Showing papers by "M Maarten Steinbuch published in 2015"

Functional and Cost-Based Automatic Generator for Hybrid Vehicles Topologies

Abstract: The energy efficiency of a hybrid electric vehicle is dictated by the topology (coupling option of power sources/sinks), choice (technology), and control of components. The first design area among these, the topology, has the biggest flexibility of them all, yet, so far in the literature, the topology design is limited investigated due to its high complexity. In practice, a predefined small set of topologies is used to optimize their energy efficiency by varying the power specifications of the main components (sizing). By doing so, the complete design of the vehicle is, inherently and to a certain extent, suboptimal. Moreover, various complex topologies appear on the automotive market and no tool exists to optimally choose or evaluate them. To overcome this design limitation, in this paper, a novel framework is presented that deals with the automatic generation of possible topologies given a set of components (e.g., engine, electric machine, batteries, or transmission elements). This paper uses a platform (library of components) and a hybrid knowledge base (functional and cost-based principles) to set up a constraint logic programming problem, and outputs a set of feasible topologies for hybrid electric vehicles. These are all possible topologies that could be built considering a fixed, yet large, set of components. Then, by using these results, insights are given on what construction principles are mostly critical for simulations time, and what topologies could be selected as candidate topologies for sizing and control studies. Such a framework can be used for any powertrain application; it can offer the topologies to be investigated in the design phase and can provide insightful results for optimal design analyses.

Control of the tokamak safety factor profile with time-varying constraints using MPC

Abstract: A controller is designed for the tokamak safety factor profile that takes real-time-varying operational and physics limits into account. This so-called model predictive controller (MPC) employs a prediction model in order to compute optimal control inputs that satisfy the given limits. The use of linearized models around a reference trajectory results in a quadratic programming problem that can easily be solved online. The performance of the controller is analysed in a set of ITER L-mode scenarios simulated with the non-linear plasma transport code RAPTOR. It is shown that the controller can reduce the tracking error due to an overestimation or underestimation of the modelled transport, while making a trade-off between residual error and amount of controller action. It is also shown that the controller can account for a sudden decrease in the available actuator power, while providing warnings ahead of time about expected violations of operational and physics limits. This controller can be extended and implemented in existing tokamaks in the near future.

Implementation of Dynamic Programming for Optimal Control Problems With Continuous States

Abstract: Dynamic programming is a numerical method to solve a dynamic optimal control problem. Due to its numerical framework, it is very suitable to describe discrete dynamics, nonlinear characteristics, and nonconvex constraints. The implementation of continuous states in the discrete framework, however, may lead to optimization inaccuracies. This brief addresses implementation methods with fundamentally different utilizations of the nodes in the quantized time-state space. A new implementation method is presented, which combines the advantages of numerical and analytical optimization techniques to substantially improve the optimization accuracy for a given quantization of the continuous state. If desired, the computation time can be substantially reduced for a given accuracy by lowering the quantization resolution. As a case study, the optimal energy controller is computed for a mechanical hybrid powertrain, which is characterized by switched dynamics, active state constraints, and nonconvex control constraints. Results show that the optimization accuracy of the new method is superior to that of the conventional method based on nearest neighbor rounding. For a given desired accuracy, the computation time is reduced by an order of magnitude.

Disturbance Rejection in Diesel Engines for Low Emissions and High Fuel Efficiency

Abstract: This brief presents a novel and time-efficient control design for modern heavy-duty diesel engines using a variable geometry turbine and an exhaust gas recirculation valve. The goal is to simultaneously and robustly achieve low fuel consumption and low emissions of nitrogen oxides (NO x ) and particulate matter (PM). A new combination of three controlled outputs is used: 1) specific engine-out NO x emissions; 2) air-fuel equivalence ratio; and 3) the pressure difference between intake and exhaust manifold, which reflect NO x and PM emissions and fuel efficiency, respectively. It is shown that this combination allows for effective disturbance rejection and results in a well-conditioned system. An underactuated input–output system is formed, for which a linear feedback controller is designed. In addition to this feedback controller, a feedforward controller is implemented, which improves the torque response and lowers the PM emissions during fast changes in torque demand. The combined control system is suitable for the full range of speed and load variations. This new controller is tested experimentally on a modern heavy-duty engine running a hot world harmonized transient cycle and compared with a baseline controller. The new controller reduces the NO x and PM emissions by 3.9% and 11.7%, respectively, without a fuel penalty.

