In this work, we present a building energy management system based on a specifically designed mixed-integer nonlinear model predictive control (MINMPC) strategy. Applied to a multifamily residential building, we first assess the potential of the developed solution in a detailed simulation study and later address its real-world deployment on a programmable logic controller (PLC). We show that our strategy successfully follows the complex system constraints, such as the minimum admissible operating time of the system components, without compromising the residents’ comfort. The performance of the approach is evaluated in direct comparison with an enhanced rule-based conventional control strategy. We accomplish an overall 24.5% reduction in total energy costs and stable real-time operation of the hardware controller.

We present a methodology based on mixed-integer nonlinear model predictive control for a real-time building energy management system in application to a single-family house with a combined heat and power (CHP) unit. The developed strategy successfully deals with the switching behavior of the system components as well as minimum admissible operating time constraints by use of a special switch-cost-aware rounding procedure. The quality of the presented solution is evaluated in comparison to the globally optimal dynamic programming method and conventional rule-based control strategy. Based on a real-world scenario, we show that our approach is more than real-time capable while maintaining high correspondence with the globally optimal solution. We achieve an average optimality gap of 2.5% compared to 20% for a conventional control approach, and are faster and more scalable than a dynamic programming approach.

For elastic drive systems the motor shaft position does not correspond to the load position. Therefore, load side position feedback is usually used in the control to achieve correct positioning of the system. This paper proposes an alternative approach, that allows for a correct positioning using the motor position feedback for control. This is achieved through the online correction of the reference position using a robust Extended Kalman Filter. Using the load feedback it estimates an idealised, offset-free motor position. The correction factor calculated from the difference between the measured and the estimated motor position is then used for reference position correction (RPC). The proposed RPC method is implemented on a belt-driven test bed and evaluated for different movements, load oscillations and significant parameter errors to show its functionality and its suitability for industrial application.

In this paper a new rotor position observer for permanent magnet synchronous machines (PMSM) based on an Extended-Kalman-Filter (EKF) is presented. With this method, just one single EKF is sufficent to evaluate the position information from electromotive force (EMF) and anisotropy. Thus, the PMSM can be controlled for the entire speed range without a position sensor and without the need to switch or synchronize between different observers. The approach covers online estimation of permanent magnetic field and mechanical load. The resulting EKF-based rotor position estimator is embedded in the existing cascaded control concept of the PMSM without need of additional angle trackers or signal filters. The experimental validation for the position sensorless control shows optimized dynamic behaviour.

In recent years, building energy supply and distribution systems have become more complex, with an increasing number of energy generators, stores, flows, and possible combinations of operating modes. This poses challenges for supervisory control, especially when balancing the conflicting goals of maximizing comfort while minimizing costs and emissions to contribute to global climate protection objectives. Mixed‐integer nonlinear model predictive control is a promising approach for intelligent real‐time control that is able to properly address the specific characteristics and restrictions of building energy systems. We present a strategy that utilizes a decomposition approach, combining partial outer convexification with the Switch‐Cost Aware Rounding procedure to handle switching behavior and operating time constraints of building components in real‐time. The efficacy is demonstrated through practical applications in a single‐family home with a combined heat and power unit and in a multi‐family apartment complex with 18 residential units. Simulation studies show high correspondence to globally optimal solutions with significant cost savings potential of around 19%.

We present a novel long short-term memory (LSTM) approach for time-series prediction of the sand demand which arises from preparing the sand moulds for the iron casting process of a foundry. With our approach, we contribute to qualify LSTM and its combination with feedback-corrected optimal scheduling for industrial processes. The sand is produced in an energy intensive mixing process which is controlled by optimal scheduling. The optimal scheduling is solved for a fixed prediction horizon. One major influencing factor is the sand demand, which is highly disturbed, for example due to production interruptions. The causes of production interruptions are in general physically unknown. We assume that information about the future behavior of the sand demand is included in current and past process data. Therefore, we choose LSTM networks for predicting the time-series of the sand demand. The sand demand prediction is performed by our multi model approach. This approach outperforms the currently used naive estimation, even when predicting far into the future. Our LSTM based prediction approach can forecast the sand demand with a conformity up to 38 % and a mean value accuracy of approximately 99 %. Simulating the optimal scheduling with sand demand prediction leads to an improvement in energy savings of approximately 1.1 % compared to the naive estimation. The application of our novel approach at the real production plant of a foundry proves the simulation results and verifies the capability of our approach.

Abstract This paper proposes an extended Petri net formalism as a suitable language for composing optimal scheduling problems of industrial production processes with real and binary decision variables. The proposed approach is modular and scalable, as the overall process dynamics and constraints can be collected by parsing of all atomic elements of the net graph. To conclude, we demonstrate the use of this framework for modeling the moulding sand preparation process of a real foundry plant.

