Autonomous vehicles have gained much importance over the last decade owing to their promising capabilities like improvement in overall traffic flow, reduction in pollution and elimination of human errors. However, when it comes to long-distance transportation or working in complex isolated environments like mines, various factors such as safety, fuel efficiency, transportation cost, robustness, and accuracy become very critical. This thesis, developed at the Connected and Autonomous Systems department of Scania AB in association with KTH, focuses on addressing the issues related to fuel efficiency, robustness and accuracy of an autonomous heavy-duty truck used for mining applications. First, in order to improve the state prediction capabilities of the simulation model, a comparative analysis of two dynamic bicycle models was performed. The first model used the empirical PAC2002 Magic Formula (MF) tyre model to generate the tyre forces, and the latter used a piece-wise Linear approximation of the former. On top of that, in order to account for the nonlinearities and time delays in the lateral direction, the steering dynamic equations were empirically derived and cascaded to the vehicle model. The fidelity of these models was tested against real experimental logs, and the best vehicle model was selected by striking a balance between accuracy and computational efficiency. The Dynamic bicycle model with piece-wise Linear approximation of tyre forces proved to tick-all-the-boxes by providing accurate state predictions within the acceptable error range and handling lateral accelerations up to 4 m/s2. Also, this model proved to be six times more computationally efficient than the industry-standard PAC2002 tyre model. Furthermore, in order to ensure smooth and accurate driving, several Model Predictive Control (MPC) formulations were tested on clothoid-based Single Lane Change (SLC), Double Lane Change (DLC) and Truncated Slalom trajectories with added disturbances in the initial position, heading and velocities. A linear time-varying Spatial error MPC is proposed, which provides a link between spatial-domain and time-domain analysis. This proposed controller proved to be a perfect balance between fuel efficiency which was achieved by minimising braking and acceleration sequences and offset-free tracking along with ensuring that the truck reached its destination within the stipulated time irrespective of the added disturbances. Lastly, a comparative analysis between various Prediction-Simulation model pairs was made, and the best pair was selected in terms of its robustness to parameter changes, simplicity, computational efficiency and accuracy. / Under det senaste årtiondet har utveckling av autonoma fordon blivit allt viktigare på grund av de stora möjligheterna till förbättringar av trafikflöden, minskade utsläpp av föroreningar och eliminering av mänskliga fel. När det gäller långdistanstransporter eller komplexa isolerade miljöer så som gruvor blir faktorer som bränsleeffektivitet, transportkostnad, robusthet och noggrannhet mycket viktiga. Detta examensarbete utvecklat vid avdelningen Connected and Autonomous Systems på Scania i samarbete med KTH fokuserar på frågor gällande bränsleeffektivitet, robusthet och exakthet hos en autonom tung lastbil i gruvmiljö. För att förbättra simuleringsmodellens tillståndsprediktioner, genomfördes en jämförande analys av två dynamiska fordonsmodeller. Den första modellen använde den empiriska däckmodellen PAC2002 Magic Formula (MF) för att approximera däckkrafterna, och den andra använde en stegvis linjär approximation av samma däckmodell. För att ta hänsyn till ickelinjäriteter och laterala tidsfördröjningar inkluderades empiriskt identifierade styrdynamiksekvationer i fordonsmodellen. Modellerna verifierades mot verkliga mätdata från fordon. Den bästa fordonsmodellen valdes genom att hitta en balans mellan noggrannhet och beräkningseffektivitet. Den Dynamiska fordonsmodellen med stegvis linjär approximation av däckkrafter visade goda resultat genom att ge noggranna tillståndsprediktioner inom det acceptabla felområdet och hantera sidoacceleration upp till 4 m/s2 . Den här modellen visade sig också vara sex gånger effektivare än PAC2002-däckmodellen. v För att säkerställa mjuk och korrekt körning testades flera MPC varianter på klotoidbaserade trajektorier av filbyte SLC, dubbelt filbyte DLC och slalom. Störningar i position, riktining och hastighet lades till startpositionen. En MPC med straff på rumslig avvikelse föreslås, vilket ger en länk mellan rumsdomän och tidsdomän. Den föreslagna regleringen visade sig vara en perfekt balans mellan bränsleeffektivitet, genom att minimering av broms- och accelerationssekvenser, och felminimering samtidigt som lastbilen nådde sin destination inom den föreskrivna tiden oberoende av de extra störningarna. Slutligen gjordes en jämförande analys mellan olika kombinationer av simulerings- och prediktionsmodell och den bästa kombinationen valdes med avseende på dess robusthet mot parameterändringar, enkelhet, beräkningseffektivitet och noggrannhet.

