Virtual Reference Signal Research Articles

Data-Driven-Based Cooperative Resilient Learning Method for Nonlinear MASs Under DoS Attacks.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Hierarchical Robust Generalized Nash Equilibrium Seeking of High-Order Uncertain Nonlinear Systems.

This article investigates the distributed generalized Nash equilibrium (GNE) seeking problem of noncooperative games (NGs) for high-order strict-feedback nonlinear multiagent systems (MASs). In particular, the feasible action set of each agent is not only subject to local set and inequality constraints but also coupled through an equality constraint with other agents. This constraint structure is more general and covers most of the constraints in the GNE seeking literature. To accomplish the concerned GNE seeking objective, we propose a novel hierarchical GNE seeking approach in this article to decouple the distributed GNE seeking algorithm design into two layers. First, we construct a distributed primary-dual GNE estimator to generate virtual reference signals that converge to the GNE. Then, with the output of the estimator as the reference signal, we develop an adaptive tracking controller to solve the resultant tracking problems under output constraints. To overcome the negative effects of the disturbances, novel compensating terms associated with smooth functions and positive integrable time-varying functions are incorporated in the controller design, which thereby realizes the exact GNE seeking in the presence of nonvanishing mismatched disturbances. At last, an example is given to support the theoretical analysis of the proposed algorithms.

Fixed‐time distributed adaptive optimization for third‐order nonlinear fully heterogeneous vehicular platoon systems

SummaryIn this article, the problem of fixed‐time distributed optimization is researched for third‐order fully heterogeneous nonlinear connected and autonomous vehicles. To address this problem, a fixed‐time distributed optimization algorithm is proposed via a two‐step strategy. First, an optimization algorithm is proposed to generate a virtual reference signal that can converge to the optimal solution. Then, we further construct an adaptive tracking controller to track the virtual signal based on the first step. In the design process of the controller, the virtual control variables and the actual control input are obtained by using the backstepping technique. Furthermore, a fast fixed‐time filter is introduced to avoid the issue of discontinuous gradient functions. Fuzzy logic systems are employed to address the unknown part of the nonlinear function. By using tools from the fixed‐time stability criterion, convex optimization theory, and Lyapunov stability theory, it can be demonstrated that the provided strategy drives all optimization variables to the optimal solution within a fixed time and guarantees the closed‐loop system signals are bounded. Finally, the effectiveness of control strategy is verified via a numerical simulation.

Moving sound source localization and tracking for an autonomous robot equipped with a self-rotating bi-microphone array.

In this paper, we present two approaches to localizing and tracking a sound source that moves in a three-dimensional (3D) space. The sound signal was captured by a unique bi-microphone system that rotates at a constant angular velocity. The motion of the sound source along with the rotation of the bi-microphone array produces a sinusoidal inter-channel distance difference (ICDD) signal with time-varying amplitude and phase. Four state-space models were developed and employed to design extended Kalman filters (EKFs) that identify instantaneous amplitude and phase of the ICDD signal. Both theoretical and numerical observability analyses of the four state-space models were performed to reveal singularities of the proposed EKFs in the domain of interest. We also developed a Hilbert-transform based method that localizes the sound source by comparing the true analytic ICDD signal to a virtual reference signal with zero elevation and azimuth angles. A moving average filter is then applied to reduce the noise and the effect of the artifacts at the beginning and the ending portions of the estimates. The effectiveness of the proposed methods was evaluated using comparison studies in simulation.