Real-time, high-rate Extended Kalman Filter (EKF) execution must include considerations of its computational load, which can pose a challenge for the implementation depending on the specific observer rate. While methods for the reduction of the computational load exist, this paper seeks to circumvent the problem, by reducing the EKF sampling rate. This is explored for the case of multi-rate sensor fusion for drive control applications where at least one sensor sampling rate exceeds the control cycle rate. A lower rate EKF is implemented where, in contrast to a single-rate EKF approach, none of the higher rate measurements are neglected, but instead collected and sequentially processed during each EKF execution. Two formulations based on this concept are introduced. The first optimises the estimation error, accepting a significant increase in computational load, while the second seeks the best compromise between estimation error and computational load.

This paper presents a holistic approach to realise model-based control concepts for complex drive systems. Such drive systems, e.g. with multiple elasticities and error-prone sensors, usually require the combination of different sophisticated control engineering methods to achieve excellent performance. However, such a combination often leads to high solution complexity with extensive computational and development efforts. Therefore, this paper presents a holistic approach based on two guidelines that enable the formulation of fully model-based solutions with minimised complexity. This is achieved by using only one common basic model and by having the individual control approaches synergistically complement and build on each other. The capacity of the approach is experimentally proven using the example of an elastic drive system with erroneous sensor data that combines the challenges of exact positioning and oscillation reduction.

This paper presents a novel approach for modeling the energy consumption of the coupled parallel moulding sand mixers of a foundry as an optimal control problem. The minimization of energy consumption is optimized by scheduling the mixing processes in a linear integer programming scheme. The sand flow through the foundry's sand preparation is characterized by a physical model. This model considers the sand demand of the moulding machine as disturbance, the stored sand masses in the mixer hoppers and machine hoppers, respectively. The novel approach of handling dwell-times for dosing, mixing and transport processes using dead-time systems and constraint pushing allows the application of a linear model. The formulation of the optimal control problem aims at real-time application as model predictive control at the production plant. Initial application results indicate an improvement in energy consumption of approximately 8 %.

The increasing variety of combinations of different building technology components offers a high potential for energy and cost savings in today's buildings. However, in most cases, this potential is not yet fully exploited due to the lack of intelligent supervisory control systems that are required to manage the complexity of the resulting overall systems. In this article, we present the implementation of a mixed-integer nonlinear model predictive control approach as a smart realtime building energy management system. The presented methodology is based on a forward-looking optimization of the overall energy costs. It takes into account energy demand forecasts and varying electricity market prices. We achieve real-time capability of the controller by applying a decomposition approach, which approximates the optimal solution of the underlying mixed-integer optimal control problem by convexification and rounding of the relaxed solution. The quality of the suboptimal solution is evaluated by comparison with the globally optimal solution obtained by the dynamic programming method. Based on a real-world scenario, we demonstrate that utilization of the real-time capable mixedinteger nonlinear model predictive control approach in a building control system leads to savings of 16% in the total operating costs and 13% in primary energy compared to the state-of-the-art control strategy without any loss of comfort for the residents.

We present a feedback-corrected optimal scheduling approach to reduce the demand of electrical energy of batch processes, exemplified at the sand preparation in foundry. The main energy driver in the exemplary foundry is the idle time of the batch-wise working sand mixers. In this novel approach, we use linear integer programming to minimize the demand of energy of the sand mixers by scheduling the batches in real-time. For the optimization we use a physical model of the sand preparation, which takes dwell-times of the processes as dead-time systems into account. In this paper, we present the steps to make the optimal scheduling approach applicable for the production process. The application at the real production plant proves the performance of the suggested approach. Compared to the conventional control, the feedback-corrected optimal scheduling approach leads to an reduction in energy consumption of approximately 6.5 % without modifying the process or the aggregates.

In this paper, a new extended Kalman filter (EK) based rotor position observer for permanent magnet synchronous machines (PMSM) is presented. With this method, just one single EKF is sufficent to evaluate the position information from EMF and anisotropy. Thus, PMSM can be controlled in the entire speed range without a position sensor and without the need to switch or synchronize between different observers. Furthermore, the phase delay in the feedback path of the control loop is reduced. With the increased accuracy of the estimated rotor position and speed, the resulting control behavior is optimized. This makes a smooth pick-up of freewheeling PMSM possibly, even if starting conditions are unknown. Demonstrated by measurements, the position sensorless control behavior shows an optimized dynamic.

