Showing papers by "M Maarten Steinbuch published in 2014"

Connecting System Identification and Robust Control for Next-Generation Motion Control of a Wafer Stage

Abstract: Next-generation precision motion systems are lightweight to meet stringent requirements regarding throughput and accuracy. Such lightweight systems typically exhibit lightly damped flexible dynamics in the controller cross-over region. State-of-the-art modeling and motion control design procedures do not deliver the required model complexity and fidelity to control the flexible dynamical behavior. The aim of this paper is to develop a combined system identification and robust control design procedure for high performance motion control and apply it to a wafer stage. Hereto, new connections between system identification and robust control are employed. The experimental results confirm that the proposed procedure significantly extends existing results and enables next-generation motion control design.

Modeling and Control of a Parallel Waste Heat Recovery System for Euro-VI Heavy-Duty Diesel Engines

Abstract: This paper presents the modeling and control of a waste heat recovery system for a Euro-VI heavy-duty truck engine. The considered waste heat recovery system consists of two parallel evaporators with expander and pumps mechanically coupled to the engine crankshaft. Compared to previous work, the waste heat recovery system modeling is improved by including evaporator models that combine the finite difference modeling approach with a moving boundary one. Over a specific cycle, the steady-state and dynamic temperature prediction accuracy improved on average by 2% and 7%. From a control design perspective, the objective is to maximize the waste heat recovery system output power. However, for safe system operation, the vapor state needs to be maintained before the expander under highly dynamic engine disturbances. To achieve this, a switching model predictive control strategy is developed. The proposed control strategy performance is demonstrated using the high-fidelity waste heat recovery system model subject to measured disturbances from an Euro-VI heavy-duty diesel engine. Simulations are performed using a cold-start World Harmonized Transient cycle that covers typical urban, rural and highway driving conditions. The model predictive control strategy provides 15% more time in vapor and recovered thermal energy than a classical proportional-integral (PI) control strategy. In the case that the model is accurately known, the proposed control strategy performance can be improved by 10% in terms of time in vapor and recovered thermal energy. This is demonstrated with an offline nonlinear model predictive control strategy.

Optimization and optimal control in automotive systems

Abstract: Part I: Optimization Methods.- Extremum Seeking,- Trajectory Planning.- Advances in Embedded MPC.- Network Optimization.- Approximate Optimal Solutions of HJB.- Part II: Inter- and Intra-Vehicle System Optimization.- Cooperative Optimal Control.- String Stability.- Trajectory Optimization.- Optimal Control for Vehicle Safety.- Fuel Economy by CACC.- Applications of Computational Optimal Control to Vehicle Dynamics.- Simulation and HIL Application Carmaker.- On Stochastic Optimal Control of Vehicle Speed for Fuel Efficient In-traffic.- Optimal Gearshift Control on Heavy Duty Applications.- Part III: Powertrain Optimization.- Powertrain Assessment.- Optimal Control of Wasteheat Recovery.- Topology Optimization.- Optimal Control of Hybrid Powertrains.- Control of Hybrid Powertrains by Approximately Linear Programming.- Optimal Control of Batteries.- Optimal Control of Fuel Cells.- Part IV: Engine Optimization.- Optimal Control of HCCI.- DOE and Automatic Mapping.- Optimal control of the Short Loading Cycle of a Wheel Loader.- On-ramp Design Suite for Powertrain Control.- Time to Torque Estimation.

Comparison of Bi-Level Optimization Frameworks for Sizing and Control of a Hybrid Electric Vehicle

Abstract: This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.

Fast and Smooth Clutch Engagement Control for a Mechanical Hybrid Powertrain

Abstract: Automatically controlled clutches are widely used in advanced automotive powertrains to transmit a demanded torque while synchronizing the rotational speeds of the shafts. The two objectives of the clutch engagement controller are a fast clutch engagement to reduce the frictional losses and thermal load, and a smooth clutch engagement to accurately track the demanded torque without a noticeable torque dip. Meanwhile, the controller is subjected to standard constraints such as model uncertainty and limited sensor information. This paper presents a new controller design that explicitly separates the control laws for each objective by introducing three clutch engagement phases. The time instants to switch between the subsequent phases are chosen such that the desired slip acceleration is achieved at the time of clutch engagement. The latter can be interpreted as a single calibration parameter that determines the tradeoff between fast and smooth clutch engagement. The controller is elaborated for a mechanical hybrid powertrain that uses a flywheel as a secondary power source and a continuously variable transmission. Simulations and experiments on a test rig show that the control objectives are realized with a robust and relatively simple controller.

