Research Papers

J. Dyn. Sys., Meas., Control. 2017;139(7):071001-071001-8. doi:10.1115/1.4035459.

This paper examines the shaping of a drug's delivery—in this case, nicotine—to maximize its efficacy. Previous research: (i) furnishes a pharmacokinetic–pharmacodynamic (PKPD) model of this drug's metabolism; (ii) shows that the drug-delivery problem is proper, meaning that its optimal solution is periodic; (iii) shows that the underlying PKPD model is differentially flat; and (iv) exploits differential flatness to solve the problem by optimizing the coefficients of a truncated Fourier expansion of the flat output trajectory. In contrast, the work in this article provides insight into the structure of the theoretical solution to this optimal periodic control (OPC) problem. First, we argue for the existence of a bijection between feasible periodic input and state trajectories of the problem. Second, we exploit Pontryagin's maximum principle to show that the optimal periodic solution has a bang–singular–bang structure. Building on these insights, this article proposes two different numerical methods for solving this OPC problem. One method uses nonlinear programming (NLP) to optimize the states at which the optimal solution transitions between the different solution arcs. The second method approximates the control input trajectory as piecewise constant and optimizes the discrete values of the input sequence. The paper concludes by discussing the computational costs of these two algorithms as well as the importance of the associated insights into the structure of the optimal solution trajectory.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071002-071002-8. doi:10.1115/1.4035452.

Precise, robust, and consistent localization is an important subject in many areas of science such as vision-based control, path planning, and simultaneous localization and mapping (SLAM). To estimate the pose of a platform, sensors such as inertial measurement units (IMUs), global positioning system (GPS), and cameras are commonly employed. Each of these sensors has their strengths and weaknesses. Sensor fusion is a known approach that combines the data measured by different sensors to achieve a more accurate or complete pose estimation and to cope with sensor outages. In this paper, a three-dimensional (3D) pose estimation algorithm is presented for a unmanned aerial vehicle (UAV) in an unknown GPS-denied environment. A UAV can be fully localized by three position coordinates and three orientation angles. The proposed algorithm fuses the data from an IMU, a camera, and a two-dimensional (2D) light detection and ranging (LiDAR) using extended Kalman filter (EKF) to achieve accurate localization. Among the employed sensors, LiDAR has not received proper attention in the past; mostly because a two-dimensional (2D) LiDAR can only provide pose estimation in its scanning plane, and thus, it cannot obtain a full pose estimation in a 3D environment. A novel method is introduced in this paper that employs a 2D LiDAR to improve the full 3D pose estimation accuracy acquired from an IMU and a camera, and it is shown that this method can significantly improve the precision of the localization algorithm. The proposed approach is evaluated and justified by simulation and real world experiments.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071003-071003-13. doi:10.1115/1.4035742.

Developing and parameterizing models that accurately predict the battery voltage and temperature in a vehicle battery pack are challenging due to the complex geometries of the airflow that influence the convective heat transfer. This paper addresses the difficulty in parameterizing low-order models which rely on coupling with finite element simulations. First, we propose a methodology to couple the parameterization of an equivalent circuit model (ECM) for both the electrical and thermal battery behavior with a finite element model (FEM) for the parameterization of the convective cooling of the airflow. In air-cooled battery packs with complex geometries and cooling channels, an FEM can provide the physics basis for the parameterization of the ECM that might have different convective coefficients between the cells depending on the airflow patterns. The second major contribution of this work includes validation of the ECM against the data collected from a three-cell fixture that emulates a segment of the pack with relevant cooling conditions for a hybrid vehicle. The validation is performed using an array of thin film temperature sensors covering the surface of the cell. Experiments with pulsing currents and drive cycles are used for validation over a wide range of operating conditions (ambient temperature, state of charge, current amplitude, and pulse width).

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071004-071004-7. doi:10.1115/1.4035607.

