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In this article, we present a novel digital predistortion (DPD) system which can be updated quickly and efficiently in response to the dynamic reconfiguration of multiuser multiple-input multiple-output (MIMO) transmitters. By identifying the shared properties of different power amplifiers (PAs) with two feature extraction stages, nonlinear behaviors of the PAs are encoded into low-dimensional features. Using the extracted features as input, a novel DPD generator is employed to synthesize DPD model coefficients directly. In this way, when the power levels or beam angles change, the DPD model can be updated fast and accurately without capturing the output data or applying linear system identification algorithms. To capture slow-varying dynamics of the PAs, the features can also be calibrated efficiently in the background. Computational complexity and operational latency can thus be reduced significantly. Simulation and experimental results demonstrate that the proposed DPD approach achieves excellent linearization performance with low complexity, making itself a promising linearization solution for 5G massive MIMO transmitters. [ABSTRACT FROM AUTHOR]

This article explores a novel composite signal extraction method to extract high-precision angular position from the signals detected by Hall sensors in the gimbal servo system of single-gimbal magnetically suspended control moment gyro (SGMSCMG). Due to magnetic field distortion and other factors, the detected signals involve lots of harmonics and unequal amplitude, which decrease the position resolution accuracy significantly. In the proposed method, normalization is first performed to obtain signals with equal amplitude. Then, on the basis of offline analysis, the signal reconstructed model is established to suppress the most harmonics. Finally, the errors of the models and unmodeled noise are restrained by applying the discrete extended state observer (ESO). Simulated results show that the influence of harmonics and unequal amplitude on the angular position can be almost attenuated by the proposed method. Besides, plentiful experiments on an SGMSCMG demonstrate the validity of the proposed method, and the desired angular position of the ultralow speed servo system can be extracted effectively. [ABSTRACT FROM AUTHOR]

Formation control finds broad applications in numerous fields, such as cooperative detection, surveillance, transportation, and disaster rescue. In this article, aiming at hunting a moving target, a two-stage surrounding control algorithm is proposed for linear multiagent systems subject to input saturations. An adaptive distributed observer is developed for each agent to reconstruct the target’s position. With the assistance of the algebraic graph theory and low gain feedback technique, distributed controllers are designed to drive the agents to encircle the moving target with a fixed radius and evenly distributed phase angles. Finally, both numerical simulations and experiments are conducted to verify the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

This article designs the static and dynamic event-triggered control (ETC) and self-triggered control (STC) algorithms to achieve the semiglobal stabilization of discrete-time systems with input constraints. First, a novel static ETC algorithm based on the discrete-time parametric Lyapunov equation (DPLE) is designed. In order to further increase the interevent times (IETs), the corresponding dynamic ETC is designed. Next, both static and dynamic STC, where the next control law updates depend on the previous triggered states, are proposed to avoid monitoring the measurement errors. The proposed algorithms are not only capable of reducing the number of transmissions significantly but also build a very simple and clear relationship between the only design parameter and the nontrivial IET (NIET). This allows us to change regularly IETs by adjusting the design parameter so that the nontriviality of static and dynamic ETC and STC is guaranteed and a tradeoff between the IETs and the control performance can be easily found. Specifically, by exploring the properties of DPLE, the designed algorithms avoid the complex relationship between the nontrivial condition and the system matrices. Finally, the designed static and dynamic ETC and STC algorithms are applied to the design of the spacecraft rendezvous control system and their effectiveness is verified by simulation results. [ABSTRACT FROM AUTHOR]

This article investigates the leader-following output consensus problem of heterogeneous linear multiagent systems, where the nonidentical followers are subject to norm bounded parameter uncertainties. Two novel distributed event-triggered consensus strategies based on state feedback and output feedback, respectively, are proposed to solve the leader-following output consensus problem. A dynamic event-triggering mechanism which involves internal state variables is proposed to determine the next triggering instant. It should be noted that the controllers can be designed without the knowledge of some global graph information, such as the eigenvalues of the Laplacian matrix associated with the corresponding communication graph and the number of agents. In addition, the interevent time can be prolonged and the Zeno behavior can be excluded with the proposed dynamic triggering mechanism. Finally, an example is presented to illustrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