Model predictive control of a waste heat recovery system for automotive diesel engines

Abstract: In this paper, a switching Model Predictive Control strategy is designed for an automotive Waste Heat Recovery system with two parallel evaporators. The objective is to maximize Waste Heat Recovery system output power, while satisfying safe operation under highly dynamic disturbances from the engine. Safe system operation is associated with vapor state after the evaporators. The closed-loop performance of the Model Predictive Control strategy is demonstrated on a high-fidelity validated Waste Heat Recovery system model subject to realistic disturbances from an Euro VI heavy-duty diesel engine. The simulation results, based on a World Harmonized Transient Cycle, demonstrate that the proposed control strategy outperforms a classical PI control strategy in terms of safety and relative average power, up to 15% and 3%, respectively. cop. 2014 IEEE.

Gear shift map design methodology for automotive transmissions

Abstract: In this paper, a design methodology is developed to condtruct the gear shift map for the automotive transmissions used in conventional and hybrid electric vehicles. The methodology utilizes an optimal gear shift strategy to derive the optimal gear shift patterns over a wide range of driving profiles. Then, statistical theory is applied to analyse the obtained gear shift patterns for a gear shift map. The designed gear shift map improves the fuel economy and driveability. It is consistent, robust to shift busyness and real-time implementable. The design process is flexible and time efficient such that applicability to various powertrain systems configured with discrete-ratio transmissions is possible. Validation on a test vehicle proves the effectiveness of the design methodology.

A dynamic state observer for real-time reconstruction of the tokamak plasma profile state and disturbances

Abstract: A dynamic observer is presented which can reconstruct the internal state of a tokamak fusion plasma, consisting of the spatial distribution of current and temperature, from measurements. Today, the internal plasma state is usually reconstructed by solving an ill-conditioned inversion problem using a large number of measurements at one point in time. Such an approach does not take into account the time evolution of the underlying dynamical system (the plasma) and strongly relies on (technically challenging) internal measurements. The observer-based approach presented here includes the dynamics of the plasma current and temperature, modeled by a set of coupled nonlinear 1-D PDEs which are discretized in space and time to yield a finite-dimensional nonlinear model. The observer, which is based on an Extended Kalman Filter, estimates the state of an augmented model which includes additive state disturbances modeled as a random walk. Simulation results demonstrate the effectiveness of this observer in the case of perturbed models and input disturbances.

Real-time optical plasma boundary reconstruction for plasma position control at the TCV Tokamak

Abstract: A dual, high speed, real-time visible light camera setup was installed on the TCV tokamak to reconstruct optically and in real-time the plasma boundary shape. Localized light emission from the plasma boundary in tangential view, broadband visible images results in clearly resolved boundary edge-features. These projected features are detected in real-time and transformed to the poloidal plane to obtain a measurement of the plasma boundary. Plasma boundary reconstructions of diverted plasma discharges are presented, showing agreement of within 1 cm compared with magnetic equilibrium reconstruction. The resulting real-time plasma shape measurement is applied in a feedback control loop for the plasma position, demonstrating effective stabilization and tracking of the plasma vertical position.

Topology and Flywheel Size Optimization for Mechanical Hybrid Powertrains

Abstract: Mechanical hybrid powertrains have the potential to improve the fuel economy of passenger vehicles at a relatively low cost, by adding a flywheel and only mechanical transmission components to a conventional powertrain. This paper presents a systematic approach to optimizing the topology and flywheel size, which are the key design parameters of a mechanical hybrid powertrain. The topology is optimized from a limited set of over 20 existing mechanical hybrid powertrains described in the literature. After a systematic classification of the topologies, a set of four competitive powertrains is selected for further investigation. The fuel-saving potential of each hybrid powertrain is computed using an optimal energy controller and modular component models, for various flywheel sizes and for three certified driving cycles. The hybridization cost is estimated based on the type and size of the components. Other criteria, such as control complexity, clutch wear, and driving comfort are qualitatively evaluated to put the fuel-saving potential and the hybridization cost into a wider perspective. Results show that, for each of the four investigated hybrid powertrains, the fuel-saving benefit returns the hybridization investment well within (about 50%) the service life of passenger vehicles. The optimal topology follows from a discussion that considers all the optimization criteria. The associated optimal flywheel size has an energy storage capacity that is approximately equivalent to the kinetic energy of the vehicle during urban driving (50 km/h).