Docking maneuvers are a relevant part of the modern space mission, requiring precision and safety to ensure the success of the overall mission. This thesis proposes using a non-linear Model Predictive Control (MPC) as a controller with various constraints to ensure safe docking maneuvers for a satellite. This was done in MATLAB using as a model for the satellite the Sliders used by the Robotics Lab at Luleå University of Technology (LTU). The controller was tested first on the MATLAB model and then briefly on hardware.The main objective of this thesis is to develop and implement an MPC-based control strategy to achieve safe docking maneuvers between two satellites. Great attention has been paid to implementing constraints, such as collision avoidance, and hardware constraints, such as thrust limits, to ensure the safety and reliability of the process.Through the MATLAB simulations, it was possible to indicate that the introduced constraints contribute significantly to the safe execution of docking maneuvers, preventing collisions, andoptimizing fuel usage. The controller successfully adapts to unforeseen disturbances and uncertainties in real-time, showcasing its robustness and reliability in dynamic space environments.The hardware simulations have shown that the controller operates as expected but needs further tuning to adapt to the hardware uncertainties.In conclusion, this thesis comprehensively explores MPC-based control strategies with constraints for space docking maneuvers. The positive results underscore this approach’s potential to ensure the safety and reliability of future space missions, opening avenues for further research and application in autonomous space systems.

Temperature control is a widely researched topic and a common application is in heating systems such as buildings. A temperature control method that is central in ensuring comfort and reduction of energy consumption in modern buildings and other heating systems is based on model predictive control (MPC). Traditionally, the MPC optimal control problem is to track a target, but there are other examples of optimization problems besides tracking problems and one such optimization problem is the economical optimization problem, an optimization based on economical objectives. A heating system with electrical supply may be controlled by an economic MPC such that the economical objective is to consider time-varying prices of electricity.  This thesis studies how time-varying prices of electricity can be utilized as an economical objective in an economical MPC to reduce electricity costs for a double-heater system. This is done using an available model of the double-heater system and an MPC to construct an economical MPC. The performance of the economical MPC is then investigated and compared to the existing MPC.  In the thesis it is found, through a test with six different cost profiles and a test with historical data of forecasts of electricity prices, that the economical MPC can reduce total electricity costs when compared to the existing MPC. Furthermore it is found that the performance of the economic MPC is acceptable when it is compared with and without prediction of setpoint changes, prediction of price changes and an isolating layer between the heaters. The thesis concludes that satisfactory results are attained, as the economical MPC leads to decreased total electricity costs for the double-heater system and notes that the economic MPC is versatile by accepting both user-defined and historical cost profiles.

A linear model of an annealing furnace is developed using a black-box system identification approach, and used when testing three different control strategies to improve temperature control. The purpose of the investigation was to see if it was possible to improve the temperature control while at the same time  decrease the switching frequency of the  burners. This will lead to a more efficient process as well as less maintenance, which has both economic and environmental benefits. The estimated model has been used to simulate the furnace with both the existing controller and possible new controllers such as a split range controller and a model predictive controller (MPC). A split range controller is a control strategy which can be used when more than one control signal affect the output signal, and the control signals have different range. The main advantage with MPC is that it can take limitations and constraints into account for the controlled process, and with the use of integer programming, explicitly account for the discrete switching behavior of the burners. In simulation both new controllers succeed in decreasing the switching and the MPC also improved the temperature control. This suggest that the control of the furnace can be improved by implementing one of the evaluated controllers.