This article investigates the global stabilization problem of the circular orbit rendezvous system with actuator saturation and time-delay. By decomposing the linearized relative motion equations into a cascade of neutral stable linear systems, linear state feedback controllers are proposed in the presence of both actuator saturation and/or time-delay. The global stability of the closed-loop system is proved. Optimal feedback gain is also obtained in the delay-free case. Simulation results are given to show the effectiveness of the presented methods. [ABSTRACT FROM AUTHOR]

This brief considers Kalman filter for linear systems with unknown structural parameters. We design a Bayesian parameter identification algorithm based on maximum likelihood (ML) criterion and expectation maximization (EM). Under the identification of structural parameter, we perform the Kalman filter to estimate the states. The Kalman filter and the parameter identification algorithm are interactive to estimate the states and the structural parameters. More specifically, first, we use EM algorithm together with Kalman smoother to estimate the structural parameters. Then, on the basis of results provided by the first step, we employ the classical Kalman filter to predict and update the states, which formulates the final proposed Kalman filter. Performance analysis and simulation results verify the presented filter. [ABSTRACT FROM AUTHOR]

This brief is concerned with the event-triggered control of linear systems with triggered dual channels. The system outputs and control inputs are respectively driven by two different triggering mechanisms. An integral controller with nonlinear compensation terms is established to attenuate the potential data jitter and reduce the unnecessary transmissions. The uniformly ultimate boundedness of the resultant closed-loop system is established by means of Simpson rule, Finsler lemma and the Lyapunov stability theory and the controller gain is solved in terms of linear matrix inequality. The validity of the proposed scheme is verified by a numerical example. [ABSTRACT FROM AUTHOR]

This brief studies the cluster synchronization problem for networks of linearly coupled systems where the dynamical system models in different clusters are nonidentical. To ensure scalability and reconfigurability of networks, a distributed adaptive law is proposed to update the intra-cluster coupling strengths. Concretely, each system is required to update its own coupling strength with its cluster members, instead of using a common strength for all systems. The adaptive updating law applied by each system depends on relative states with respective to connected cluster members which are locally available information. Moreover, the parameters involved in the adaptive law of each system allow decentralized off-line computations. Both generic linear systems which are partial-state coupled and nonlinear oscillators which are full-state coupled are considered, respectively. [ABSTRACT FROM AUTHOR]

Outliers can be caused by sensor errors, model uncertainties, changes in the ambient environment, data loss, or malicious cyberattacks to contaminate the measurement process of many nonlinear dynamic systems. When the extended Kalman filter (EKF) is applied to such systems for state estimation, the outliers can seriously reduce the estimation accuracy. This brief proposes an innovation saturation mechanism to make the EKF robust against outliers. This mechanism applies a saturation function to the innovation process that the EKF leverages to correct the state estimation. As such, when outliers occur, the distorted innovation is saturated, so as not to undermine the state estimation. The mechanism features an adaptive adjustment of the saturation bounds. The design leads to the development of robust EKF approaches for both continuous- and discrete-time systems. The stability of the proposed approaches when applied to linear systems is characterized, showing that they are capable of performing bounded-error estimation in the presence of bounded outlier disturbances in this case. A simulation study about mobile robot localization is presented to illustrate the efficacy of the proposed design. Compared to existing methods, the proposed approaches can effectively reject outliers of various magnitudes, types, and durations, at significant computational efficiency and without requiring additional measurement redundancy. [ABSTRACT FROM AUTHOR]

The filtered split-path nonlinear integrator (F-SPANI) is a generic nonlinear controller designed to improve the transient performance of linear (motion) systems in terms of overshoot. The main idea underlying F-SPANI is that the amplitude and phase of an integrator can be tuned using independent filters, resulting in more efficient use of the buffer of the integrator. In this article, a general description of F-SPANI is presented. In addition, a stability analysis result is presented that provides sufficient conditions in the form of linear matrix inequalities (LMIs) for closed-loop stability analysis on the basis of construction of a common quadratic Lyapunov function (CQLF). The ease of the design, implementation, and the potential of the proposed controller are illustrated both in simulations and in experiments on an industrial pick-and-place machine. [ABSTRACT FROM AUTHOR]