New generation of torque converter automatic transmissions (AT) includes a large number of gears for improved fuel economy and vehicle performance, which leads to exponentially increasing number of shift types and shift events. In order to facilitate various numerical/simulation analyses of AT dynamics, shift control optimization, and control strategy design, a full-order AT model is usually reduced by eliminating state variables related to locked clutches in particular gears or shifts. The paper proposes an automated model-order reduction method for an arbitrary, user-specified clutch state, and demonstrates its application on an example of ten-speed AT. The method is based on determining the locked-clutch torque variables and their substitution into the full-order state-space model input vector, as well as finding a linear relation between the reduced-order and full-order model state-space variables.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071005-071005-11. doi:10.1115/1.4035760.

Pose estimation of human–machine interactions such as bicycling plays an important role to understand and study human motor skills. In this paper, we report the development of a human whole-body pose estimation scheme with application to rider–bicycle interactions. The pose estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider's upper- and lower-limbs and the trunk. A single feature point is collocated with each wearable gyroscope and also on the body segment link where the gyroscope is not attached. An extended Kalman filter (EKF) is designed to fuse the visual-inertial measurements to obtain the drift-free whole-body poses. The pose estimation design also incorporates a set of constraints from human anatomy and the physical rider–bicycle interactions. The performance of the estimation design is validated through ten subject riding experiments. The results illustrate that the maximum errors for all joint angle estimations by the proposed scheme are within 3 degs. The pose estimation scheme can be further extended and used in other types of physical human–machine interactions.

Topics: Bicycles , Design , Errors
Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071006-071006-10. doi:10.1115/1.4035741.

Time-varying unknown wind disturbances influence significantly the dynamics of wind turbines. In this research, we formulate a disturbance observer (DOB) structure that is added to a proportional-integral-derivative (PID) feedback controller, aiming at asymptotically rejecting disturbances to wind turbines at above-rated wind speeds. Specifically, our objective is to maintain a constant output power and achieve better generator speed regulation when a wind turbine is operated under time-varying and turbulent wind conditions. The fundamental idea of DOB control is to conduct internal model-based observation and cancelation of disturbances directly using an inner feedback control loop. While the outer-loop PID controller provides the basic capability of suppressing disturbance effects with guaranteed stability, the inner-loop disturbance observer is designed to yield further disturbance rejection in the low frequency region. The DOB controller can be built as an on–off loop, that is, independent of the original control loop, which makes it easy to be implemented and validated in existing wind turbines. The proposed algorithm is applied to both linearized and nonlinear National Renewable Energy Laboratory (NREL) offshore 5-MW baseline wind turbine models. In order to deal with the mismatch between the linearized model and the nonlinear turbine, an extra compensator is proposed to enhance the robustness of augmented controller. The application of the augmented DOB pitch controller demonstrates enhanced power and speed regulations in the above-rated region for both linearized and nonlinear plant models.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071007-071007-10. doi:10.1115/1.4035740.

This study presents the development of an embedded system for controlling a high-speed robotic manipulator. Three different types of controllers including hardware proportional derivative (PD), software PD, and single time scale visual servoing are considered in this study. Novel field programmable gate array (FPGA) technology was used for implementing the embedded system for faster execution speeds and parallelism. It is comprised of dedicated hardware and software modules for obtaining sensor feedback and control signal (CT) estimation, providing the control signal to the servovalves. A NIOS II virtual soft processor system was configured in the FPGA for implementing functions that are computationally expensive and difficult to implement in hardware. Quadrature decoding, serial peripheral interface (SPI) input and output modules, and control signal estimation in some cases was carried out using the dedicated hardware modules. The experiments show that the proposed controller performed satisfactory control of the end effector position. It performed single time scale visual servoing with control signal updates at 330 Hz to control the end effector trajectory at speeds of up to 0.8 ms−1. The FPGA technology also provided a more compact single chip implementation of the controller.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071008-071008-9. doi:10.1115/1.4035615.