Robust high performance bilateral teleoperation under bounded time-varying dynamics

Abstract: The control design for bilateral teleoperation systems represents a challenge in finding the proper balance in the inherent tradeoff between transparency and stability. To address this problem, we propose a methodology in which we develop a parametric model of the teleoperation system. Subsequently, we exploit robust control techniques based on linear matrix inequalities to design controllers that aim to achieve a predefined performance, and are robust to bounded but arbitrarily fast-time-varying parametric uncertainties. We present analysis, simulation, and experimental results of the designed controller, thus showing that the assumptions made during modeling are appropriate and the effectiveness of the method to tradeoff perfect transparency and stability.

From Optimal to Real-Time Control of a Mechanical Hybrid Powertrain

Abstract: This brief presents the design of an energy controller for a mechanical hybrid powertrain, which is suitable for implementation in real-time hardware. The mechanical hybrid powertrain uses a compact flywheel module to add hybrid functionalities to a conventional powertrain that consists of an internal combustion engine and a continuously variable transmission. The control objective is to minimize the overall fuel consumption for a given driving cycle. The design approach follows a generic framework to: 1) solve the optimization problem using optimal control; 2) make the optimal controller causal using a prediction of the future driving conditions; and 3) make the causal controller robust by tuning of one key calibration parameter. The highly constrained optimization problem is solved with dynamic programming. The future driving conditions are predicted using a model that smoothly approximates statistical data, and implemented in the receding model predictive control framework. The controller is made tunable by rule extraction from the model predictive controller, based on physical understanding of the system. The resulting real-time controller is transparent, causal, and robust, where the latter is shown by simulations for various driving cycles and start conditions.

Second-Order Iterative Learning Control for Scaled Setpoints

Abstract: Iterative learning control (ILC) is a control technique for systems subject to repetitive setpoints or disturbances. However, in many applications, the setpoint is not strictly repetitive, and the learning process should start all over from the beginning if the setpoint changes. In this brief, point-to-point movements with different magnitudes will be considered, which are constructed by scaling a nominal setpoint. Second-order ILC with an adaptive low-pass filter in the trial domain is used to accurately track these scale varying setpoints under the influence of disturbances that are either repetitive or experience the same scaling as the setpoint. Experiments have been carried out to validate the proposed method.

Control of a Waste Heat Recovery system with decoupled expander for improved diesel engine efficiency

Abstract: In this paper, a switching Model Predictive Control strategy is proposed for a Waste Heat Recovery system in heavy-duty automotive application. The objective is to maximize the WHR system output power while satisfying the output constraints under highly dynamic engine variations. For control design, a WHR system architecture with the expander and pumps decoupled from the engine is considered. Compared to a WHR system with the expander coupled to the engine, up to 29% more output power is obtained for the considered design. This holds for both steady state and highly dynamic engine conditions. The simulation results are obtained using a validated high-fidelity WHR system model with realistic disturbances from a Euro VI heavy-duty diesel engine.

Performance trade-offs in disturbance feedforward compensation of active hard-mounted vibration isolators

Abstract: With disturbance feedforward compensation (DFC), input disturbances are measured and compensated to cancel the effect of the disturbance. Perfect cancellation is not possible in practice due to the causal nature of DFC, in which the compensation generally comes too late. Therefore, non-perfect plant inversion, controller discretization and sensor dynamics lead to a non-zero residual error. The properties of this residual error are described in the frequency domain using a theoretical framework that is closely related to the design constraints known from filtering theory. It is shown that, like in feedback control systems, performance limitations of DFC systems are described by a waterbed effect. An experimental validation is included to demonstrate the properties of the residual error on an active hard-mounted vibration isolation setup.

