Research Papers

J. Dyn. Sys., Meas., Control. 2017;139(8):081001-081001-13. doi:10.1115/1.4035743.

This article optimizes the allocation of external current demand among parallel strings of cells in a lithium-ion battery pack to improve Fisher identifiability for these strings. The article is motivated by the fact that better battery parameter identifiability can enable the more accurate detection of unhealthy outlier cells. This is critical for pack diagnostics. The literature shows that it is possible to optimize the cycling of a single battery cell for identifiability, thereby improving the speed and accuracy with which its health-related parameters can be estimated. However, the applicability of this idea to online pack management is limited by the fact that overall pack current is typically dictated by the user, and difficult to optimize. We circumvent this challenge by optimizing the internal allocation of total pack current for identifiability. We perform this optimization for two pack designs: one that exploits switching control to allocate current passively among parallel strings of cells, and one that incorporates bidirectional DC–DC conversion for active charge shuttling among the strings. A novel evolutionary algorithm optimizes identifiability for each pack design, and a local outlier probability (LoOP) algorithm is then used for diagnostics. Simulation studies show significant improvements in diagnostic accuracy for an automotive protocol.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081002-081002-11. doi:10.1115/1.4035533.

In the present study, variational iteration and Adomian decomposition methods (ADMs) are applied for solving a class of fractional optimal control problems (FOCPs). Also, a comparative study between these two methods is presented. The fractional derivative (FD) in these problems is in the Caputo sense. To solve the problem, first the necessary optimality conditions of FOCP are achieved for a linear tracking fractional optimal control problem, and then, these two methods are used to solve the resulting fractional differential equations (FDEs). It is shown that the modified Adomian decomposition method and variational iteration method (VIM) use the same iterative formula for solving linear and nonlinear FOCPs. The convergence of the modified Adomian decomposition method is analytically studied and to illustrate the validity and applicability of the methods, some examples are provided.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081003-081003-7. doi:10.1115/1.4035611.

The electrooculography (EOG) signal is considered most suitable for drowsiness detection. Besides its simplicity and low cost, EOG signals are not affected by environmental factors such as light intensity and driver movement. However, existing EOG-based drowsiness detection techniques employ arbitrarily chosen features for classifier training, leading to results that are less robust against changes in the measurement method, noise level, and individual subject variability. In this study, we propose a system dynamics-based approach to drowsiness detection. The EOG signal is treated as a neurophysiological response of the oculomotor system. Each blink action is considered as a result of a series of neuron firing impulses entering the system. Blink signatures are thus extracted to identify the system transfer function, from which system poles are computed to characterize the drowsiness state of the subject. It was found that the location of system poles on the pole–zero map for blink signatures from an alert state was distinctly different from those from a drowsy state. A simple criterion was subsequently developed for drowsiness detection by counting the ratio of real and complex poles of the system over any given period of time. The proposed methodology is a systematic approach and does not require extensive classifier training. It is robust against variations in the subject condition, sensor placement, noise level, and blink rate.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081004-081004-10. doi:10.1115/1.4035870.

A method of controller restructuring is introduced for improved closed-loop control of nonlinear plants. In this method, an initial controller, potentially the linear controller designed according to the linearized model of the plant, is expanded into several candidate nonlinear control structures that are subsequently shaped to achieve a desired closed-loop response. The salient feature of the proposed method is a metric for quantifying structural perturbations to the controllers, which it uses to scale the structural Jacobian for improving its condition number. This improved Jacobian underlies shaping of candidate controllers through gradient-based search. Results obtained from three case studies indicate the success of the proposed restructuring method in finding nonlinear controllers that improve not only the closed-loop response of the nonlinear plant but also its robustness to modeling uncertainty.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081005-081005-14. doi:10.1115/1.4035901.

This article presents a one-dimensional numerical model for vertical upward multiphase flow dynamics in a pipeline. A quasi-steady-state condition is used for the gas phase as well as liquid and gas momentum equations. A second-order polynomial for homogeneous flows and a sixth-order polynomial for separated flows are derived to determine the two-phase flow dynamics, assuming that the gas flow mass is conserved. The polynomials are formulated based on the homogenous and separate flows' momentum equation and the homogeneous flows' rise velocity equation and their zeros are the flow actual liquid holdup. The modeling polynomial approach enables the study of the polynomial liquid holdup zeros existence and uniqueness and as a result the design of a stable numerical model in terms of its outputs. The one-dimensional solution of the flow for the case of slug and bubble flow is proven to exist and to be unique when the ratio of the pipe node length to the time step is inferior to a specific limit. For the annular flow case, constraints on the inlet gas superficial velocity and liquid to gas density ratio show that the existence is ensured while the uniqueness may be violated. Simulations of inlet pressure under transient condition are provided to illustrate the numerical model predictions. The model steady-state results are validated against experimental measurements and previously developed and validated multiphase flow mechanistic model.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081006-081006-9. doi:10.1115/1.4035873.