Exploiting additional actuators and sensors for nano-positioning robust motion control

Abstract: The ongoing need for miniaturization and an increase of throughput in IC-manufacturing is obstructed by performance limitations in motion control of nano-positioning wafer stages. These limitations are imposed by flexible dynamical behavior, associated with structural deformations of the nano-positioning stages. The aim of this research is to investigate limits on achievable performance in a conventional control configuration and to mitigate these limits through the use of additional actuators and sensors. To this end, a systematic framework for control design using additional actuators and sensors in the generalized plant configuration is presented, which leads to a well-posed ℋ ∞ -control optimization problem that extends conventional design approaches in a natural way and exploits physical insight to address structural deformations in weighting filter design. Through an experimental confrontation of the design framework with a prototype next-generation nano-positioning motion system, successful performance enhancement beyond the conventional limits is demonstrated.

Accuracy aspects in motion feedforward tuning

Abstract: Feedforward control can significantly improve the performance of a motion system through compensation of known disturbances. Recently, new feedforward algorithms have been proposed that exploit measured data from previous tasks and a suitable feedforward parametrization to attain high performance. The aim of this paper is to analyze the accuracy of these approaches. To achieve this, related results from closed-loop identification are exploited in feedforward control. Furthermore, a new algorithm is proposed that leads to optimal accuracy. The results are confirmed in a simulation study of a motion system.

Optimal Control of a Mechanical Hybrid Powertrain With Cold-Start Conditions

Abstract: This paper investigates the impact of cold-start conditions on the fuel-saving potential and the associated optimal energy controller of a mechanical hybrid powertrain. The mechanical hybrid powertrain uses a flywheel system to add fuel-saving functionalities to a conventional powertrain, which consists of an internal combustion engine and a continuously variable transmission (CVT). The cold-start conditions refer to a low powertrain temperature, which increases the frictional power dissipation in the engine and transmission, and a stationary (or energyless) flywheel system, which must be energized to a minimum energy level before it can be effectively utilized. The heating of the powertrain and the initialization of the flywheel system can be influenced by the energy controller, which controls the power distribution between the engine, the flywheel, and the vehicle. The energy controller aims at minimizing the overall fuel consumption for a given driving cycle. The optimal energy controller is found analytically for a simplified model to gain qualitative insights in the controller and numerically using dynamic programming for a detailed model to quantify the impact on the fuel consumption. The results show that the cold-start conditions have a significant impact on the fuel-saving potential, yet a negligible impact on the optimal energy controller. The latter result implies that the temperature state can be eliminated from the state space of the energy controller, which is an important step toward the design of an effective yet simple energy controller suitable for real-time implementation.

Extremum seeking control with data-based disturbance feedforward

Abstract: This paper presents a practical extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for the changes in the extremum of a related performance function can be measured. The additional information is used to improve accuracy, convergence speed and robustness of the underlying ESC scheme. Based on the disturbance measurements a map between them and the optimal inputs is iteratively constructed and used as an extremum seeking feedforward. A supervising state-machine is designed to regulate feedforward and search processes ensuring the latter is conducted in the close vicinity of an extremum. The search is based on the sinusoidal input perturbation introduced each time the disturbance is detected and removed once the optimal set-point is identified. Simulation results for the cases of photovoltaic and turbine driven electrical generator systems demonstrate the benefits of the presented design.

Gain Scheduling Control of a Walking Piezo Actuator

Abstract: Piezoelectric actuators are commonly used to drive high-precision stages. In this paper, a walking piezo actuator containing four piezoelectric legs is used to drive a high-precision nanomotion stage with constant velocity and point-to-point reference signals. The gain of the system is dependent on the momentary orientation of the piezo legs during the walking movement. The aim of this paper is to design a feedback controller that employs knowledge of the varying gain of the drive legs over one drive cycle. The gain variation is determined by a combination of local and global identification and modeling techniques. The excitation signal for the local frequency response function measurements at different positions of the legs in the drive cycle is designed to avoid stick-slip effects between the drive legs and the drive surface of the stage. Using the knowledge of the system variations, a linear parameter-varying model and a gain scheduling feedback controller are designed for the nanomotion stage. Experiments show that the designed gain scheduling controller reduces the tracking error and settling time compared to a robust H∞ controller with a comparable closed-loop bandwidth up to 53% and 80%, respectively.

On inferential iterative learning control : with example on a printing system

Abstract: Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are utilized to arrive at control structures suited for inferential ILC. The proposed frameworks extend earlier results and encompass various controller structures. The results are supported with a simulation example.