Skin-Induced Motion Artifact Removal for Ambulatory Electrocardiography

Skin-induced motion artifact (SIMA) is a troublesome problem for ambulatory electrocardiography (ECG) signal acquisition by e-skin dry sensor. Reference based algorithm is a promising way to tackle it. A highly correlated reference signal is the key for this solution. In this article, by using a new synthesized reference signal for LMS algorithm, which combines both skin induced impedance behavior and skin “slope” and recovery “time constant”, the SIMA related noise is greatly reduced. Firstly, we show the phenomenon that the SIMA “slope” and “recovery time constant” have some relationships with skin induced impedance, its derivatives and specific tissue characteristics. Secondly, We do the offline “slope” and “time constant” calibration work first, then measure the real time skin induced impedance, and then combine the above information together, to form a virtual reference signal, then we show the new reference has high correlation with actual SIMA noise. Finally, considering that SIMA potential consists of fast changing part and slow changing part, we use an improved LMS algorithm to handle the contradiction between speed and accuracy, which includes fast tracking part and low convergence error part. We set up the SIMA removal system in Matlab, and use actual motion artifact contaminated ECG signal as the input, and then do offline signal processing. Results show that SIMA is improved around <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7\sim 15$ </tex-math></inline-formula> dB typically on average. Integrating skin-induced impedance with human body related “slope” and “time constant” as a reference signal seems a feasible method to remove skin-induced motion artifact.

Event-Triggered Robust Control for Output Consensus of Unknown Discrete-Time Multiagent Systems With Unmodeled Dynamics.

This article investigates the event-triggered output consensus problem for a class of unknown heterogeneous discrete-time linear multiagent systems in the presence of unmodeled dynamics. The agents have individual nominal dynamics with unknown parameters, and the unmodeled dynamics are in the form of multiplicative perturbations. A novel design framework is developed based on an event-triggered internal reference model and a distributed model reference adaptive controller. To deal with the heterogeneity of the multiagent system, the event-triggered internal reference model is designed to generate a virtual reference signal for each agent with a dynamic event-triggering mechanism being adopted to reduce the communication burden between neighboring agents. To handle the unknown parameters and unmodeled dynamics, the robust model reference adaptive controller is then designed to follow the generated virtual reference signal. It is shown that if the unmodeled dynamics satisfy certain conditions, then the boundedness of all the signals and variables in the closed-loop system and convergence of consensus errors to a residual set are guaranteed. Moreover, the consensus errors will converge to zero asymptotically in the absence of unmodeled dynamics. Compared with existing related works, the proposed framework is able to deal with the agents with individual unknown nominal dynamics and unmodeled dynamics. Moreover, the proposed framework is fully distributed in the sense that no knowledge of any global information is needed. Finally, the performance of the proposed method is validated by examples.

Adaptive virtual referencing for the extraction of extracellularly recorded action potentials in noisy environments

Objective. Removal of common mode noise and artifacts from extracellularly measured action potentials, herein referred to as spikes, recorded with multi-electrode arrays (MEAs) which included severe noise and artifacts generated by an ultrahigh field (UHF) 16.4 Tesla magnetic resonance imaging (MRI) scanner. Approach. An adaptive virtual referencing (AVR) algorithm is used to remove artifacts and thus enable extraction of neural spike signals from extracellular recordings in anesthetized rat brains. A 16-channel MEA with 150-micron inter-site spacing is used, and a virtual reference is created by spatially averaging the 16 signal channels which results in a reference signal without extracellular spiking activity while preserving common mode noise and artifacts. This virtual reference signal is then used as the input to an adaptive FIR filter which optimally scales and time-shifts the reference to each specific electrode site to remove the artifacts and noise. Main results. By removing artifacts and reducing noise, the neural spikes at each electrode site can be well extracted, even from data originally recorded with a high noise floor due to electromagnetic interference and artifacts generated by a 16.4T MRI scanner. The AVR method enables many more spikes to be detected than would otherwise be possible. Further, the filtered spike waveforms can be well separated from each other using PCA feature extraction and semi-supervised k-means clustering. While data in a 16.4T MRI scanner contains significantly more noise and artifacts, the developed AVR method enables similar data quality to be extracted as recorded on benchtop experiments outside the MRI scanner. Significance. AVR of extracellular spike signals recorded with MEAs has not been previously reported and fills a technical need by enabling low-noise extracellular spike extraction in noisy and challenging environments such as UHF MRI that will enable further study of neuro-vascular coupling at UHF.

