Showing papers in "IEEE Transactions on Control Systems and Technology in 2007"

Predictive Active Steering Control for Autonomous Vehicle Systems

Abstract: In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

A Survey of Control Issues in Nanopositioning

Abstract: Nanotechnology is the science of understanding matter and the control of matter at dimensions of 100 nm or less. Encompassing nanoscale science, engineering, and technology, nanotechnology involves imaging, measuring, modeling, and manipulation of matter at this level of precision. An important aspect of research in nanotechnology involves precision control and manipulation of devices and materials at a nanoscale, i.e., nanopositioning. Nanopositioners are precision mechatronic systems designed to move objects over a small range with a resolution down to a fraction of an atomic diameter. The desired attributes of a nanopositioner are extremely high resolution, accuracy, stability, and fast response. The key to successful nanopositioning is accurate position sensing and feedback control of the motion. This paper presents an overview of nanopositioning technologies and devices emphasizing the key role of advanced control techniques in improving precision, accuracy, and speed of operation of these systems.

A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles

Abstract: Hybrid electric vehicles (HEVs) improvements in fuel economy and emissions strongly depend on the energy management strategy. The parallel HEV control problem involves the determination of the time profiles of the power flows from the engine and the electric motor. This is also referred to as the power split between the conventional and the electric sources. The objective of HEV control is in fact to find out the sequence of optimal power splits at each instant of time that minimizes the fuel consumption over a given driving schedule. Big obstacles to the control design are the model complexity and the necessity of "a priori" knowledge of torque and velocity profiles. This paper presents three different energy management approaches for the control of a parallel hybrid electric sport-utility-vehicle that do not require a priori knowledge of the driving cycle. The considered approaches are: a rule-based control, an adaptive equivalent fuel consumption minimization strategy (A-ECMS), and the Hinfin control. Results, compared with the optimal solution given by the dynamic programming, show that the A-ECMS strategy is the best performing strategy

Design and Modeling of a High-Speed AFM-Scanner

Abstract: A new mechanical scanner design for a high-speed atomic force microscope (AFM) is presented and discussed in terms of modeling and control. The positioning range of this scanner is 13 mum in the X- and Y-directions and 4.3 mum in the vertical direction. The lowest resonance frequency of this scanner is above 22 kHz. This paper is focused on the vertical direction of the scanner, being the crucial axis of motion with the highest precision and bandwidth requirements for gentle imaging with the AFM. A second- and a fourth-order mathematical model of the scanner are derived that allow new insights into important design parameters. Proportional-integral (Pl)-feedback control of the high-speed scanner is discussed and the performance of the new AFM is demonstrated by imaging a calibration grating and a biological sample at 8 frames/s.

Feedback-Linearized Inverse Feedforward for Creep, Hysteresis, and Vibration Compensation in AFM Piezoactuators

Abstract: In this brief, we study the design of a feedback and feedforward controller to compensate for creep, hysteresis, and vibration effects in an experimental piezoactuator system. First, we linearize the nonlinear dynamics of the piezoactuator by accounting for the hysteresis (as well as creep) using high-gain feedback control. Next, we model the linear vibrational dynamics and then invert the model to find a feedforward input to account vibration - this process is significantly easier than considering the complete nonlinear dynamics (which combines hysteresis and vibration effects). Afterwards, the feedforward input is augmented to the feedback-linearized system to achieve high-precision highspeed positioning. We apply the method to a piezoscanner used in an experimental atomic force microscope to demonstrate the method's effectiveness and we show significant reduction of both the maximum and root-mean-square tracking error. For example, high-gain feedback control compensates for hysteresis and creep effects, and in our case, it reduces the maximum error (compared to the uncompensated case) by over 90%. Then, at relatively high scan rates, the performance of the feedback controlled system can be improved by over 75% (i.e., reduction of maximum error) when the inversion-based feedforward input is integrated with the high-gain feedback controlled system.

Cooperative UAV Formation Flying With Obstacle/Collision Avoidance

Abstract: Navigation problems of unmanned air vehicles (UAVs) flying in a formation in a free and an obstacle-laden environment are investigated in this brief. When static obstacles popup during the flight, the UAVs are required to steer around them and also avoid collisions between each other. In order to achieve these goals, a new dual-mode control strategy is proposed: a "safe mode" is defined as an operation in an obstacle-free environment and a "danger mode" is activated when there is a chance of collision or when there are obstacles in the path. Safe mode achieves global optimization because the dynamics of all the UAVs participating in the formation are taken into account in the controller formulation. In the danger mode, a novel algorithm using a modified Grossberg neural network (GNN) is proposed for obstacle/collision avoidance. This decentralized algorithm in 2-D uses the geometry of the flight space to generate optimal/suboptimal trajectories. Extension of the proposed scheme for obstacle avoidance in a 3-D environment is shown. In order to handle practical vehicle constraints, a model predictive control-based tracking controller is used to track the references generated. Numerical results are provided to motivate this approach and to demonstrate its potential.