The increasing complexity of modern technical systems can exacerbate model uncertainty in model-based control, posing a great challenge to safe and effective system operation under closed loop. Online learning of model uncertainty can enhance control performance by reducing plant–model mismatch. This article presents a learning-based stochastic model predictive control (LB-SMPC) strategy for reference tracking of stochastic linear systems with additive state-dependent uncertainty. The LB-SMPC strategy adapts the state-dependent uncertainty model online to reduce plant–model mismatch for control performance optimization. Standard reachability and statistical tools are leveraged along with the state-dependent uncertainty model to develop a chance constraint-tightening approach, which ensures state constraint satisfaction in probability. The stability and recursive feasibility of the LB-SMPC strategy are established for tracking time-varying targets, without the need to redesign the controller every time the target is changed. The performance of the LB-SMPC strategy is experimentally demonstrated on an atmospheric pressure plasma jet (APPJ) testbed with prototypical applications in plasma medicine and materials processing. Real-time control comparisons with learning-based MPC with no uncertainty handling and offset-free MPC showcase the usefulness of LB-SMPC for predictive control of safety-critical systems with hard-to-model and/or time-varying dynamics. [ABSTRACT FROM AUTHOR]

This article investigates the problem of secure state estimation for continuous-time linear systems in the presence of sparse sensor attacks. Compared with the existing results, the attacked sensor set can be changed by adversaries against secure estimation. To address the more erratic attacks, a novel supervisory state observer is proposed, which employs a bank of candidate nonlinear subobservers and a switching logic administrated by a monitoring function to select the active subobserver at every instant of time. Based on the stability analysis of switched systems, it is proven that the supervisory observer asymptotically converges to a neighborhood of the true system state in the presence of the sensor attacks and bounded disturbances. A simulation example is given to substantiate the theoretical results. [ABSTRACT FROM AUTHOR]

In this article, the consensus problem of linear systems is revisited from a novel geometric perspective. The interaction network of these systems is assumed to be piecewise fixed. Moreover, it is allowed to be disconnected at any time but holds a quite mild joint connectivity property. The system matrix is marginally stable and the input matrix is not of full-row rank. By directly examining the subspace determined by the network, we first establish convergence by resorting to an observability condition. Then, according to joint connectivity, we are able to extend this convergence uniformly to the entire orthogonal complement of the consensus manifold. In this way, we work out the necessary and sufficient condition for exponential consensus. It turns out that, with a suitably designed feedback matrix, exponential consensus can be realized globally and uniformly if and only if a jointly $(\delta,T)$ -connected condition and an observability condition relying only on the system and input matrices are satisfied. We also characterize the lower bound of the convergence rate. Simple yet effective examples are presented to illustrate the findings. [ABSTRACT FROM AUTHOR]

Addressing the nonlinearity induced signal degradation is of critical importance for harvesting the transparent benefits of an analog radio over fiber (A-RoF) architecture, to support mobile fronthaul (MFH) with high spectral efficiency and reduced latency. Here, for linearizing the low-latency A-RoF MFH using digital-domain fast Fourier transform /inverse fast Fourier transform (FFT/IFFT) assisted channel aggregation/de-aggregation (CA/DA), we propose and experimentally demonstrate a compatible adaptive frequency-domain (FD) nonlinear equalization scheme. This scheme is characterized by a much lower computational complexity compared with the popular time-domain Volterra nonlinear equalizer while delivering a comparable performance. The proposed equalization scheme is implemented using the FD two-box cascaded Hammerstein structure. It enables simplified coefficients estimation, computationally efficient equalization, FFT/IFFT calculation reusing in channel DA, and shared legacy one-tap FD linear equalization in the OFDM system. Experimental validations of the proposed nonlinear equalization approach are carried out via typical electroabsorption modulator (EAM) and Mach-Zehnder modulator (MZM) based intensity modulation and direct detection (IMDD) A-RoF links. Cost-effective fiber-optic (with EAM) and optical up-conversion enabled V-band millimeter-wave (mm-wave) fiber-wireless (with MZM) transmissions are demonstrated. Experimental results show that the proposed scheme can effectively improve the performances of both EAM and MZM based 10-km fiber-optic A-RoF MFH links, in terms of the (>8 dB) reduction of adjacent channel leakage ratio, (>10 dB) improvement of error-vector magnitude (EVM) and (>5 dB) enhancement of input intermediate-frequency (IF) power range, which retains an acceptable (below 8 %) EVM performance for the 64-QAM modulation. The input IF signal aggregates 8 125-MHz 64-QAM OFDM channels. Furthermore, the proposed scheme enables the transmission of a 5-Gbit/s aggregated IF 64-QAM signal over a 10-km fiber link and 1.2-m 60-GHz wireless channel, wherein a >12 dB improved input power margin is observed. [ABSTRACT FROM AUTHOR]