Nanometre-accurate form measurement machine for E-ELT M1 segments

Abstract: To enable important scientific discoveries, ESO has defined a new ground-based telescope: the European Extremely Large Telescope (E-ELT). The baseline design features a telescope with a 39-m-class primary mirror (M1), making it the largest and most powerful telescope in the world. The M1 consists of 798 hexagonal segments, each about 1.4 m wide, but only 50 mm thick. In the last stages of the manufacturing process of these M1 segments, a nanometre-accurate metrology method is required for the M1 to be within specifications. The segments have to be measured on their whiffle-tree support structures with a nanometre-level uncertainty, with a total budget on form accuracy of 50 nm RMS for any segment assembly. In this paper a measurement machine design is presented based on a non-contact single-point scanning technique, capable of measuring with nanometre accuracy, being universal, fast and with low operational costs, providing suitable metrology for M1 segments. A tactile precision probe is implemented to be able to use the machine in earlier stages of the segment manufacturing process. In particular, this paper describes the design of the air-bearing motion system and the separate metrology system based on a moving Sintered Silicon Carbide tube, a fixed Zerodur metrology frame and an interferometric system for a direct and short metrology loop. Preliminary calculations show nanometre-level measurement uncertainty after calibration.

Linear viscoelastic fluid characterization of ultra-high-viscosity fluids for high-frequency damper design

Abstract: This paper presents the use of a state of the art damper for high-precision motion stages as a sliding plate rheometer for measuring linear viscoelastic properties in the frequency range of 10 Hz–10 kHz. This device is relatively cheap and enables to obtain linear viscoelastic (LVE) fluid models for practical use in precision mechanics applications. This is an example of reversed engineering, i.e., turning a machine part into a material characterization device. Results are shown for a high-viscosity fluid. The first part of this paper describes the damper design that is based on a high-viscosity fluid. This design is flexure-based to minimize parasitic nonlinear forces such as hysteresis and stick-slip. In the second part of the paper, LVE fluid characterization by means of the damper setup is presented. Measurements are performed and model parameters are fitted by a non-convex optimization algorithm in order to obtain the frequency-dependent behavior of the fluid. The resulting fluid model is validated by comparison with a second measurement with a different damper geometry. This paper shows that LVE fluid characterization between 10 Hz and 10 kHz for elastic high-viscosity fluids is possible with a motion stage damper for which the undamped behavior is known.

Identification for robust control of complex systems : algorithm and motion application

Abstract: Increasing performance demands in control applications necessitate accurate modeling of complex systems for control. The aim of this chapter is to develop a new system identification algorithm that delivers models that are suitable for subsequent robust control design and can be reliably applied to complex systems. To achieve this, an identification algorithm is developed that delivers a system model in terms of recently developed coprime factorizations and thereby extends classical iterative procedures to the closed-loop case. These coprime factorizations have important advantages for uncertainty modeling and robust controller synthesis of complex systems. A numerically optimal implementation is presented that relies on orthonormal polynomials with respect to a data-dependent discrete inner product. Experimental results on a nanometer-accurate positioning system confirm that the algorithm is capable of delivering the required coprime factorizations and the implementation is numerically reliable, which is essential for complex systems as common implementations suffer from severe ill-conditioning.

Model-based geometric calibration for medical x-ray systems.

Abstract: Purpose: Currently applied calibration approaches lead to satisfying 3D roadmapping overlay and 3D reconstruction accuracies; however, the required calibration times are extensive. The aim of this paper is the introduction of a novel model-based approach for geometric system calibrations, leading to a significant reduction of calibration times. Methods: By using physical insight into the system, a physical model is derived which can be exploited to predict geometric calibrations parameters. Model-parameters are estimated using a limited set of phantom-based measurement data. Effectively, the calibration procedure is recast to a parameter identification experiment. Results: The potential of the proposed approach is illustrated by virtue of a benchmark object, successful reconstruction of a clinical phantom, and comparison to phantom-based accuracies. Conclusions: Accurate models are required to achieve the desired accuracies. Based on the results in this work, the approach seems to be feasible for practical applications; however, to achieve all the desired specifications, future research should focus on enhanced modeling techniques.

Effect of gear shift and engine start losses on energy management strategies for hybrid electric vehicles

Abstract: In this study, the energetic loss models involved with gear shift and engine start in a parallel passenger hybrid electric vehicle (HEV) will be introduced, and their effects on energy management strategies will be analysed. The simulation results disclose a superior fuel efficient property of the powershift-automated manual transmission (PS-AMT) HEV over the AMT one. However, by reducing the interruption time in the gear shift process of the AMT as much as possible, its fuel deficiency, when compared to the PSAMT, can be reduced noticeably. Furthermore, with an assumption of given preview route information, a model predictive control (MPC) algorithm is applied to investigate an achievable fuel saving with respect to the prediction horizon. It reveals a minimum prediction horizon of 5 s is required for the MPC-based controller to get the possible maximum fuel economy under the impact of the engine start loss.