Effective Coverage Control for Mobile Sensor Networks With Guaranteed Collision Avoidance

Abstract: This paper studies the problem of dynamically covering a given region in the plane using a set of mobile sensor agents. A novel problem formulation is proposed that addresses a number of important multiagent missions. The coverage goal, which is to cover a given search domain using multiple mobile sensors such that each point is surveyed until a certain preset level is achieved, is formulated in a mathematically precise problem statement. A control law is developed that guarantees to meet the coverage goal. This control law is modified to guarantee that a partially connected fleet also attains the coverage goal. Finally, a collision avoidance component is added to the controller to guarantee that the agents do not collide. The new controller is shown to safely achieve coverage. Several numerical examples are provided to illustrate the main results.

A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios

Abstract: In this paper, we propose a novel reference model-based control approach for automotive longitudinal control. The reference model is nonlinear and provides dynamic solutions consistent with safety constraints and comfort specifications. The model is based on physical laws of compliant contact and has the particularity that its solutions can be explicitly described by integral curves. This allows to characterize the set of initial condition for which the constraints can be met. This model is combined with a simple feedback loop used to compensate unmodeled dynamics and external disturbances. Model simulations together with experimental results are also presented

Kalman Filter-Based Integration of DGPS and Vehicle Sensors for Localization

Abstract: We present a position estimation scheme for cars based on the integration of global positioning system (GPS) with vehicle sensors. The aim is to achieve enough accuracy to enable in vehicle cooperative collision warning, i.e., systems that provides warnings to drivers based on information about the motions of neighboring vehicles obtained by wireless communications from those vehicles, without use of ranging sensors. The vehicle sensors consist of wheel speed sensors, steering angle encoder, and a fiber optic gyro. We fuse these in an extended Kalman filter. The process model is a dynamic bicycle model. We present data from about 60 km of driving in urban environments including stops, intersection turns, U-turns, and lane changes, at both low and high speeds. The data show the filter estimates position, speed, and heading with the accuracies required by cooperative collision warning in all except two kinds of settings. The data also shows GPS and vehicle sensor integration through a bicycle model compares favorably with position estimation by fusing GPS and inertial navigation system (INS) through a kinematic model.

Predictive Control of a Diesel Engine Air Path

Abstract: This brief addresses the model-based control of the air path of diesel engines in terms of an optimal control problem with input constraints which can be solved using model predictive algorithms. A multilinear model identified from data and a switched controller design are used to cope with the nonlinearity of the engine. Experimental results on a production engine confirm that the proposed control method strongly improves the dynamics of the air path and enormously reduces the parameterization work if compared with the conventional approach. To obtain improvements in emissions as well, the new controller approach cannot simply be plugged in at the site of the conventional one, but new set points must be determined. After such a redesign, improvements of 50% in terms of nitrogen oxides and of 10% in terms of particulate matter have been recorded without a net consumption increase, the main price being the increased activity of the turbocharger vane and especially of the exhaust gas recirculation valve

Finding the Direction of Disturbance Propagation in a Chemical Process Using Transfer Entropy

Abstract: In continuous chemical processes, variations of process variables usually travel along propagation paths in the direction of the control path and process flow. This paper describes a data-driven method for identifying the direction of propagation of disturbances using historical process data. The novel concept is the application of transfer entropy, a method based on the conditional probability density functions that measures directionality of variation. It is sensitive to directionality even in the absence of an observable time delay. Its performance is studied in detail and default settings for the parameters in the algorithm are derived so that it can be applied in a large scale setting. Two industrial case studies demonstrate the method

Iterative Control Approach to Compensate for Both the Hysteresis and the Dynamics Effects of Piezo Actuators