This paper presents the mathematical modeling and the design of an optimal pressure tracking controller for an often used setup in pneumatic applications. Two pneumatic chambers are connected with a pneumatic tube. The pressure in the second chamber is to be controlled using two switching valves connected to the first chamber and based on the pressure measurement in the first chamber. The optimal control problem is formulated and solved using the MPC framework. The designed controller shows good tracking quality, while fulfilling hard constraints, like maintaining the pressure below a given upper bound.

Model predictive control (MPC) has gained great acceptance in the industry since it was developed and first applied about 25 years ago [1]. It has established its place mainly in the advanced control community. Traditionally, MPC configurations are developed and commissioned by control experts. MPC implementations have usually been only worthwhile to apply on processes that promise large profit increase in return for the large cost of implementation. Thus the scale of MPC applications in terms of number of inputs and outputs has usually been large. This is the main reason why MPC has not made its way into low-level loop control. In recent years, academia and control system vendors have made efforts to broaden the range of MPC applications. Single loop MPC and multiple PID strategy replacements for processes that are difficult to control with PID controllers have become available and easier to implement. Such processes include deadtime-dominant processes, override strategies, decoupling networks, and more. MPC controllers generally have more "knobs" that can be adjusted to gain optimum performance than PID. To solve this problem, general PID replacement MPC controllers have been suggested. Such controllers include forward modeling controller (FMC)[2], constraint LQ control[3] and adaptive controllers like ADCO[4]. These controllers are meant to combine the benefits of predictive control performance and the convenience of only few (more or less intuitive) tuning parameters. However, up until today, MPC controllers generally have only succeeded in industrial environments where PID control was performing poorly or was too difficult to implement or maintain. Many papers and field reports [5] from control experts show that PID control still performs better for a significant number of processes. This is on top of the fact that PID controllers are cheaper and faster to deploy than MPC controllers. Consequently, MPC controllers have actually replaced only a small fraction of PID controllers. This research shows that deficiencies in the feedback control capabilities of MPC controllers are one reason for the performance gap between PID and MPC. By adopting knowledge from PID and other proven feedback control algorithms, such as statistical process control (SPC) and Fuzzy logic, this research aims to find algorithms that demonstrate better feedback control performance than methods commonly used today in model predictive controllers. Initially, the research focused on single input single output (SISO) processes. It is important to ensure that the new feedback control strategy is implemented in a way that does not degrade the control functionality that makes MPC superior to PID in multiple input multiple output (MIMO) processes. / text

This report proposes three algorithms using model predictive control (MPC) in order to improve the positioning accuracy of an unmanned vehicle. The developed algorithms succeed in reducing the uncertainty in position by allowing the vehicle to deviate from a planned path, and can also handle the presence of occluding objects. To achieve this improvement, a compromise is made between following a predefined trajectory and maintaining good positioning accuracy. Due to the recent development of threats to systems using global navigation satellite systems to localise themselves, there is an increased need for methods of localisation that can function without relying on receiving signals from distant satellites. One example of such a system is a vehicle using a range-bearing sensor in combination with a map to localise itself. However, a system relying only on these measurements to estimate its position during a mission may get lost or gain an unacceptable level of uncertainty in its position estimates. Therefore, this thesis proposes a selection of algorithms that have been developed with the purpose of improving the positioning accuracy of such an autonomous vehicle without changing the available measurement equipment. These algorithms are: A nonlinear MPC solving an optimisation problem. A linear MPC using a linear approximation of the positioning uncertainty to reduce the computational complexity. A nonlinear MPC using a linear approximation (henceforth called the approximate MPC) of an underlying component of the positioning uncertainty in order to reduce computational complexity while still having good performance. The algorithms were evaluated in two different types of simulated scenarios in MATLAB. In these simulations, the nonlinear, linear and approximate MPC algorithms reduced the root mean squared positioning error by 20-25 %, 14-18 %, and 23-27 % respectively, compared to a reference path. It was found that the approximate MPC seems to have the best performance of the three algorithms in the examined scenarios, while the linear MPC may be used in the event that this is too computationally costly. The nonlinear MPC solving the full problem is a reasonable choice only in the case when computing power is not limited, or when the approximation used in the approximate MPC is too inaccurate for the application.