This paper concerns a repetitive-control system with an input-dead-zone (IDZ) nonlinearity. First, the expression for the IDZ is decomposed into a linear term and a disturbance-like one that depends on the parameters of the dead zone. A function of the system-state error is used to approximate the combination of the disturbancelike term and an exogenous disturbance. The estimate is used to compensate for the overall effect of the IDZ and the exogenous disturbance. Next, the state-feedback gains are obtained from a linear matrix inequality that contains two tuning parameters for adjusting control performance; and the pole assignment method is employed to design the gain of a state observer. Then, two stability criteria are used to test the stability of the closed-loop system. The method is simple, employing neither an inverse model of the plant nor an adaptive control technique. It is also robust with regard to the different parameters of the IDZ, uncertainties in the plant, and the exogenous disturbance. Finally, two numerical examples demonstrate the effectiveness of this method and its advantages over others.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071009-071009-12. doi:10.1115/1.4035758.

In this paper, an adaptive fuzzy controller design methodology via multi-objective particle swarm optimization (MOPSO) based on robust stability criterion is proposed. The plant to be controlled is modeled from its input–output experimental data considering a Takagi–Sugeno (TS) fuzzy nonlinear autoregressive with exogenous input model, by using the fuzzy C-means clustering algorithm (antecedent parameters estimation) and the weighted recursive least squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital proportional-integral-derivative (PID) controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time-varying delay are presented to illustrate the efficiency and applicability of the proposed methodology.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071010-071010-8. doi:10.1115/1.4035872.

The control system of induction motors is designed to achieve dynamic stability, allowing accurate tracking of flux and speed. However, changes in electrical parameters, due to temperature rise or saturation level, can lead to undesirable errors of speed and position, eventually resulting in instability. This paper presents two modes for parametric identification of the induction motor based on the least squares method: batch estimator and recursive estimator. The objective is to update the electrical parameters during operation when the motor is driven by a vector control system. A drawback related to the batch estimator is the need for high quantity of available memory to make the process of identification robust enough. The proposed algorithm allows the batch estimator to be viewed as a single matrix problem reducing the need for processing memory. The identification procedure is based on the stator currents measurement and stator fluxes estimation. Basically, both modes of identification will be analyzed. Experimental results are presented to demonstrate the theoretical approach.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071011-071011-10. doi:10.1115/1.4035745.

The subject of this paper is a flow-adaptive measurement grid algorithm developed for one-dimensional (1D) and two-dimensional (2D) flow field surveys with pneumatic probes in turbomachinery flows. The algorithm automatically determines the distribution and the amount of measurement points needed for an approximation of the pressure distribution within a predefined accuracy. The algorithm is based on transient traverses, conducted back and forth in the circumferential direction. A correction of the dynamic response is applied by deconvolving the transient measurement data using the information embedded in both transient measurements. In consequence, the performance of the algorithm is largely independent of the transient traversing speed and the geometry of the pressure measuring system. Insertion and removal strategies are incorporated in order to reduce measurement points and increase robustness toward differing flow field conditions. The performance of the algorithm is demonstrated for 2D flow field surveys with a pneumatic five-hole probe in an annular cascade wind tunnel. The measurement grid points are automatically adjusted so that a consistent resolution of the flow features is achieved within the measurement domain. Furthermore, the application of the algorithm shows a significant reduction in the number of measurement points. Compared to the measurement duration based on uniform grids, the duration is reduced by at least 7%, while maintaining a high accuracy of the measurement. The purpose of this paper is to demonstrate the performance of measurement grids adapted to local flow field conditions. Consequently, valuable measurement time can be saved without a loss in quality of the data obtained.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071012-071012-6. doi:10.1115/1.4035612.