Abstract: In this brief, the compensation for both the nonlinear hysteresis and the vibrational dynamics effects of piezo actuators is studied. Piezo actuators are the enabling device in many applications such as atomic force microscopy (AFM) to provide nano- to atomic-levels precision positioning. During high-speed, large-range positioning, however, large positioning errors can be generated due to the combined hysteresis and dynamics effects of piezo actuators, making it challenging to achieve precision positioning. The main contribution of this brief is the use of an inversion-based iterative control (IIC) technique to compensate for both the hysteresis and vibrational dynamics effects of piezo actuators. The convergence of the IIC algorithm is investigated by capturing the input-output behavior of piezo actuators with a cascade model consisting of a rate-independent hysteresis at the input followed by the dynamics part of the system. The size of the hysteresis and the vibrational dynamics variations that can be compensated for (by using the IIC method) is quantified. The IIC approach is illustrated through experiments on a piezotube actuator used for positioning on an AFM system. Experimental results show that high-speed, large-range precision positioning can be achieved by using the proposed IIC technique. Furthermore, the proposed IIC algorithm is also applied to experimentally validate the cascade model and the rate-independence of the hysteresis effect of the piezo actuator.

High-Performance Control of Piezoelectric Tube Scanners

Abstract: In this paper, a piezoelectric tube of the type typically used in scanning tunneling microscopes (STMs) and atomic force microscopes (AFMs) is considered. Actuation of this piezoelectric tube is hampered by the presence of a lightly damped low-frequency resonant mode. The resonant mode is identified and damped using a positive velocity and position feedback (PVPF) controller, a control technique proposed in this paper. Input signals are then shaped such that the closed-loop system tracks a raster pattern. Normally, piezoelectric tubes are actuated using voltage amplifiers. Nonlinearity in the form of hysteresis is observed when actuating the piezoelectric tubes at high amplitudes using voltage amplifiers. It has been known for some time that hysteresis in piezoelectric actuators can be largely compensated by actuating them using charge amplifiers. In this paper, high-amplitude actuation of a piezoelectric tube is achieved using a charge amplifier.

A Model-Free Cross-Coupled Control for Position Synchronization of Multi-Axis Motions: Theory and Experiments

Abstract: In this brief, a model-free cross-coupled controller is proposed for position synchronization of multi-axis motions. The position synchronization error of each axis is defined as the differential position error between this axis and its two adjacent axes, which is then coupled with the position error to form a coupled position error. A proportional and derivative (PD)-type synchronization controller with feedback of this coupled position error is proposed and proven to guarantee asymptotic convergence to zero of both position and synchronization errors in a setpoint position control. A setpoint tracking controller is further developed by adding feedforward control terms and a saturation function to the PD synchronization controller. The proposed method is easy to implement in practice since it is model free and the control gains are time-invariant. Experiments are performed to verify effectiveness of the proposed approach

Distributed Robust Receding Horizon Control for Multivehicle Guidance

Abstract: This paper presents a new distributed robust model predictive control algorithm for multivehicle trajectory optimization and demonstrates the approach with numerical simulations and multivehicle experiments. The technique builds on the robust-safe-but-knowledgeable (RSBK) algorithm, which is developed in this paper for the multivehicle case. RSBK uses constraint tightening to achieve robustness to external disturbances, an invariant set to ensure safety in the presence of changes to the environment, and a cost-to-go function to generate an intelligent trajectory around known obstacles. The key advantage of this RSBK algorithm is that it enables the use of much shorter planning horizons while still preserving the robust feasibility guarantees of previously proposed approaches. The second contribution of this paper is a distributed version of the RSBK algorithm, which is more suitable for real-time execution. In the distributed RSBK (DRSBK) algorithm, each vehicle only optimizes for its own decisions by solving a subproblem of reduced size, which results in shorter computation times. Furthermore, the algorithm retains the robust feasibility guarantees of the centralized approach while requiring that each agent only have local knowledge of the environment and neighbor vehicles' plans. This new approach also facilitates the use of a significantly more general implementation architecture for the distributed trajectory optimization, which further decreases the delay due to computation time.

Robust Multiple Frequency Trajectory Tracking Control of Piezoelectrically Driven Micro/Nanopositioning Systems

Abstract: A novel modeling and control methodology is proposed in this paper for real-time compensation of nonlinearities along with precision trajectory control of piezoelectric actuators in various range of frequency operation. By integrating a modified Prandtl-Ishlinskii hysteresis operator with a second-order linear dynamics, a nonlinear dynamic model and an inverse feedforward controller are developed and experimentally validated for a piezoelectrically driven nanopositioning stage. This modeling and control framework, however, lacks the accuracy due to the hysteresis model limitation, parametric uncertainties, and ever present unmodeled dynamics. Utilizing the sliding mode control strategy coupled with a perturbation estimation technique, a robust controller is then proposed for trajectory tracking of the actuator displacement. The controller gains are adjusted based on an intelligent comparison of the dynamic model and the control law. Eventually, the performance of the proposed controller is verified for the nanopositioning stage which is equipped with a high resolution capacitive position sensor. Experimental results demonstrate that the controller is capable of precisely tracking triangular and multiple frequency sinusoidal trajectories, which are common practices in many scanning probe microscopy systems.

