H. Trogmann

Unknown input high-gain observer for internal combustion engine test benches

In the modern world of internal combustion engines, test benches play an important role in the development, the design and the improvement of the engine itself as well as of the control loops implemented in the engine control unit. A test bench replaces the clutch, the gearbox, the shafts and the wheels of a vehicle as well as the load acting on the vehicle. The control of the test bench has to ensure that the internal combustion engine is loaded like in a vehicle, which is best achieved if torque based control is used. As in many cases torque can not be measured and torque estimators are used. However, the internal combustion engines at a test bench are changed frequently due to reduced development cycles and an increased number of different engines. This could require a continuous update of the observer parameters. To take care of this development an enhanced high-gain observer exclusive of a model of the internal combustion engine is proposed in this paper. The proposed method reduces the time required for tuning and parameterization and therefore contributes to a simple and fast change of the internal combustion engine. Measurements on a real test bench show the good performance of the proposed observer compared with measured quantities and quantities determined using an observer including a model of the internal combustion engine.

Nonlinear control of an Electro-Magnetic Actuator under highly dynamic disturbances

This paper presents a control concept for Electro-Magnetic Actuators (EMA) under the effect of strong and dynamic disturbances. The proposed control consists of two components, a backstepping control which exploits the knowledge of the plant and of a disturbance observer. Experiments at a test stand show that the presented approach is able to fulfill very high dynamic requirements and to reject disturbances with a higher dynamics than the actuator under realistic conditions.

Inverse torque control of hydrodynamic dynamometers for combustion engine test benches

Hydrodynamic dynamometers can be used for the entire range of combustion engines from cart engines up to large ship engines, are inexpensive and have a small moment of inertia. Due to their strong nonlinearities and the absence of precise models, they are still rarely used for dynamic testing. Against this background, this paper proposes an inverse control of an approximate form determined experimentally. As the paper shows using measurements on a dynamic truck engine test bench, the proposed approach is able to offer a significantly better performance with respect to the classical implementation thus opening a new path for the intended use for dynamic testing.

Model based control of human heart rate on a bicycle ergometer

A model based controller which regulates the braking intensity of a bicycle ergometer based on information on the heart-rate and breathing volume of the bicyclist is developed. The control system enables training at a desired heart-rate by automatically adjusting the load. The mathematical model was determined using linear continuous time system identification techniques and can reproduce the behavior of the human subject under aerobic metabolic conditions sufficiently well. Data for identification is obtained through an easy to perform test sequence. A linear quadratic controller is synthesized based on the identified model. Experimental results of one subject show the effectiveness of the developed system.

Control oriented modeling of a water brake dynamometer

Water brakes combine high power ratings with a low moment of inertia and in case of high power ratings they are a good alternative to other braking systems. Despite these advantages water brakes are not widely used in dynamic testing as their nonlinearities make them hard to control. Mathematical models of hydrodynamic dynamometers are presented in this paper. A first principles approach is compared with a data-based model and a gray box model. The first principles model is hard to parametrize. In contrast a purely databased linear model is easy to tune but is not able to extrapolate. To increase the extrapolation ability it gets necessary to use a gray box approach which combines the simple structure of a first principles model with a data-based part. The resulting gray box model is best suited to the plant, of simple structure and can be used for the design of a model-based controller.

Hybrid control of type 1 diabetes bolus therapy

The probably best treatment of type 1 diabetes mellitus (T1DM) would be an artificial pancreas (AP). Unfortunately, AP is still not available as AP requires continuous glucose measurements and continuous insulin delivery systems which are not available. The vast majority of patients do not use any of them but instead resort to a few irregularly sampled measurements and single insulin administrations (‘insulin bolus’). Choosing time and quantity of a bolus delivery is critical for the health of T1DM patients, and ideally this should be done on basis of a model. While good physiological models for populations exist, in general they can hardly be tuned to specific patients and are therefore not very useful for bolus choice. Against this background, this paper proposes to see the issue of model based bolus choice in a hybrid framework in which the continuous time meal and insulin model is replaced by a discrete parameterization which associates to each meal and bolus a function of given, patient specific shape whose amplitude depends on the respective amounts. This allows restating the standard model predictive control of the AP design by a line search method.

On the time optimal control for nonlinear saturated systems

Time optimal output transition for input constrained systems is a frequent requirement in many actuation systems. In the case of known and/or linear systems, techniques exist to determine the corresponding control input either analytically and/or experimentally. In other cases however, is a sufficiently precise model is available. Against this background this paper proposes a learning algorithmic approach to solve the issue for unknown nonlinear saturated systems. The proposed approach searches the fastest transition by setting a time optimal but unfeasible trajectory and using learning techniques to recover the nearest feasible one in some sense. To achieve this goal, the proposed approach performs twice a learning process. On one side, it determines by iterative learning control for each suggested trajectory either the optimal input if the trajectory is feasible or an error information if this is not the case. On the other side, a model free learning model generator is used to determine the best feasible reference trajectory.

Speed control of hydrodynamic dynamometers for internal combustion engine test benches

Hydrodynamic dynamometers are primarily used in stationary testing of large internal combustion engines. They are inexpensive, have a small moment of inertia and cover the entire range of internal combustion engines, so their use for dynamical testing would be interesting, but actually they are not used for this purpose due to the strongly nonlinear behavior. Against this background, this paper proposes a cascaded speed control approach based on an inversion of the output nonlinearities. Available degrees of freedom are used to take into account boundaries on fluid temperatures. Measurements on a dynamic truck engine test bench show that the proposed approach is able to offer a significantly better performance with respect to the standard control, allowing an extension of the application of hydrodynamic dynamometers from purely stationary tests to dynamic testing used e.g. in emission legislation.

Time Optimal Compressor Valve Soft Landing by Two Step ILC

Normally compressor load control is realized either passively with a bypass loop with high energy consumption or by an active valve. Actively controlled valves must fulfill a set of conditions which can be expressed as a constrained optimal control. However, compressor valves are subject to serious disturbances which might be unpredictable and difficult to describe, and have complex dynamics. This makes the solution of the real optimal problem difficult and even questionable. Against this background, this paper proposes a two step design approach: first a problem approximation is derived for which an explicit solution of the problem can be computed, then iterative learning control (ILC) is used with the real plant to enforce it. Due to limitation of the actuators, basically a switching solution results, and the tracking of this solution yields a satisfactory solution near to the optimal one. The method has been tested in simulation and experimentally on a special valve design including a small rotational electrical motor and a high gear rate spindle.Copyright

Learning Time Optimal Control of Smart Actuators with Unknown Friction

Active valves are most effective tools to control gas flow in compressors if fast transitions between the open mode and closed mode are needed. Unfortunately, an accurate model including several nonlinear effects and in particular the resistance and gas flow forces is not available, and this prevents the use of standard model based approaches for time optimal control. However, the repetitive nature of the operation of valves suggests the use of learning methods to track a reference in spite of the insufficient information on the control behavior, thus shifting the problem from the search of the time optimal control to the search of the reference corresponding to its solution. To this end, in this paper, a previously proposed algorithm for the iterative determination of the fastest feasible trajectory is analyzed in terms of convergence conditions and applied to the valve model.