Este trabalho visa o desenvolvimento de uma metodologia para a re-identificação do modelo usado em controladores preditivos (MPC) desenvolvidos em uma estrutura em duas camadas: uma camada estática que calcula os targets para as variáveis manipuladas e uma dinâmica que implementa os targets para as entradas. Espera-se que esse procedimento de reidenticação seja acionado sempre que for observada uma significativa degradação do modelo de controle do processo. Neste trabalho assume-se que a re-identicação do modelo deve ser realizada em malha fechada. No método aqui proposto, admite-se que o código fonte do programa do controlador preditivo não está disponível, e conseqüentemente, o método proposto não deve requerer qualquer modificação no código fonte. No método aqui proposto, o sinal de excitação é introduzido através dos coeficientes da função objetivo da camada estática que calcula os targets para as entradas. O método proposto é testado por simulação em dois processos diferentes. O primeiro processo é uma coluna de destilação para a qual estão disponíveis vários modelos lineares obtidos em diferentes condições operacionais. O segundo processo aqui estudado é um reator químico não linear que deve ser representado localmente por um modelo linear. / This work aims at the development of a methodology to the re-identification of the model to be used in a MPC, which is developed in a two layers structure: a target calculation layer and a dynamic layer where the targets to the inputs are implemented. It is expected that the reidentification procedure should be started whenever it is observed a significant degradation of the process model. Here, it is assumed that the model re-identification is to be performed in closed-loop. In the method proposed here, it is assumed that the source code of the MPC controller is not available, and consequently, the proposed method should not require any modification the source code. In the method proposed here, the excitation signal is introduced through the coefficients of the objective function of the target calculation layer. The proposed method is tested by simulation in two different processes. The first one is a distillation column where several linear models obtained at different operating conditions are available. The second process studied here is a nonlinear chemical reactor that is locally represented by a linear model.

O escopo desta dissertação é o desenvolvimento de um método para detectar os modelos da matriz dinâmica que estejam degradando o desempenho de controladores preditivos baseados em modelos. O método proposto se baseia na análise de correlação cruzada entre o erro nominal do controlador em malha fechada e a uma estimativa da contribuição de cada canal para o cálculo da saída, filtrada pela função de sensibilidade do controlador. Esse método pode ser empregado na auditoria de controladores com variáveis controladas em setpoints e/ou com variáveis que operem entre faixas, como é usual de se encontrar na indústria. Esta dissertação apresenta os resultados da aplicação bem sucedida do método no sistema de quatro tanques (JOHANSSON, 2000), para o qual três cenários foram avaliados. No primeiro cenário, o método localizou corretamente discrepâncias de ganho e de dinâmica de modelos de um controlador preditivo baseado em modelos (Model-based Predictive Controller, ou controlador MPC). No segundo, o método foi utilizado para avaliar a influência de uma variável externa para melhorar o desempenho de um controlador afetado por distúrbios não medidos. No terceiro cenário, o método localizou canais com modelos nulos que deveriam ser incluídos na matriz de controle de um controlador MPC de estrutura descentralizada. Os resultados deste estudo de caso foram comparados com aqueles obtidos pelo método proposto por BADWE, GUDI e PATWARDHAN (2009), constatando-se que o método proposto é mais robusto que o método usado na comparação, não demandando ajustes de parâmetros por parte do usuário para fornecer bons resultados. A dissertação inclui também um estudo de caso da aplicação industrial do método na auditoria de desempenho de um controlador preditivo linear de estrutura descentralizada, com doze variáveis controladas, oito manipuladas e quatro distúrbios não medidos, aplicado a um sistema de fracionamento de propeno e propano em uma indústria petroquímica. A auditoria permitiu reduzir o escopo de revisão do controlador a dezenove canais da matriz, sendo que quatorze destes correspondiam a canais com modelos nulos que deveriam ser incluídos na matriz. A eficácia do método foi comprovada repetindo-se a avaliação da qualidade de modelo para todas as variáveis controladas. / The scope of this dissertation is the development of a method to detect the models of the dynamic matrix that are affecting the performance of model-based predictive controllers. The proposed method is based on the cross correlation analysis between the nominal controller error and an estimate of the contribution of each channel to the controller output, filtered by the controller nominal sensitivity function. The method can be used in the performance assessment of controllers employing variables controlled at the setpoint and/or those controlled within ranges. This dissertation presents the results of the successful application of the method to the quadruple-tank process (JOHANSSON, 2000), for which three scenarios were evaluated. In the first scenario, the method correctly located gain and dynamic mismatches on a model-based predictive controller (MPC controller). In the second one, the method was used to evaluate the influence of an external variable to improve the performance of a controller affected by unmeasured disturbances. In the third scenario, the method located null models that should be included in the dynamic matrix of a decentralized MPC controller. The results of the three scenarios were compared with the ones obtained through the method proposed by BADWE, GUDI e PATWARDHAN (2009). The proposed method was considered more robust than the reference one for not requiring parameters estimation performed by the user to provide good results. This dissertation also includes a case study about the application of the method on the performance assessment of an industrial linear predictive controller of decentralized structure. The controller has twelve controlled variables, eight manipulated variables, and four unmeasured disturbances and is applied to a propylene-propane fractionation system of a petrochemical industry. The performance assessment allowed reducing the scope of the controller revision to nineteen channels of the models matrix, fourteen of which were null models that should be included in the controller. The efficacy of the proposed method was confirmed by repeating the model quality evaluation for all the controlled variables.

