that this method is dependent on the type of the lubricant tested. RULER performed well for all tested lubricants. It was shown that this analyse method can predict when the lubricant is going to start to degrade due to oxidation. Tests showed that the oxidation of the lubricant starts when there are 20-25% of the antioxidants remaining.</p>

Traditional oil analysis methods - e.g. acidity and viscosity measurements - have been used to monitor lubricant conditions. These methods can detect when the useful life of a lubricant is over but fall short when trying to gain insight on how long a lubricant in current use could last. This makes it difficult to make proactive decisions and estimate oil drain periods. Lubricants do not start to degrade until the antioxidants, which prevent from oxidation, have depleted to a certain level where they no longer can protect the base oil from degradation. During the degradation process insoluble contaminants form that can lead to sludge and varnish. Four engine oils were oxidized using oxygen pressurized vessels and four hydraulic oils were oxidized with turbine oil stability test (TOST). At different stages of oxidation, sample aliquots were withdrawn and analysed. A blend of engine oil and biodiesel was also tested as well as a mixture of hydraulic oil and water. Samples of engine oils were also tested from a rig test running at SCANIA’s facilities in Södertälje, Sweden. The samples were evaluated with Remaining Useful Life Evaluation Routine (RULER) and Membrane Patch Colorimetry (MPC). RULER is a voltammetric method that measures the antioxidant level in a lubricant sample and MPC measure the insoluble contaminants by spectrophotometric analysis. Results from these analyses were compared to conventional methods such as acid number, viscosity, and Fourier Transform Infrared spectroscopy (FTIR). Results from the MPC-analyses showed that this method is dependent on the type of the lubricant tested. RULER performed well for all tested lubricants. It was shown that this analyse method can predict when the lubricant is going to start to degrade due to oxidation. Tests showed that the oxidation of the lubricant starts when there are 20-25% of the antioxidants remaining.

The control of nonholonomic wheeled mobile robots (WMRs) has gained a lot of attention in the field of robotics over the past few decades as WMRs provide an increased range of motion resulting in a larger workspace. This research focuses on the application of Model Predictive Control (MPC) for real-time trajectory tracking of a nonholonomic WMR. MPC is a control strategy in which the control law is designed based on optimizing a cost function. The input and output constraints that may arise in practical situations can be directly incorporated into the control system using MPC. Computation time is the biggest hurdle in adapting MPC strategies for trajectory tracking. This research applies a non-feasible active set MPC algorithm developed in [1] which is faster than the traditional active set methods (ASMs). A discrete-time linear model of a general WMR is used for the simulation. MATLAB simulations are performed for tracking circular as well as square trajectories using the discretized WMR model and the non-feasible ASM (NF-ASM). The performance of NF-ASM is compared to two other well-known traditional algorithms, i.e. Fletcher’s ASM and MATLAB’s Quadratic Programming algorithm. It is shown that, although all these algorithms are capable of providing satisfactory trajectory tracking performance, NF-ASM is a better choice in terms of the simulation time and required number of iterations for realtime trajectory tracking of any type as long as the constraints on the inputs stay active for a long period during the simulation. / UOIT

Remotely Operated underwater Vehicles (ROVs) are getting more and more advanced withevery new model. As new functionality is added, the price increases. This thesis is one partof a larger project, where the goal is to develop a low-budget ROV. The ROV should later bemade autonomous and entered into a competition.This thesis have focused on the modeling and stabilizing control of an ROV that was designedby mechanical engineering students at Linköping University. The only sensor used was anInertial Measurement Unit (IMU) and the ROV has a torpedo-like design. The modeling wasdone using identification in Matlab with the grey box and black box methods. Water trialsand simulations show that the model is estimated sufficiently good to be used as the basis ofdifferent model based controllers.Two different control strategies were implemented; a linear quadratic controller (LQ) and amodel predictive controller (MPC). Both controllers worked desirably in simulations. Onlythe LQ controller was evaluated in real world tests. Due to problems with the implementationenvironment chosen for the MPC, the MPC could not be tested.The thesis also uses decision matrices as a mean to motivate the important decisions thathave been made.

This thesis is a study of modelling and control of a multirotor unmanned aerial vehicle(UAV). On behalf of Intuitive Aerial, a model of their hexarotor aircraft has been developedas a tool in further development and testing of their product. The potential of using ModelPredictive Control (MPC) as control method for multirotor UAV:s has also been evaluated.The model was successfully implemented in MATLAB/Simulink, as was the Model Predic-tive Controller. Quaternion angle representation has been used to avoid singularities in themodel and non-linear dynamics have been included in the simulation model. Unknownmodel parameters have been estimated with data acquired from testing. Model validity hasalso been evaluated with flight data gathered from flights using a test vehicle. A basic MPCformulation has been expanded to include reference tracking, integral action and to ensurestability.Assessment proved the model to be feasible but in need of more rigorous evaluation to guar-antee good performance. The MPC controller showed promising performance compared toa linear feedback controller.

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