Decentralized Control of Web Processing Lines

Abstract: The focus of this research is on modeling and design of a decentralized controller for web processing lines. First, an accurate dynamic model is developed for the unwind (rewind) roll in a web processing line by explicitly taking into account the time-varying nature of the roll inertia and radius. The unwind roll in a web processing line releases unfinished web to the process section; the rewind roll accumulates the finished web. Second, a strategy for computing the equilibrium inputs and reference velocities for each driven roll/roller is given; this strategy is based on dividing the web processing line into tension zones and using the reference web tension of each zone and the reference velocity of the master speed roller, which sets the desired web transport speed for the process line. Based on the new model developed, a decentralized controller is proposed. Variations in web tension and transport velocity in each tension zone are shown to exponentially converge to zero. A large experimental web platform, which mimics most of the features of an industrial process line, is used for experimentation. Extensive comparative experiments were conducted with the proposed decentralized controller and an often used decentralized industrial proportional-integral (PI) controller. A representative sample of the experimental results is shown and discussed

Gain Scheduled $H_{\infty}$ Control for Air Path Systems of Diesel Engines Using LPV Techniques

Abstract: This paper addresses the modeling and control of the air path system of diesel engines. The underlying issues are critical for the control of the transient exhaust gas fraction pumped into the cylinders, which is known to be a dominant factor to reduce the nitrogen oxides (NO x) emissions. In this paper, we propose a new approach, based on a data-based grey-box linear parameter varying (LPV) model as well as on the gain scheduled Hinfin technique for the controller design. The modeling step is shown to lead naturally to a so-called quasi-LPV structure, which also delivers the scheduling variables to be accounted for. Using this information, gain scheduled Hinfin techniques allow to design a controller which enforces a much better tracking performance than the standard production electronic control unit, while not requiring any calibration work. The performance of the proposed approach is demonstrated by experimental results

Hardware Synthesis of Explicit Model Predictive Controllers

Abstract: The general solution to constrained linear and piecewise linear model predictive control (MPC) has recently been explicitly characterized in terms of piecewise-linear (PWL) state feedback control. This means that a PWL controller can be precomputed using parametric programming, and the exact explicit MPC implementation amounts to the evaluation of a PWL function in the control unit. It has recently been shown that PWL function evaluation can be accelerated by searching a binary tree data structure, leading to highly efficient, accurate, and verifiable software implementation in low-cost embedded control units. In this work, we report hardware synthesis results for this type of PWL control, and show that explicit MPC solutions can be implemented in an application specific integrated circuit (ASIC) with about 20 000 gates, leading to computation times in the microsecond scale. This opens the way for the use of highly advanced control designs such as constrained MPC in small-scale industrial and consumer electronics application areas that are characterized by fast sampling or low cost, including mechatronics, microelectromechanical systems (MEMS), automotive control, power electronics, and acoustics. The main limitation of the approach is that the memory requirements increase rapidly with the problem dimensions

Cascaded Kalman Filters for Accurate Estimation of Multiple Biases, Dead-Reckoning Navigation, and Full State Feedback Control of Ground Vehicles

Abstract: This paper develops a cascaded estimation algorithm for estimating all of the biases and states for full state feedback and dead reckoning of a farm tractor through short global positioning system (GPS) outages First, a conventional (one stage) estimation scheme is presented The single state estimation scheme is shown to have degraded performance in bias state estimation and dead-reckoning due to vehicle model errors However, the states for position and velocity are not highly coupled to the tractor dynamic states, allowing for separation of the estimators Therefore, the state estimation algorithms are divided into two cascaded estimators in order to prevent the errors in the vehicle model from corrupting the navigation states A dead reckoning (or navigation) estimator estimates all of the inertial sensor biases while GPS is available When GPS is not available, the dead reckoning estimator integrates rate measurements to provide position and heading estimates in order to maintain continuous control of the vehicle through these GPS outages A second estimator is then used to estimate the additional states needed for full state feedback control algorithms Bias estimates from the dead reckoning estimator are used to correct the sensor measurement used in the second estimator An extended kalman filter (EKF) is utilized for each of the estimators Results are given, showing that the cascaded estimation technique provides better estimation of the vehicle states over a conventional estimation scheme, especially during a GPS outage Results are also given which verify the ability of the estimation algorithm to estimate all of the system biases and provide continuous control of the tractor through a short GPS outage