This paper presents a databased approach for improving the precision of the moulding sand compressibility in the moulding sand mixer of a foundry. In this approach, the deviation between the measured and the target compressibility is reduced by controlling the water addition. The complex dynamic behaviour of the process variables and their influence on the water addition is modelled with a long short-term memory (LSTM) network. Another LSTM network as control path simulates the impact of the water addition on the compressibility. Simulation and experimental results with the applied model for water prediction in a feedforward control yield relevant improvements of the moulding sand compressibility.

Flatness-based feedforward control is an approach for combining fast motion with low oscillations for nonlinear or flexible drive systems. Its desired trajectories must be continuously differentiable to the degree of the system order. Designing such trajectories, that also reach the dynamic system limits, poses a challenge. Common solutions, like Gevrey functions, usually require lengthy offline calculations. To achieve a quicker and simpler industrial-suited solution, this paper presents a new online trajectory generation scheme. The algorithm utilizes higher order s-curve trajectories created by a cyclic filtering process using moving average filters. An experimental validation proves the capability as well as industrial applicability of the presented approach for flexible structures like stacker cranes.

This paper presents a new systematic approach for the design of a gain-scheduling-control system for the oil pressure system of internal combustion engines. Therefore, optimal linear time invariant controllers are designed at given operating points and their characteristics are interpolated by an analytical function. An optimization algorithm finds optimal parameters for a given linear controller structure with a fixed order and two degrees of freedom. To solve the optimization problem, constraints in the $$\mathcal{H}_{\infty}$$-norm are given for six different transfer functions of the closed loop. The same restrictions are applied for all operating points. For the gain-scheduling-approach, no classical superimposition of the output variables of the linear controllers is selected, as it is often the case with gain-scheduling-controllers. Instead, the poles and zeros of the gain-scheduling-controller are functionally calculated from the linear control loops depending on the scheduling variable. Therefore, the resulting gain-scheduling-controller shows the set behaviour of the linear controllers independently of the original operating points. A control loop that is independent of the nonlinearity of the controlled system is more efficient than conventional oil pressure control systems that use too high target oil pressures for robustness reasons. This results in increased efficiency of the oil pressure system, which can make a significant contribution to fuel savings.

In this paper, the parametrization of the fundamental wave model and the model for high frequency voltage excitation (HF-Modell) of a permanent magnet excited synchronous machine (PMSM) are presented. Saturation-dependent machine parameters are identified and approximated. This includes a selection of useful mathematical functions as basis, and an optimization algorithm for the approximation. Moreover the results of identification and approximation are made plausible in terms of physical aspects. Finally, the estimated state variables are shown and evaluated by means of measurements.

The rapidly growing maritime industry of Southwestern Finland is suffering from lack of engineers. Industrial Management Engineers including Sales Engineers have technical knowledge and commercial competencies as well as management and soft skills, which are all fundamental for the maritime sector. However, it takes rather long time before a graduated engineer will be a productive worker for the company. A solution to getting the students faster operational into the companies is to deepen cooperation between higher education and industry during the studies and increase workplace based learning following the examples available in e.g. Germany and France. The authors present a new approach of academic-industrial cooperation in education in Finland which is developed in the frame of the project ‘RADICAL - Filling Skills Gaps in Blue Industry by Radical Competence Boost in Engineering VET (VET: Vocational Education and Training)’, co-funded by the Erasmus+ program of the European Union.

This paper presents a cascaded methodology for enhancing the path accuracy of industrial robots by using advanced control schemes. It includes kinematic calibration as well as dynamic modeling and identification. This is followed by a centralized model-based compensation of robot dynamics. The implemented feed-forward torque control shows the expected improvements of control accuracy. However, external measurements show the influence of joint elasticities as systematic path errors. To further increase the accuracy an iterative learning controller (ILC) based on external camera measurements is designed. The implementation yields to significant improvements of path accuracy. By means of a kind of automated “Teach-In”, an overall effective concept for the automated calibration and optimization of the accuracy of industrial robots in high-dynamic path-applications is realized.

In the course of this paper, an expansion of the dynamic fundamental wave model is presented that approximates the saturation behavior of the self- and mutualinductivities of a permanent magnet synchronous machine (PMSM), to increase the accuracy of model based rotor position observers. The saturation of the individual inductivities is dependent on all magnetization currents. For this purpose, the basic wave model of the machine is considered first. This already takes the mutualinductivities between orthogonal coordinate directions into account. For the analysis of saturation effects, a function for the field strength-dependent permeability of ferromagnetic materials is developed from the magnetization characteristic. The approximate replica of the permeability is carried out via a tangent hyperbolic function. The proportionality between the field strength and the magnetization current is determined by comparing the magnetization characteristic and the no-load characteristic of the PMSM. This leads to the current-dependent self- and mutualinductivities results. The current-dependent inductance matrix is then set up with consideration of the saturation and integrated into the basic wave model. Finally, the simulation results of the approximated saturation effect are presented.