Active disturbance compensation is crucial for realizing high-precision servo control. However, the suppression of rapidly changing disturbances is still a challenging issue. In this article, observer-based disturbance-rejection control is investigated, and an observer with adaptive switching gain is proposed for improved robustness against changing disturbances. First, a disturbance observer (DOB) is established, based on the tracking differentiator technique. Second, a sliding-mode-assisted observer with an adaptive law is designed to estimate the residual-disturbance estimation error of the DOB. The proposed scheme instantly increases the switching gain when the state deviates from a preset boundary, thus eliminating the tuning time. The switching control action with an immediately large switching gain is robust against suddenly changing disturbances. Moreover, the switching gain then decays to zero, reducing chattering in the steady state. The gain adjustment process is designed to have the same dynamic characteristics as the proposed DOB, which guarantees the convergence of the proposed scheme. This method is not only suitable for linear systems, but also extendable to nonlinear systems. The proposed scheme was evaluated experimentally; the results showed that it improves disturbance rejection and decreases servo error. [ABSTRACT FROM AUTHOR]

VLSI layout patterns provide critical resources in various design for manufacturability research, from early technology node development to back-end design and sign-off flows. However, a diverse layout pattern library is not always available due to long logic-to-chip design cycle, which slows down the technology node development procedure. To address this issue, in this paper, we explore the capability of generative machine learning models to synthesize layout patterns. A transforming convolutional auto-encoder (TCAE) family is developed to learn vector-based instantiation of squish pattern topologies. We show our TCAE models can capture simple design rules and enlarge the existing squish topology space under certain transformations. With adaptive configurations, the proposed G-TCAE framework allows both massive pattern generation and context-specific pattern generation. Geometry information of each squish topology is obtained from an associated linear system derived from design rule constraints. Experiments on $7nm$ EUV designs show that our framework can more effectively generate diverse pattern libraries with DRC-clean patterns compared to a state-of-the-art industrial layout pattern generator. [ABSTRACT FROM AUTHOR]

Due to the nonlinear time-periodic nature, precise impedance modeling of power-converter-based single-phase ac systems is complex. Small-signal modeling for the systems with a fixed operating point is easy, but the extension of the same procedure to the systems with time-periodic trajectory is still challenging. In this article, a recursive single-input single-output (SISO) impedance modeling framework for single-phase rectifiers is presented, which can greatly simplify both the modeling procedure and the resulting impedance model. A general linear-time periodic modeling method is proposed by extending the linear-time invariant modeling method, where frequency-coupling effects are modeled by the transfer function vector rather than the transfer function matrix, so the modeling complexity is reduced. Furthermore, based on the idea of mathematical induction, an analytical and accurate recursive SISO impedance model is derived. The resulting recursive SISO impedance model can characterize the frequency-coupling dynamics of arbitrary order with a low computation burden. Experiments validate the accuracy of the recursive SISO impedance modeling method. [ABSTRACT FROM AUTHOR]