Control of Integrated Powertrain With Electronic Throttle and Automatic Transmission

Abstract: A process to design the control strategy for a vehicle with electronic throttle control (ETC) and automatic transmission is proposed in this paper. The driver's accelerator pedal position is interpreted as a power request, which is to be satisfied by coordinating the transmission gear shift and the throttle opening in an optimal fashion. The dynamic programming (DP) technique is used to obtain the optimal gear shift and throttle opening which maximizes fuel economy while satisfying the power demand. The optimal results at different power levels are then combined to form a gear map and a throttle map which governs the operation of the integrated powertrain. A control architecture concept is presented where the relationship between the accelerator pedal position and the power demand level can be adjusted according to the preference of the vehicle performance target. Simulation, vehicle test, and dynamometer test results show that the proposed integrated powertrain control scheme produces power consistently and improves fuel efficiency compared with conventional powertrain control schemes

Passivity-Based Dynamic Visual Feedback Control for Three-Dimensional Target Tracking: Stability and $L_{2}$ -Gain Performance Analysis

Abstract: This paper investigates vision-based robot control based on passivity for three-dimensional (3-D) target tracking. First, using standard body-attached coordinate frames (the world frame, camera frame, and object frame), we represent the relative position and orientation between a moving target and a camera as an element of SE(3). Using this representation we derive a nonlinear observer to estimate the relative rigid body motion from the measured camera data. We then establish the relationship between the estimation error in a 3-D workspace and in the image plane. We show passivity of the dynamic visual feedback system by combining the passivity of both the visual feedback system and the manipulator dynamics which allows us to prove stability in the sense of Lyapunov for the full 3-D dynamic visual feedback system. The L2 -gain performance analysis, which deals with the disturbance attenuation problem, is then considered via dissipative systems theory. Finally, experimental results are presented to verify the stability and L2-gain performance of the dynamic visual feedback system

Actuator Failure Identification and Compensation Through Sliding Modes

Abstract: The actuator failure compensation problem is addressed in this brief. It is considered an uncertain linear plant, which is supposed to undergo unknown failures causing the plant input components to be stuck at some uncertain but bounded time functions. A sliding-mode-based control policy is presented, guaranteeing the detection of the fault and the identification of the failed component by means of a suitable test input. Once the failed component has been identified, the control law is reconfigured, redistributing the control activity among the controllers still working. The proposed controller has been tested by simulation on a benchmark problem

Cooperative Tracking Using Vision Measurements on SeaScan UAVs

Abstract: A cooperative tracking approach for uninhabited aerial vehicles (UAVs) with camera-based sensors is developed and verified with flight data. The approach utilizes a square root sigma point information filter, which takes important properties for numerical accuracy (square root), tracking accuracy (sigma points), and fusion ability (information). Important augmentations to the filter are also developed for delayed data, by estimating the correlated processes, and moving targets, by using multiple models in a square root interacting multiple model formulation. The final form of the algorithm is general and scales well to any tracking problem with multiple, moving sensors. Flight data using the SeaScan UAV is used to verify the algorithms for stationary and moving targets. Cooperative tracking results are evaluated using multiple test flights, showing excellent results.

High-Gain Estimator and Fault-Tolerant Design With Application to a Gas Turbine Dynamic System

Abstract: For dynamic systems with actuator faults, sensor faults, input disturbances, and measurement noises, a novel high-gain estimation technique is presented in this paper to estimate system states, actuator faults, and sensor faults simultaneously. The key idea is to represent faults as auxiliary system states so that a descriptor system representation can be formulated. By using the estimated state and fault signals, a fault-tolerant controller design approach is developed, where the closed-loop dynamic system can still be guaranteed to operate normally when actuator and sensor faults occur. It has been shown that in the proposed design framework, the actuator fault, sensor fault, input disturbance, and measurement noise can appear simultaneously and can be allowed to be in different bounded forms. Finally, the proposed algorithm is applied to the simulation study of a three-shaft gas turbine system and desired results have been obtained.