Output Homogenization and Synchronization of Heterogeneous Nonlinear Multiagent Networks

In this article, we are concerned with the output synchronization of a group of heterogeneous agents with nonlinear dynamics. It is shown that via dynamic coupling, the output synchronization problem is transformed into a family of output homogenization and internal-model-based synchronization problems. In contrast to conventional approaches, the adopted approach does not rely on the generation of a specific reference trajectory, to be tracked by all agents. Based on this premise, we propose a distributed state-feedback protocol driving all the heterogeneous multiagent individual systems onto a copy of a virtual reference signal. Invoking the results from the theory of output regulation and cooperative control, the state-feedback controller synthesis is conducted to achieve the output synchronization of heterogeneous multiagent networks. Immediate extensions of the obtained results to observer-based control are also discussed. The effectiveness of the proposed output synchronization algorithm is verified through theoretical analysis and case studies.

Friction-Tracker-Embedded Discrete Finite-Time Sliding Mode Control Algorithm for Precise Motion Control of Worm-Gear Reducers Under Unknown Switched Assistive/Resistive Loading

This paper proposes a novel method for simultaneous estimation of unknown switched external disturbance and precise position control of dynamical systems which employ worm gear drive reducers. This algorithm is based on an improved discrete sliding mode control (DSMC) design which ensures convergence to the desired sliding set without introducing chattering effects to the dynamical system. In order to improve the performance of DSMC and to obtain an accurate estimation of gearbox model, a new method for incorporation of switched friction is proposed in the presence of external disturbance. To this end, the dynamical response of worm gear drive reducer is investigated in distinct switched disturbance modes of resistive disturbances, assistive disturbances and no disturbance to obtain approximate functions describing system dynamics in aforementioned configurations. In comparison with the feedback data, the obtained models permit appropriate incorporation of friction into the improved DSMC scheme. Based on multiple common Lyapunov functions and calculation of virtual reference signals for armature currents, the feasibility of control algorithm is ensured in all modes of switched dynamics. The effectiveness of the proposed estimation and control schemes has been verified using an experimental setup employing a worm gear drive reducer on which multiple permanent magnet DC motors are installed, and improvements resulted from incorporation of friction estimation algorithm are analyzed.

Error-Driven-Based Nonlinear Feedback Recursive Design for Adaptive NN Trajectory Tracking Control of Surface Ships With Input Saturation

In this paper, we investigate the trajectory tracking control problem of surface ship subject to the dynamic uncertainties, unknown time-varying disturbances and input saturation. To handle the non-smooth input saturation nonlinearity and compensate the ship dynamic uncertainties, Gaussian error function and adaptive neural network technique are employed. In control design, to obtain the transient motion reference signal, finite-time nonlinear tracking differentiator is applied to generate virtual reference signal and to extract the derivative of virtual control law. Referring to the effects of the kinematics subsystem on the kinetics subsystem caused by the error of tracking differentiator, and the effects of the input saturation on the control accuracy and the dynamic quality of the trajectory tracking control system, we propose an error-driven-based nonlinear feedback recursive design technique to design trajectory tracking control law, and employ a new non-quadratic Lyapunov functions to analyze the trajectory tracking control system stability. The proposed control scheme fully embodies the characteristics of the lowgain and high-gain control, and overcomes the effect of tracking differentiator error on closed-loop system by recursive design method. Simulation results verify the effectiveness of our proposed control scheme.

Robust Adaptive Fuzzy Control of a Class of Uncertain Nonlinear Systems With Unstable Dynamics and Mismatched Disturbances.

This paper studies the robust stabilization problem for a class of uncertain nonlinear systems with unstable zero dynamics. The considered zero dynamic is not assumed to be input-to-state practically stable and contains nonlinear uncertainties and mismatched external disturbances. A new robust adaptive fuzzy control method is developed by combining theory with backstepping technique. First, an ideal virtual control function is designed, which can guarantee the zero dynamic asymptotically stable with a suboptimal performance. Then, based on some non-negative functions and backstepping design, the actual controller is constructed for the overall system, which ensures that the tracking error for the ideal virtual control signal converges to a priori accuracy regardless of external disturbances. In this design, an auxiliary signal is introduced to overcome the difficulties from the unavailable virtual reference signal. By exploiting the implicit function theorem, the proposed design technique is directly applied to a special case, where the zero dynamic is partially linear. A two inverted pendulums is used to illustrate the application and effectiveness of the proposed design method.