This article studies the flocking problem of multiagent systems (MASs) with a dynamic virtual leader (VL) for the linear systems subject to time-varying uncertainties and external disturbance problems. We assume that the agents follow a leader with a bounded acceleration, which information is sensed only by the informed agents. We assign an adaptive estimator to each uninformed agent to estimate the velocity of the leader. By deriving a lemma and using a standard adaptive law, we design a continuous adaptive controller for solving the problem under the mentioned challenges, which is also fully distributed. The designed flocking control ensures that the velocity of followers asymptotically is converged to that of the VL. Also, the agents’ network remains connected, and no collision occurs among agents. Finally, we present a simulation that illustrates the validity of the results. [ABSTRACT FROM AUTHOR]

This article develops a generalized $\mathcal {H}$ -representation, which can transforms stochastic systems into deterministic systems. With the help of this new approach, the necessary and sufficient conditions of $p$ th moment asymptotic stability and stabilization of linear stochastic systems are addressed. Meanwhile, the generalized $\mathcal {H}$ -representation method is also applied to investigate $p$ th moment observability and $p$ th moment complete observability for the addressed systems. [ABSTRACT FROM AUTHOR]

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as robust simultaneously stabilizing decoupling (RSSD) output feedback controller that achieves simultaneous stabilization (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input–multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, we considered a plant in this set that has the smallest maximum $v-$ gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm-based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations. [ABSTRACT FROM AUTHOR]

This article addresses the robust fixed-time stabilization control problem for generic linear systems with both matched and mismatched disturbances. A new observer-based fixed-time control technique is proposed to solve this robust stabilization problem, provided that the system matrix pair $(A,B)$ is controllable. The ultimate boundedness of the closed-loop system in the presence of mismatched disturbances is proven. An upper bound of the convergence time is provided, which is irrelevant to initial conditions. Finally, a simulation example is presented to show the efficiency of the proposed control design method. [ABSTRACT FROM AUTHOR]

This article addresses the problem of adaptive finite-time neural tracking control for nonstrict-feedback nonlinear systems via dynamic surface (DS) technique. First, a new quasi-fast finite-time practical stability (QFPS) criterion is proposed for a class of general nonlinear systems. Then, the presented QFPS criterion is applied to design the desired adaptive finite-time neural tracking controller for a class of nonstrict-feedback nonlinear systems. The presented design scheme for the nonstrict-feedback nonlinear system has the following two features: 1) the “explosion of complexity” issue of the backstepping design is addressed by utilizing the DS technique and 2) the structural feature of Gaussian functions is applied to solve the design difficulties caused by the nonstrict-feedback form. It is proved that the designed controller for the nonstrict-feedback nonlinear system can make the resulting closed-loop system stabilizable in a quasi-fast finite time and the tracking error converges to a sufficiently small neighborhood of the origin. Finally, the simulation results are given to show the validity and practicability of the proposed design scheme. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) has been successfully applied in several data mining tasks. Recently, there is an increasing interest in the acceleration of NMF, due to its high cost on large matrices. On the other hand, the privacy issue of NMF over federated data is worthy of attention, since NMF is prevalently applied in image and text analysis which may involve leveraging privacy data (e.g, medical image and record) across several parties (e.g., hospitals). In this paper, we study the acceleration and security problems of distributed NMF. First, we propose a distributed sketched alternating nonnegative least squares(DSANLS) framework for NMF, which utilizes a matrix sketching technique to reduce the size of nonnegative least squares subproblems with a convergence guarantee. For the second problem, we show that DSANLS with modification can be adapted to the security setting, but only for one or limited iterations. Consequently, we propose four efficient distributed NMF methods in both synchronous and asynchronous settings with a security guarantee. We conduct extensive experiments on several real datasets to show the superiority of our proposed methods. The implementation of our methods is available at https://github.com/qianyuqiu79/DSANLS. [ABSTRACT FROM AUTHOR]

This article studies the containment control problem for a group of linear systems, consisting of more than one leader, over switching topologies. The input matrices of these linear systems are not required to have full-row rank and the switching can be arbitrary, making the problem quite general and challenging. We propose a novel analysis framework from the viewpoint of a state transition matrix. Specifically, according to the inherent linearity, we successfully establish a connection between state transition matrices of the above multileader system and a virtual leader-following system obtained by combining those leaders. This enlightening result relates the containment problem to a consensus one. Then, by analyzing the property of the state transition matrix, we uncover that each component of any follower’s state converges to the convex hull spanned by the corresponding components of the leaders’, provided some mild conditions are satisfied. These conditions are derived in terms of the concept of a positive linear system. A special case of the second-order linear system is further discussed to illustrate these conditions. Moreover, two different design methods of the feedback gain matrix are provided, which additionally require that the network topology contains a united spanning tree all the time. [ABSTRACT FROM AUTHOR]

