University students are asked to become all-round human beings, knowing how to be engaged in Engineering in the future, as well as wholly socialised and going through personal development steps. However, how and where are the students supposed to acquire these skills? Do we already have them in the Higher Education programmes and curricula? This article explores low threshold steps that can be taken to tweak the curriculum and implicit professionalisation of staff towards incorporating transversal skills and reflective activities that allow students to develop to their full potential.. One is a roadmap Workshop identifying guiding principles and touchpoint activities for curricular change. The other is a survey on how transversal skills are currently thought to have been embedded in the curriculum.

In this research a controller is developed that can control path-tracking both within and beyond stable limit handling. The controller is based on the equations of motion of the nonlinear bicycle model. The performance of the controller is evaluated in simulation, a sensitivity analysis is performed and the controller is implemented on a 1/10 scale radio controlled car. The controller is able to track a path in normal driving conditions and let the vehicle enter and maintain a drift while remaining close to the desired path.

By installing a battery storage system in the power grid, Distribution Network Operators (DNOs) can solve congestion problems caused by decentralized renewable generation. This paper provides the necessary theory to use such a community battery for grid congestion reduction, backed up by experimental results. A simple network model was constructed by linearizing the load flow equations using a constant impedance load model. Using this model, an accurate estimate of voltage and overload problems is fed into a receding horizon charge path optimizer. The charge path optimization problem is posed as a linear problem and subsequently solved by an LP solver. The algorithms have been applied and validated on a real-world community battery installation. It was found that the voltages and currents can be controlled to a great degree, increasing the grid capacity significantly. The proposed control framework can be used to safeguard network constraints and is compatible with other battery control goals, such as energy trading or energy independence. Network design formulas are described with which a DNO can quickly estimate the potential (de) stabilization of a community battery on the steady-state voltages and currents in the grid.

In this research a controller is developed that can control path-tracking both within and beyond stable limit handling. A controller is developed, based on the equations of motion of the nonlinear bicycle model. The performance of the controller is evaluated in both simulation and on a 1/10 scale radio controlled car. The controller is able to track a path in typical cornering conditions and let the vehicle enter and maintain a drift while remaining close to the desired path.

In this paper we develop a scenario-based Distributed Model Predictive Control (DMPC) approach for large-scale freeway networks. The uncertainties in a large-scale freeway network are categorized into global uncertainties for the overall network and local uncertainties for subnetworks. A reduced scenario tree is proposed, consisting of global scenarios and a reduced local scenario tree. For handling uncertainties in the scenario-based DMPC problem, a min-max setting is considered. A case study is implemented for investigating the scenario-based DMPC approach, and the results show that in the presence of uncertainties it is effective in improving the control performance with the queue length constraint being satisfied.

To deal with the traffic congestion and emissions, traffic-responsive control approaches can be used. The main aim of the control is then to use the existing capacity of the network efficiently, and to reduce the harmful economical and environmental effects of heavy traffic. In this paper, we design a highly efficient model-predictive control system that uses a gradient-based approach to solve the optimization problem, which has been reformulated as a two-point boundary value problem. A gradient-based approach computes the derivatives to find the optimal value. Therefore, the optimization problem should involve only smooth functions. Hence, for nonsmooth functions that may appear in the internal model of the MPC controller, we propose smoothening approaches. The controller then uses an integrated smooth flow and emission model, where the control objective is to reduce a weighted combination of the total time spent and total emissions of the vehicles. We perform simulations to compare the efficiency and the CPU time of the smooth and nonsmooth optimization approaches. The simulation results show that the smooth approach significantly outperforms the nonsmooth one both in the CPU time and in the optimal objective value.

With an increasing use of distributed energy resources and intelligence in the electricity infrastructure, the possibilities for minimizing costs of household energy consumption increase. Technology is moving toward a situation in which households manage their own energy generation and consumption, possibly in cooperation with each other. As a first step, in this paper a decentralized controller based on model predictive control is proposed. For an individual household using a micro combined heat and power (μCHP) plant in combination with heat and electricity storages the controller determines what the actions are that minimize the operational costs of fulfilling residential electricity and heat requirements subject to operational constraints. Simulation studies illustrate the performance of the proposed control scheme, which is substantially more cost effective compared with a control approach that does not include predictions on the system it controls.