Constrained MPC to track time‐varying reference signals: Online optimization of virtual reference signals and controller states

In this paper, we propose a model predictive control (MPC) algorithm for linear systems with constraints to track time‐varying reference signals. The proposed controller is assumed to have an integrator as a servo compensator. In the proposed control approach, the value of the integrator state is optimally initialized at each sampling time to improve tracking control performance until the cost function becomes sufficiently small. Moreover, while the cost function is sufficiently small, the integral action is used to achieve offset‐free tracking. The control algorithm is reduced to a convex optimization problem with linear matrix inequality constraints. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.

Iterative turbo beamforming for orthogonal frequency division multiplexing-based hybrid terrestrial-satellite mobile system

In the context of orthogonal frequency division multiplexing (OFDM)-based systems, pilot-based beamforming (BF) exhibits a high degree of sensitivity to the pilot sub-carriers. Increasing the number of reference pilots significantly improves BF performance as well as system performance. However, this increase comes at the cost of data throughput, which inevitably shrinks due to transmission of additional pilots. Hence an approach where reference signals available to the BF process can be increased without transmitting additional pilots can exhibit superior system performance without compromising throughput. Thus, the authors present a novel three-stage iterative turbo beamforming (ITBF) algorithm for an OFDM-based hybrid terrestrial-satellite mobile system, which utilises both pilots and data to perform interference mitigation. Data sub-carriers are utilised as virtual reference signals in the BF process. Results show that when compared to non-iterative conventional BF, the proposed ITBF exhibits bit error rate gain of up to 2.5 dB with only one iteration.

Partial sound field decomposition in multireference near-field acoustical holography by using optimally located virtual references

It has been shown previously that the multiple reference and field signals recorded during a scanning acoustical holography measurement can be used to decompose the sound field radiated by a composite sound source into mutually incoherent partial fields. To obtain physically meaningful partial fields, i.e., fields closely related to particular component sources, the reference microphones should be positioned as close as possible to the component physical sources that together comprise the complete source. However, it is not always possible either to identify the optimal reference microphone locations prior to performing a holographic measurement, or to place reference microphones at those optimal locations, even if known, owing to physical constraints. Here, post-processing procedures are described that make it possible both to identify the optimal reference microphone locations and to place virtual references at those locations after performing a holographic measurement. The optimal reference microphone locations are defined to be those at which the MUSIC power is maximized in a three-dimensional space reconstructed by holographic projection. The acoustic pressure signals at the locations thus identified can then be used as optimal “virtual” reference signals. It is shown through an experiment and numerical simulation that the optimal virtual reference signals can be successfully used to identify physically meaningful partial sound fields, particularly when used in conjunction with partial coherence decomposition procedures.

Virtual reference signals for active noise cancellation system.

This paper proposes a method that provides a new set of linear fixed coefficients filters to define and synthesize a new set of reference signals that are fewer in number than the original set of reference signals. It also can be used as the reference signals for an active noise cancellation system. To optimize the performance of the active noise cancellation system, it is very important to find proper reference signals that are coherent with the target noise. In a case of active road noise cancellation in a vehicle cabin, for satisfying this particular requirement, many accelerometers are installed to detect vibration on the vehicle's suspension mechanism. In most cases, these reference signals contain information about fewer number of noise sources compared to the number of accelerometers. Proposing the linear filtering method that is based on Singular Value Decomposition (SVD) of the power spectrum matrix of the reference signals enables the synthesis of the virtual reference signals. After defining the frequency response functions of the filters, we design the ARMA direct form digital filters. After designing the fixed coefficients filters for the virtual reference signals, we evaluate the performance of the ANC system through experimental tests and its results by comparing the original reference signals and the virtual reference signals to confirm the proposed method.

Iterative Generation of Virtual Reference for a Manipulator

SUMMARYVirtual reference is defined as the signal for a closed-loop System so that the response tracks the desired path perfectly. It is proposed to obtain a virtual reference signal suitable for a manipulator to track a predetermined trajectory using repetitive operations. Thus the algorithm ensures that the difference between the desired and the manipulator trajectories will become negligible as the number of operations approaches infinity if the manipulator is compensated for properly by local feedback. This algorithm uses a dual System to recursively improve the reference signal. Such a System has the advantage of being simple to design. The derivation of this algorithm is based on functional analysis. The effectiveness is confirmed by two experimental results.