The trajectory tracking in the flexible-joint manipulator (FJM) system becomes complicated since the flexibility of the joint of the FJM superimposes vibrations and nonminimum phase characteristics. In this paper, a distributed higher-order differential feedback controller (DHODFC) using the link and joint position measurement was developed to reduce joint vibration in step input response and to improve tracking behavior in reference trajectory tracking control. In contrast to the classical higher-order differential (HOD), the dynamics of the joint and link are considered separately in DHODFC. In order to validate the performance of the DHODFC, step input, trajectory tracking, and disturbance rejection experiments are conducted. In order to illustrate the differences between classical HOD and DHODFC, the performance of these controllers is compared based on tracking errors and energy of control signal in the tracking experiments and fundamental dynamic characteristics in the step response experiments. DHODFC produces better tracking errors with almost same control effort in the reference tracking experiments and a faster settling time, less or no overshoot, and higher robustness in the step input experiments. Dynamic behavior of DHODFC is examined in continuous and discontinues inputs. The experimental results showed that the DHODFC is successful in the elimination of the nonminimum phase dynamics, reducing overshoots in the tracking of such discontinuous input trajectories as step and square waveforms and the rapid damping of joint vibrations.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081007-081007-12. doi:10.1115/1.4035613.

The hydraulic buck converter (HBC) is a novel high-bandwidth and energy-efficient device which can adjust or control flow and pressure by a means that does not rely on throttling the flow and dissipation of power. However, the nature of a HBC can cause severe fluid-borne noise (FBN), which is the unsteady pressure or flow in the fluid-filled hydraulic circuit. This is due to the operation nature of a high-speed switching valve of the device. The FBN creates fluctuating forces on the pipes which lead to system structure-borne noise that develops air-borne noise reaching to 85 dB. Thus, there is a need for an effective method that does not impair the system performance and efficiency to reduce the FBN. This paper describes the first investigation of an active controller for FBN cancellation in a HBC based on in-series and by-pass structures. The dynamics and the noise problem of the HBC are investigated using the analytical models. A piezoelectrically actuated hydraulic valve with a fast response and high force is applied as the adaptive FBN attenuator. The performance and robustness of the designed noise controller were studied with different operating conditions of a HBC. Simulated and experimental results show that excellent noise cancellation (30 dB) was achieved. The proposed active attenuator is a very promising solution for FBN attenuation in modern digital hydraulic systems which promise high energy efficiency but suffer severe noise or vibration problems in practice.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081008-081008-11. doi:10.1115/1.4035608.

Variable displacement axial piston units are the core components of many hydrostatic and hydraulic hybrid drive trains. Therein, the fast and accurate control of the swash plate angle, utilizing the full possible dynamics of the displacement system, is essential for a good performance of the overall drive train. This paper describes the development, implementation, and the experimental validation of a control strategy for the swash plate angle based on nonlinear model predictive control (NMPC). A tailored mathematical model, which serves as the basis for the NMPC, is described in the first part of the paper. Two versions of NMPC, an indirect and a direct method, are compared with respect to their numerical complexity and their capability of handling input and state constraints. An observer strategy, which is designed to obtain the nonmeasurable states and varying parameters of the system, completes the overall control strategy. To reduce the negative influence of stick–slip friction, the concept of dithering is applied in the experimental implementation. The differences of the NMPC methods are analyzed by simulation studies and experiments. Finally, the experimental results, using an industrial electronic control unit (ECU), prove the practical feasibility and the improved control accuracy and robustness in comparison to classical (nonlinear) control strategies.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081009-081009-7. doi:10.1115/1.4035759.