Ground robotic vehicles are used in many different applications. Many of these uses include tele-operation of the robot. This allows the robot to be deployed in locations that are too difficult or are unsafe for human access. The ability of a ground robot to autonomously navigate to a desired location without a-priori map information and without using GPS would allow robotic vehicles to be used in many of these situations and would free the operator to focus on other more important tasks. The purpose of this research is to develop algorithms that enable a ground robot to autonomously navigate to a user-selected location. The goal is selected from a video feed from the robot and the robot drives to the goal location while avoiding obstacles. The method uses a monocular camera for measuring the locations of the goal and landmarks. The method is validated in simulation and through experiments on an iRobot Packbot platform. A novel goal-based robocentric mapping algorithm is derived in Chapter 3. This map is created using an extended Kalman filter (EKF) by tracking the position of the goal along with other available landmarks surrounding the robot as it drives towards the goal. The mapping is robocentric, meaning that the map is a local map created in the robot-body frame. A unique state definition of the goal states and additional landmarks is presented that improves the estimate of the goal location. An improved 3D model is derived and used to allow the robot to drive on non-flat terrain while calculating the position of the goal and other landmarks. The observability and consistency of the proposed method are shown in Chapter 4. The visual tracking algorithm is explained in Chapter 5. This tracker is used with the EKF to improve tracking performance and to allow the objects to be tracked even after leaving the camera field of view for significant periods of time. This problem presents a difficult challenge for visual tracking because of the drastic change in size of the goal object as the robot approaches the goal. The tracking method is validated through experiments in real-world scenarios. The method of planning and control is derived in Chapter 6. A Model Predictive Control (MPC) formulation is designed that explicitly handles the sensor constraints of a monocular camera that is rigidly mounted to the vehicle. The MPC uses an observability-based cost function to drive the robot along a path that minimizes the position error of the goal in the robot-body frame. The MPC algorithm also avoids obstacles while driving to the goal. The conditions are explained that guarantee the robot will arrive within some specified distance of the goal. The entire system is implemented on an iRobot Packbot and experiments are conducted and presented in Chapter 7. The methods described in this work are shown to work on actual hardware allowing the robot to arrive at a user-selected goal in real-world scenarios.