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In Memoriam

J. Dyn. Sys., Meas., Control. 2017;140(3):030101-030101-2. doi:10.1115/1.4038266.
Commentary by Dr. Valentin Fuster

Editorial

J. Dyn. Sys., Meas., Control. 2017;140(3):030201-030201-1. doi:10.1115/1.4038256.
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Nearly 60 years ago, Professor Kalman presented his “A New Approach to Linear Filtering and Prediction Problems” at an American Society of Mechanical Engineering conference, and he subsequently published this paper in the ASME Journal of Basic Engineering. This Special Section issue of the ASME Journal of Dynamic Systems, Measurement, and Control is published in his honor and for his work that changed the world. I wish to thank Professor Tarunraj Singh of the University of Buffalo for diligently leading this effort. He was ably supported in this endeavor by Professor Prashant Mehta of the University of Illinois at Urbana-Champaign and his colleague at the University of Buffalo, Professor Puneet Singla.

Commentary by Dr. Valentin Fuster

SPECIAL SECTION PAPERS

J. Dyn. Sys., Meas., Control. 2017;140(3):030901-030901-8. doi:10.1115/1.4037777.

In this paper, a new robust Kalman filter is proposed for discrete-time time-varying linear stochastic systems. The system under consideration is subject to stochastic and norm-bounded uncertainties in all matrices of the system model. In the proposed approach, the filter is first achieved by solving a stochastic min–max optimization problem. Next, we find an upper bound on the estimation error covariance, and then, by using a linear matrix inequality (LMI) optimization problem, unknown parameters of the filter are determined such that the obtained upper bound is minimized. Finally, two numerical examples are given to demonstrate the effectiveness and performance of the proposed filtering approach compared to the existing robust filters.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030902-030902-9. doi:10.1115/1.4037778.

This paper describes an algorithm that identifies a state-space model and an associated steady-state Kalman filter gain from noise-corrupted input–output data. The model structure involves two Kalman filters where a second Kalman filter accounts for the error in the estimated residual of the first Kalman filter. Both Kalman filter gains and the system state-space model are identified simultaneously. Knowledge of the noise covariances is not required.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030903-030903-11. doi:10.1115/1.4037779.

This paper presents a novel distributed Bayesian filtering (DBF) method using measurement dissemination (MD) for multiple unmanned ground vehicles (UGVs) with dynamically changing interaction topologies. Different from statistics dissemination (SD)-based algorithms that transmit posterior distributions or likelihood functions, this method relies on a full-in and full-out (FIFO) transmission protocol, which significantly reduces the transmission burden between each pair of UGVs. Each UGV only sends a communication buffer (CB) and a track list (TL) to its neighbors, in which the former contains a history of sensor measurements from all UGVs, and the latter is used to trim the redundant measurements in the CB to reduce communication overhead. It is proved that by using FIFO, each UGV can disseminate its measurements over the whole network within a finite time, and the FIFO-based DBF is able to achieve consistent estimation of the environment state. The effectiveness of this method is validated by comparing with the consensus-based distributed filter (CbDF) and the centralized filter (CF) in a multitarget tracking problem.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030904-030904-11. doi:10.1115/1.4037780.

This paper is concerned with the filtering problem in continuous time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman–Bucy filter, which provides an exact solution for the linear Gaussian problem; (ii) the ensemble Kalman–Bucy filter (EnKBF), which is an approximate filter and represents an extension of the Kalman–Bucy filter to nonlinear problems; and (iii) the feedback particle filter (FPF), which represents an extension of the EnKBF and furthermore provides for a consistent solution in the general nonlinear, non-Gaussian case. The common feature of the three algorithms is the gain times error formula to implement the update step (to account for conditioning due to the observations) in the filter. In contrast to the commonly used sequential Monte Carlo methods, the EnKBF and FPF avoid the resampling of the particles in the importance sampling update step. Moreover, the feedback control structure provides for error correction potentially leading to smaller simulation variance and improved stability properties. The paper also discusses the issue of nonuniqueness of the filter update formula and formulates a novel approximation algorithm based on ideas from optimal transport and coupling of measures. Performance of this and other algorithms is illustrated for a numerical example.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030905-030905-14. doi:10.1115/1.4037781.

This paper is concerned with the problem of tracking single or multiple targets with multiple nontarget-specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization—to the nonlinear non-Gaussian case—of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking (MTT) applications.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030906-030906-9. doi:10.1115/1.4037782.

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030907-030907-22. doi:10.1115/1.4037783.

This paper presents a computationally efficient approach to evaluate multidimensional expectation integrals. Specifically, certain nonproduct cubature points are constructed that exploit the symmetric structure of the Gaussian and uniform density functions. The proposed cubature points can be used as an efficient alternative to the Gauss–Hermite (GH) and Gauss–Legendre quadrature rules, but with significantly fewer number of points while maintaining the same order of accuracy when integrating polynomial functions in a multidimensional space. The advantage of the newly developed points is made evident through few benchmark problems in uncertainty propagation, nonlinear filtering, and control applications.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):030908-030908-16. doi:10.1115/1.4037784.

