Many engineering problems are naturally posed as optimal control problems. It may involve moving between two points in the fastest possible way, or to put a satellite into orbit with minimum energy consumption. Many optimal control problems are too difficult to be solved analytically and therefore require the use of numerical methods. The numerical methods that are the most widespread are the so-called direct methods. However, there is one major drawback with these. If the problem is non-convex, the solution is not guaranteed globally optimal, that is, the absolute best, instead it is guaranteed locally optimal, that is the best in its vicinity. To compensate for this, the problem should be solved several times, under different conditions, in order to investigate whether the solution is a good candidate for the global optimum. CasADi is a software specifically designed for dynamic optimization. It has gained wide spread in recent years because it provides all the necessary building blocks for dynamic optimization. This has given individual engineers and scientists the ability to independently formulate and solve all sorts of optimal control problems. However, this requires good theoretical knowledge of the necessary numerical methods. The advantage of a toolbox, which solves general optimal control problems, is that the underlying numerical methods have been tested and shown to function on optimal control problems with known solutions. This means that the user does not need exhaustive knowledge of the numerical methods involved, but can focus on formulating and solving optimal control problems. The main contribution of this thesis is an optimal control toolbox for MATLAB based on CasADi. The toolbox does not require expert knowledge of the numerical methods, but provides an alternative lower level abstraction that allows for more complex problem formulations. The toolbox implements two direct methods, direct multiple shooting and direct collocation. This allows a problem formulation with many degrees of freedom. The most important property of the toolbox is that the discretization can be changed, without the problem formulation needing to be altered. This way the user can easily change the conditions for his/her problem. The thesis describes how the two implemented direct methods work, and the design choices made. It also describes what remains to test and evaluate, and the problems that have been used as a reference during the development process.

Simulation modeling is important for resource management and operational strategy within the industry. Optimation AB specializes in modeling and simulation of complex systems using Dymola, but also offers solutions for decision support by solving simplified optimal control problems (OCPs). Since simulation models can be exported as functional mock-up units (FMUs), interfacing the underlying equations, this thesis explores the use of FMUs to formulate and solve OCPs in Python, proposing a workflow based on the softwares CasADi, Rockit and IPOPT. Test cases of increasing complexity, including a cogeneration plant OCP, were employed to evaluate the workflow. Promising results were obtained for simplified models, though scaling, initial guesses and solver settings require further consideration. Collocation demonstrated the fastest convergence time and overall robustness. It could be concluded that integrating FMUs into OCPs is feasible, although complex models require modifications. This suggest that creating simplified component libraries in Dymola, tailored for optimization, could improve method implementation and re-usability.

Engine control systems aim to ensure satisfactory output performance whilst adhering to requirements on emissions, drivability and fuel efficiency. Model predictive control (MPC) has shown promising results when applied to multivariable and nonlinear systems with operational constraints, such as diesel engines. This report studies the torque generation from a mean-value heavy duty diesel engine with exhaust gas recirculation and variable-geometry turbocharger using state feedback linearization based MPC (LMPC). This is accomplished by first introducing a fuel optimal reference generator that converts demands on torque and engine speed to references on states and control signals for the MPC controller to follow. Three different MPC controllers are considered: a single linearization point LMPC controller and two different successive LMPC (SLMPC) controllers, where the controllers are implemented using the optimization tool CasADi. The MPC controllers are evaluated with the World Harmonized Transient Cycle and the results show promising torque tracking using a SLMPC controller with linearization about reference values.