The computational burden of parameter exploration of nonlinear dynamical systems can become a costly exercise. A computationally efficient lower dimensional representation of a higher dimensional dynamical system is achieved by developing a reduced order model (ROM). Proper orthogonal decomposition (POD) is usually the preferred method in projection-based nonlinear model reduction. POD seeks to find a set of projection modes that maximize the variance between the full-scale state variables and its reduced representation through a constrained optimization problem. Here, we investigate the benefits of an ROM, both qualitatively and quantitatively, by the inclusion of time derivatives of the state variables. In one formulation, time derivatives are introduced as a constraint in the optimization formulation—smooth orthogonal decomposition (SOD). In another formulation, time derivatives are concatenated with the state variables to increase the size of the state space in the optimization formulation—extended state proper orthogonal decomposition (ESPOD). The three methods (POD, SOD, and ESPOD) are compared using a periodically, periodically forced with measurement noise, and a randomly forced beam on a nonlinear foundation. For both the periodically and randomly forced cases, SOD yields a robust subspace for model reduction that is insensitive to changes in forcing amplitudes and input energy. In addition, SOD offers continual improvement as the size of the dimension of the subspace increases. In the periodically forced case where the ROM is developed with noisy data, ESPOD outperforms both SOD and POD and captures the dynamics of the desired system using a lower dimensional model.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081010-081010-11. doi:10.1115/1.4035609.

In this paper, a new computational algorithm for the numerical solution of the adjoint equations for the nonlinear optimal control problem is introduced. To this end, the main features of the optimal control theory are briefly reviewed and effectively employed to derive the adjoint equations for the active control of a mechanical system forced by external excitations. A general nonlinear formulation of the cost functional is assumed, and a feedforward (open-loop) control scheme is considered in the analytical structure of the control architecture. By doing so, the adjoint equations resulting from the optimal control theory enter into the formulation of a nonlinear differential-algebraic two-point boundary value problem, which mathematically describes the solution of the motion control problem under consideration. For the numerical solution of the problem at hand, an adjoint-based control optimization computational procedure is developed in this work to effectively and efficiently compute a nonlinear optimal control policy. A numerical example is provided in the paper to show the principal analytical aspects of the adjoint method. In particular, the feasibility and the effectiveness of the proposed adjoint-based numerical procedure are demonstrated for the reduction of the mechanical vibrations of a nonlinear two degrees-of-freedom dynamical system.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081011-081011-12. doi:10.1115/1.4035874.

A cloud-supported coverage control scheme is proposed for multi-agent, persistent surveillance missions. This approach decouples assignment from motion planning operations in a modular framework. Coverage assignments and surveillance parameters are managed on the cloud and transmitted to mobile agents via unplanned and asynchronous exchanges. These updates promote load-balancing, while also allowing effective pairing with typical path planners. Namely, when paired with a planner satisfying mild assumptions, the scheme ensures that (i) coverage regions remain connected and collectively cover the environment, (ii) regions may go uncovered only over bounded intervals, (iii) collisions (sensing overlaps) are avoided, and (iv) for time-invariant event likelihoods, a Pareto optimal configuration is produced in finite time. The scheme is illustrated in simulated missions.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081012-081012-6. doi:10.1115/1.4036080.

The paper presents the new way of identification of complex nonlinear dynamic systems. The method has been explained with the use of a dynamic structure (degenerated one) with 1.5 degrees-of-freedom and some nonlinear restitution force. The applied method allows for the assessment of the dynamic behavior of material in a wide range of dynamic loads. The equation of energy balance when oscillations are set harmonic is applicable to the solution. It is possible when the loading force is adjustable. The method has been computer verified using a system with cubic spring characteristic.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081013-081013-10. doi:10.1115/1.4036070.

A method for distributed control of nonlinear flow equations is proposed. In this method, first, Takagi–Sugeno (T–S) fuzzy model is used to substitute the nonlinear partial differential equations (PDEs) governing the system by a set of linear PDEs, such that their fuzzy composition exactly recovers the original nonlinear equations. This is done to alleviate the mode-interaction phenomenon occurring in spectral treatment of nonlinear equations. Then, each of the so-obtained linear equations is converted to a set of ordinary differential equations (ODEs) using the fast Fourier transform (FFT) technique. Thus, the combination of T–S method and FFT technique leads to a number of ODEs for each grid point. For the stabilization of the dynamics of each grid point, the use is made of the parallel distributed compensation (PDC) method. The stability of the proposed control method is proved using the second Lyapunov theorem for fuzzy systems. In order to solve the nonlinear flow equation, a combination of FFT and Runge–Kutta methodologies is implemented. Simulation studies show the performance of the proposed method, for example, the smaller settling time and overshoot and also its relatively robustness with respect to the measurement noises.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081014-081014-14. doi:10.1115/1.4036232.