Image feature-based localization and mapping applications useful in field robotics are considered in this paper. Exploiting the continuity of image features and building upon the tracking algorithms that use point correspondences to provide an instantaneous localization solution, an extended Kalman filtering (EKF) approach is formulated for estimation of the rigid body motion of the camera coordinates with respect to the world coordinate system. Recent results by the authors in quantifying uncertainties associated with the feature tracking methods form the basis for deriving scene-dependent measurement error statistics that drive the optimal estimation approach. It is shown that the use of certain relative motion models between a static scene and the moving target can be recast as a recursive least squares problem and admits an efficient solution to the relative motion estimation problem that is amenable to real-time implementations on board mobile computing platforms with computational constraints. The utility of the estimation approaches developed in the paper is demonstrated using stereoscopic terrain mapping experiments carried out using mobile robots. The map uncertainties estimated by the filter are utilized to establish the registration of the local maps into the global coordinate system.

Commentary by Dr. Valentin Fuster

Research Papers

J. Dyn. Sys., Meas., Control. 2017;140(3):031001-031001-15. doi:10.1115/1.4037736.

This paper addresses the problem of autonomous trajectory tracking control for a quadrotor in a global positioning system (GPS)-denied environment using only onboard sensing. To achieve that goal, it requires accurate estimation of quadrotor states followed by proper control actions. For the position estimation in a GPS-denied environment, an open source high speed optical flow sensor PX4FLOW is adopted. As for the quadrotor control, there are several challenges due to its highly nonlinear system dynamics, such as underactuation, coupling, model uncertainties, and external disturbances. To deal with those challenges, the cascaded inner–outer uncertainty and disturbance estimator (UDE)-based robust control scheme has been developed and applied to the attitude and position control of a quadrotor. Extensive real flight experiments, including attitude stabilization, hover, disturbance rejection, trajectory tracking, and comparison with the proportional–integral–derivative (PID) controller are carried out to demonstrate the effectiveness of the developed UDE-based controllers.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):031002-031002-10. doi:10.1115/1.4037733.

In many haptic applications, producing a sharp feeling of impact is crucial for high-fidelity force feedback rendering of virtual objects (VOs). Although suitable for rendering collision-rich haptic interactions, impulse-based methods are rarely used in a pure form. Instead, they are combined with penalty-based elements in different forms such as virtual couplings (VCs) and hybridization. In this paper, we first propose the direct impulse-based paradigm for rendering haptic contacts using a new sampled-data interpretation of the impact problem. Then, we cast this interpretation into a systematic framework entitled the generalized contact controller (GCC). This enables us to implement different contact rendering methods as controllers and to improve them by appropriating a wide array of analysis and design tools developed in the control field. We specifically show how to apply position and velocity corrections to the purely impulse-based contact controller for enhancing its energy and sustained contact characteristics, and how to add an anti-windup compensator (AWC) for meeting actuation limits. These propositions are validated via simulation and experiments, as well as via human perception studies. Results show the promising aspects of the proposed impulse-based methods for generating a sharper unfiltered feeling of rigid-body contacts even at low sampling rates.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):031003-031003-12. doi:10.1115/1.4037653.

Parametric optimization problems are considered for serial robots with regenerative drive mechanisms. A subset of the robot joints are conventional, in the sense that external power is used for actuation. Other joints are energetically self-contained passive systems that use (ultra)capacitors for energy storage. Two different electrical interconnections are considered for the regenerative drives, a distributed and a star configuration. The latter allows for direct electric energy redistribution among joints, a novel idea shown in this paper to enable higher energy utilization efficiencies. Closed-form expressions are found for the optimal manipulator parameters (link masses, link lengths, etc.) and drive mechanism parameters (gear ratios, etc.) that maximize regenerative energy storage between any two times, given motion trajectories. A semi-active virtual control strategy previously proposed is used to achieve asymptotic tracking of trajectories. Optimal solutions are shown to be global and unique. In addition, closed-form expressions are provided for the maximum attainable energy. This theoretical maximum places limits on the amount of energy that can be recovered. The results also shed light on the comparative advantages of the star and distributed configurations. A numerical example with a double inverted pendulum and cart system is provided to demonstrate the results.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;140(3):031004-031004-8. doi:10.1115/1.4037527.

This paper studies a systematic linear matrix inequality (LMI) approach for controller design of nonlinear chaotic power systems. The presented method is based on a Takagi–Sugeno (TS) fuzzy model, a double-fuzzy-summation nonparallel distributed compensation (non-PDC) controller, and a double-fuzzy-summation nonquadratic Lyapunov function (NQLF). Since time derivatives of fuzzy membership functions (MFs) appear in the NQLF-based controller design conditions, local controller design criteria is considered, and sufficient conditions are formulated in terms of LMIs. Compared with the existing works in hand, the proposed LMI conditions provide less conservative results due to the special structure of the NQLF and the non-PDC controller in which two fuzzy summations are employed. To evaluate the effectiveness of the presented approach, two practical benchmark power systems, which exhibit chaotic behavior, are considered. Simulation results and hardware-in-the-loop illustrate the advantages of the proposed method compared with the recently published works.

Commentary by Dr. Valentin Fuster

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