Semi-task-dependent and uncertainty-driven world model maintenance

Abstract: Nearly every task a domestic robot could potentially solve requires a description of the robot's environment which we call a world model. One problem underexposed in the literature is the maintenance of world models. Rather than on creating a world model, this work focuses on finding a strategy that determines when to update which object in the world model. The decision whether or not to update an object is based on the expected information gain obtained by the update, the action cost of the update and the task the robot performs. The proposed strategy is validated during both simulations and real world experiments. The extended series of simulations is performed to show both the performance gain with respect to a benchmark strategy and the effect of the various parameters. The experiments show the proposed approach on different set-ups and in different environments.

Time-delay pre-filter design for vibration free tracking of periodic reference signals

Abstract: The aim of this work is the development of time-delay filters that enable tracking of periodic signals with zero phase error Time-Delay control or input-shaping is a proven technique to reduce motion induced vibrations for systems with lightly damped modes subject to point-to-point movements An extension is proposed for systems subject to periodic reference signals, for which a closed-form solution is provided for second-order systems, followed by a generalization for higher order systems and multi-harmonic reference signals It is shown that the theory enables a significant reduction of tracking errors for non-differentiable reference signals The paper concludes with the presentation of an approach to desensitize the pre-filter to uncertainties in the forcing frequencies

Notch Filters for Port-Hamiltonian Systems

Abstract: Many powerful tools exist for control design in the frequency domain, but are theoretically only justified for linear systems. On the other hand, nonlinear control deals with control design methodologies that are theoretically justified for a larger and more realistic class of systems, but primarily dealing with stability and to a lesser extent with performance. In this technical note a standard linear notch filter is modeled in the port-Hamiltonian (PH) framework, thereby proving that the notch filter is a passive system. The notch filter can then be interconnected with any other (nonlinear) PH system, while preserving the overall passivity property. By doing so, we can combine a frequency-based control method to improve performance, the notch filter, with the nonlinear control methodology of passivity-based control.

Combining ILC and repetitive control to handle repeating, event-triggered disturbances in precision inkjet printing

Abstract: Learning and repetitive control are powerful instruments in handling recurring disturbances. Repetitive control properly handles constantly repeating variations, while iterative learning control is well-equipped when it comes to handling event triggered deviations. Neither controller is well equipped to adequately deal with repetitive disturbances, which are only present during limited, but varying, periods of time. These are often seen in precision handling systems such as production inkjet printers. This paper combines ILC and RC using a structure which originated in multi-period repetitive control. It is shown that this enables full suppression of the repeating event-triggered disturbances. The approach is successfully demonstrated in an illustrative simulation, as well as by using experimental data from a precision inkjet printing setup.

A new instrument for deep brain stimulation surgery

Abstract: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.

Structural analysis of control design objectives for vehicular propulsion systems

Abstract: In the optimal control design for vehicular propulsion systems, the objective function describing the design objectives (fuel economy, driveability, comfort, emissions, battery operation, battery aging, etc.) plays an important role in defining the optimal solution. However, the definition of the objective function is not often analysed explicitly and thoroughly. In this study, a method of objectively analysing and evaluating the objective function is introduced. The method uses the singular value decomposition (SVD) technique to analyse the correlations among the design objectives and investigate their sensitivity to an optimal control algorithm. Accordingly, the dependent design objective(s) will be omitted such that the objective function can be simplified. This will reduce the complexity of the control design.

Identification of cyclic disturbances in positioning systems via compressive sensing

Abstract: In industrial precision positioning systems the measurement position is hardly ever the same as the location of the actuator. The properties and imperfections of the actuator and the underlying components between the sensor and the actuator mainly lead to deterministic reproducible position errors. The advantage of these systematic cyclic disturbances is that they can be compensated for, once identified. In this paper we use nonuniform sampling combined with Compressive Sensing (CS) to identify high spatial frequency disturbances in positioning systems when the spatial sample period is limited. The proposed strategy is implemented on the paper positioning unit of a wide format printer, to identify the cyclic disturbances in the positioning of paper with respect to the printheads. Based on CS, we present a strategy to identify the cyclic disturbances in the paper positioning from randomly obtained relative position error measurements. Experiments with a limited spatial sample period show that the high disturbance frequencies are also successfully identified.