This paper describes a model-based feedback control method to transition from spark ignition (SI) to homogeneous charge compression ignition (HCCI) combustion in gasoline engines. The purpose of the control structure is to improve robustness and reduce calibration complexity by incorporating feedback of the engine variables into nonlinear model-based calculations that inherently generalize across operating points. This type of structure is sought as an alternative to prior SI-HCCI transition approaches that involve open-loop calibration of input command sequences that must be scheduled by operating condition. The control architecture is designed for cam switching type SI-HCCI mode transition strategies with practical two-stage cam profile hardware, which previously have only been investigated in a purely open-loop framework. Experimental results on a prototype engine show that the control architecture is able to carry out SI-HCCI transitions across the HCCI load range at 2000 rpm engine speed while requiring variation of only one major set point and three minor set points with operating condition. These results suggest a noteworthy improvement in controller generality and ease of calibration relative to previous SI-HCCI transition approaches.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081015-081015-8. doi:10.1115/1.4036407.

A parameter adaptation method for a previously developed spark ignition (SI) to homogeneous charge compression ignition (HCCI) combustion mode transition control architecture is described. The goal of the adaptive method is to use transient SI–HCCI transition data gathered in online operation to tune the controller model parameters on a cylinder individual basis, in order to improve the accuracy of the controller's model-based calculations and account for cylinder to cylinder variability and drifts over time. The parameter adaptation is implemented on an experimental engine in an indirect adaptive control structure where the model parameters of the SI–HCCI transition controller are updated based on real-time measurements and used in subsequent model-based calculations. Comparison of SI–HCCI transition responses before and after adaptation at a single operating condition shows notable benefits from use of the adaptive method. When tested at differing operating points, the performance of the adapted controller remains overwhelmingly favorable to that of the baseline controller even when conditioned on data from only a single operating point.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081016-081016-11. doi:10.1115/1.4036228.

In the framework of wave-based method, we have examined swing motion control for double-pendulum and load-hoist models. Emphases are placed on wave scattering by the middle load mass in the double-pendulum model and on time-varying configuration in the load-hoist model. By analyzing wave transmission and reflection, trolley's motion to alleviate swing is designed by absorbing reflected wave through adjusting the velocity of trolley. Simulation and experiment are also conducted to validate the proposed control method. The results show that with the designed trolley's motion swings of load can be significantly reduced for both double-pendulum model, suspended rod model which is demonstrated a special case of double-pendulum model, and load-hoist model. Simulation results agree well with the experimental measurement. Launch velocity profiles may have important impact on motion design, especially on force necessary to displace trolley. Finally, a wave-based feedback control is also discussed to demonstrate the flexibility of method.

Topics: Stress , Waves , Pendulums , String
Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081017-081017-11. doi:10.1115/1.4036237.

This paper investigates the problem of robust stabilization for a class of discrete-time Takagi–Sugeno (TS) fuzzy systems via input random delays in control input. The main objective of this paper is to design a state feedback H controller. Linear matrix inequality (LMI) approach together with the construction of proper Lyapunov–Krasovskii functional is employed for obtaining delay dependent sufficient conditions for the existence of robust H controller. In particular, the effect of both variation range and distribution probability of the time delay is taken into account in the control input. The key feature of the proposed results in this paper is that the time‐varying delay in the control input not only dependent on the bound but also the distribution probability of the time delay. The obtained results are formulated in terms of LMIs which can be easily solved by using the standard optimization algorithms. Finally, a numerical example with simulation result is provided to illustrate the effectiveness of the obtained control law and less conservativeness of the proposed result.

Commentary by Dr. Valentin Fuster
J. Dyn. Sys., Meas., Control. 2017;139(8):081018-081018-5. doi:10.1115/1.4036229.

In this paper, delayed bidirectional associative memory (BAM) neural networks, which consist of one neuron in the X-layer and other neurons in the Y-layer, will be studied. Hopf bifurcation analysis of these systems will be discussed by proposing a general method. In fact, a general n-neuron BAM neural network model is considered, and the associated characteristic equation is studied by classification according to n. Here, n can be chosen arbitrarily. Moreover, we find an appropriate Lyapunov function that under a hypothesis, results in global stability. Numerical examples are also presented.

Commentary by Dr. Valentin Fuster

Technical Brief

J. Dyn. Sys., Meas., Control. 2017;139(8):084501-084501-6. doi:10.1115/1.4036227.

This paper addresses to demonstrate the uniform-ultimately bounded stability (uniformly-ultimately-bounded (UUB)-stability) of the proportional derivative (PD+) compensator where, the joint velocity is not available to be measured but rather it is estimated. The proposed stabilization control strategy is developed for a “n” degrees-of-freedom (DOF) robotic manipulator process, where the joint speed is not available to be measured; furthermore, the external disturbances and/or uncertain dynamics are considered in the system dynamics. To conclude the closed-loop robust stabilization, the proposed feedback strategy is based on the nonlinear state estimation with a Luenberger-like observer and the classical PD+ used in robot manipulators.

Commentary by Dr. Valentin Fuster

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