Flatness-Based Vehicle Steering Control Strategy With SDRE Feedback Gains Tuned Via a Sensitivity Approach

Abstract: This paper presents a feedback steering control strategy for a vehicle in an automatic driving context. Two main contributions in terms of control are highlighted. On the one hand, the automatic reference trajectories generation from geometric path constraints (obstacles). Thanks to the flatness property of the considered model, the longitudinal velocity will be controlled around a quasi-constant value while lateral and yaw dynamics targets will allow to avoid obstacles. On the other hand, a sensitivity-based methodology will be presented to choose the best possible gains parameterization in a state Riccati dependent equation (SDRE) feedback controller. Both direct and adjoint sensitivity methods are used, together with a dynamic inversion of the system, in order to optimize the performances of the controller. Obstacle avoiding simulation results will be validated and compared with other nonlinear optimal feedback controllers, from a realistic industrial simulator environment for vehicle dynamics

A Study on LQG/LTR Control for Damping Inter-Area Oscillations in Power Systems

Abstract: This brief presents results on a robust linear quadratic Gaussian (LQG) damping control scheme for improving the inter-area mode oscillations of power systems. A technique is also proposed to guarantee minimum-phase/well-damped transmission zeros by appropriately "squaring" the design plant, for the purposes of efficient robust recovery. A 7th-order multiple-input single-output (MISO) centralized controller is designed for a 16-machine, 5-area power system (138th order) reinforced with a thyristor-controlled series capacitor (TCSC) to improve the damping of the critical inter-area modes by employing appropriate global signal measurements. Loop transfer recovery (LTR) is then applied to reinforce controller robustness in the case of faults and unknown disturbances. The performance of the designed system is assessed in the frequency domain and via appropriate time-domain simulations based upon the nonlinear model under a variety of scenaria

Optimal Scheduling and Control of Heavy Haul Trains Equipped With Electronically Controlled Pneumatic Braking Systems

Abstract: This brief compares the performances of different operation strategies based on optimal scheduling and heuristic scheduling for heavy haul trains equipped with electronically controlled pneumatic braking systems. Train scheduling here refers to an open-loop control design that brings the train to a desired (steady-state) motion trajectory. A (closed-loop) cruise control is used to maintain a steady-state motion of a train. In train handling, energy consumption, speed tracking, and in-train force are concerns for transportation corporations. The last is particularly important for safe train running. An optimal train scheduling as well as an optimal cruise control can take these factors into consideration. A speed profile is assumed first. The objective of the study is to find optimal driving methodologies for an implementation of a desired speed profile with energy consumption and in-train forces considered. Simulation results show that optimal scheduling can improve the performance of the closed-loop controller, and that the 2-2 strategy, the electronically controlled pneumatic/independent distributed power mode, is the best of all strategies.

Backlash Estimation With Application to Automotive Powertrains

Abstract: In automotive powertrains, backlash imposes well-known limitations on the quality of control and, hence, on vehicle driveability. High-performance controllers for backlash compensation require high-quality measurements of the current state of the powertrain. Information about the size of the backlash is also needed. In this paper, nonlinear estimators for backlash size and state are developed, using the Kalman filtering theory. A linear estimator for fast and accurate estimation of the angular position of a wheel and the engine is also described. It utilizes standard engine speed sensors and the antilock brake system speed sensors and event-based sampling at each pulse from these sensors. The estimators are validated through experiments on a real vehicle and the results show that the estimates are of high quality, and hence, useful for improving backlash compensation functions in the powertrain control system

Speed Sensorless Control of Induction Motors Based on a Reduced-Order Adaptive Observer

Abstract: A novel speed sensorless indirect field-oriented control for the full-order model of the induction motor is presented. It provides local exponential tracking of smooth speed and flux amplitude reference signals together with local exponential field orientation, on the basis of stator current measurements only and under assumption of unknown constant load torque. Speed estimation is performed through a reduced-order adaptive observer based on the torque current dynamics, while no flux estimate is required for both observation and control purposes. The absence of the flux model in the proposed algorithm allows for simple and effective time-scale separation between the speed-flux tracking error dynamics (slow subsystem) and the estimation error dynamics (fast subsystem). This property is exploited to obtain a high performance sensorless controller, with features similar to those of standard field-oriented induction motor drives. Moreover, time-scale separation and physically-based decomposition into speed and flux subsystems allow for a simple and constructive tuning procedure. The theoretical analysis based on the singular perturbation method enlightens that a persistency of excitation condition is necessary for the asymptotic stability. From a practical viewpoint, it is related to the well-known observability and instability issues due to a lack of back-emf signal at zero-frequency excitation. A flux reference selection strategy has been developed to guarantee Persistency of excitation in every operating condition. Extensive simulation and experimental tests confirm the effectiveness of the proposed approach.