Finite-time consensus has attracted significant research interest due to its wide applications in multiagent systems. Various results have been developed to enable multiagent systems to complete desired tasks in finite-time. However, most existing results in the literature can only ensure finite-time consensus without considering temporal constraints, where the time used to achieve consensus cannot be preset arbitrarily and is generally determined by the system initial conditions, prohibiting its application in time-sensitive tasks. Motivated to achieve consensus within a desired time frame, user-specified finite-time consensus is developed in the present work for a multiagent system to ensure consensus at a prespecified time instant. The interaction among agents (e.g., communication and information exchange) is modeled as a time-varying graph, where each edge is associated with a time-varying weight representing the time-varying interaction between neighboring agents. Consensus over such time-varying graph is then proven based on a time transformation and is guaranteed to be completed within a prespecified time frame. To demonstrate the developed framework, finite-time rendezvous of a multiagent system is considered as an example application, where agents with limited communication capabilities are desired to meet at a common location at a preset time instant with constraints on preserving global network connectivity. A numerical simulation is provided to demonstrate the efficiency of the developed result.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):071013-071013-15. doi:10.1115/1.4035815.

In this work, a novel design of a portable leg rehabilitation system (PLRS) is presented. The main purpose of this paper is to provide a portable system, which allows patients with lower-limb disabilities to perform leg and foot rehabilitation exercises anywhere without any embarrassment compared to other devices that lack the portability feature. The model of the system is identified by inverse kinematics and dynamics analysis. In kinematics analysis, the pattern of motion of both leg and foot holders for different modes of operation has been investigated. The system is modeled by applying Lagrangian dynamics approach. The mathematical model derived considers calf and foot masses and moment of inertias as important parameters. Therefore, a gait analysis study is conducted to calculate the required parameters to simulate the model. Proportional derivative (PD) controller and proportional-integral-derivative (PID) controller are applied to the model and compared. The PID controller optimized by hybrid spiral-dynamics bacteria-chemotaxis (HSDBC) algorithm provides the best response with a reasonable settling time and minimum overshot. The robustness of the HSDBC–PID controller is tested by applying disturbance force with various amplitudes. A setup is built for the system experimental validation where the system mathematical model is compare with the estimated model using system identification (SI) toolbox. A significant difference is observed between both models when applying the obtained HSDBC–PID controller for the mathematical model. The results of this experiment are used to update the controller parameters of the HSDBC-optimized PID.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Dyn. Sys., Meas., Control. 2017;139(7):074501-074501-8. doi:10.1115/1.4035094.

In this paper, we study the design and analysis of adaptive control systems over wireless networks using event-triggering control theory. The proposed event-triggered adaptive control methodology schedules the data exchange dependent upon errors exceeding user-defined thresholds to reduce wireless network utilization and guarantees system stability and command following performance in the presence of system uncertainties. Specifically, we analyze stability and boundedness of the overall closed-loop dynamical system, characterize the effect of user-defined thresholds and adaptive controller design parameters to the system performance, and discuss conditions to make the resulting command following performance error sufficiently small. An illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):074502-074502-7. doi:10.1115/1.4035871.

A dynamic neural network (DNN) observer-based output feedback controller for uncertain nonlinear systems with bounded disturbances is developed. The DNN-based observer works in conjunction with a dynamic filter for state estimation using only output measurements during online operation. A sliding mode term is included in the DNN structure to robustly account for exogenous disturbances and reconstruction errors. Weight update laws for the DNN, based on estimation errors, tracking errors, and the filter output are developed, which guarantee asymptotic regulation of the state estimation error. A combination of a DNN feedforward term, along with the estimated state feedback and sliding mode terms yield an asymptotic tracking result. The developed output feedback (OFB) method yields asymptotic tracking and asymptotic estimation of unmeasurable states for a class of uncertain nonlinear systems with bounded disturbances. A two-link robot manipulator is used to investigate the performance of the proposed control approach.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(7):074503-074503-5. doi:10.1115/1.4035744.

Super-twisting algorithm, a second-order sliding mode control method, is studied for hydropower plant frequency control. Two versions of this algorithm are introduced in this paper. Simulation results from both of these second-order methods and regular sliding mode control are compared on the basis of system responses and control efforts. It is shown that the second-order sliding mode controller is able to reduce chattering effects associated with the regular sliding mode control and preserve the robustness of the regular sliding mode control as well.

Commentary by Dr. Valentin Fuster

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