This article investigates the input-to-state stability (ISS) of continuous-time networked control systems with model uncertainty and bounded noise based on event triggering. The feedback loop is closed over an unreliable digital communication network. Feedback packets suffer from network delay and may be dropped in an independent and identically distributed (i.i.d.) way, which may harm to the concerned stability. This article focuses on a Lyapunov-based event-triggered control design scheme with the consideration of i.i.d. packet dropouts. By designing a state-dependent event-triggering threshold and updating methods, it can still ensure ISS for the concerned multidimensional system in the presence of i.i.d. packet dropouts and model uncertainty without exhibiting the Zeno behavior. Simulations are done to verify the effectiveness of the achieved results. [ABSTRACT FROM AUTHOR]

In this paper it is considered the stabilization and control of a class of high order linear systems that are subject to constant time delay at the input-output channel. In particular, systems with two unstable poles and any number of real or complex conjugate stable poles are studied. To improve the solutions existing in the literature, a new proposal for the solution of the considered problem is taken into account consisting in the design of an observer and a ProportionalDerivative PD controller to assure stable closedloop performance, establishing by means of a frequency analysis necessary and sufficient conditions for the proposed control strategy. The proposal novelty is the inclusion of the PD control action in the observer that allows to estimate internal signals used in the solution. This configuration offers advantages in the time delay size that can be handled and on the control performance of the closed loop system. A practical design procedure is presented to determine the value of the controller gains. Numerical simulations examples are presented to show the operation of the proposal. [ABSTRACT FROM AUTHOR]

This article demonstrates how motion effect can be exploited to read moving chipless radio frequency identification (RFID) tags at larger distances compared to what has been reached without taking into account the movement. According to the Doppler effect, due to the time-variant behavior of moving chipless tags, the tag backscattered field contains frequency components different from those that are transmitted. These motion-induced frequency components can be utilized to efficiently detect the tag at large distances, in a real environment composed of stationary objects. A circuit-based analytical model verified by full-wave simulations is presented to effectively predict the quasi-stationary backscattered field from moving scatterers, with fast computation process. The developed analytic model is applied to rotating dipole scatterers and is used to design chipless tags, including an identifier. In terms of identification, good agreement is observed between the measurement results and those are predicted by the model. Finally, read range enhancement is proven experimentally by a real environment measurement where the chipless tags are readable at distances up to several meters. [ABSTRACT FROM AUTHOR]

Parameter uncertainties and measurement outliers unavoidably exist in a real linear system. Such uncertainties and outliers make the true joint state-measurement distributions (induced by the true system model) deviate from the nominal ones (induced by the nominal system model) so that the performance of the optimal state estimator designed for the nominal model becomes unsatisfactory or even unacceptable in practice. The challenges are to quantitatively describe the uncertainties in the model and the outliers in the measurements, and then robustify the estimator in a right way. This article studies a distributionally robust state estimation framework for linear systems subject to parameter uncertainties and measurement outliers. It utilizes a family of distributions near the nominal one to implicitly describe the uncertainties and outliers, and the robust state estimation in the worst case is made over the least-favorable distribution. The advantages of the presented framework include: 1) it only uses a few scalars to parameterize the method and does not require the structural information of uncertainties; 2) it generalizes several classical filters (e.g., the fading Kalman filter, risk-sensitive Kalman filter, relative-entropy Kalman filter, outlier-insensitive Kalman filters) into a unified framework. We show that the distributionally robust state estimation problem can be reformulated into a linear semi-definite program and in some special cases it can be analytically solved. Comprehensive comparisons with existing state estimation frameworks that are insensitive to parameter uncertainties and measurement outliers are also conducted. [ABSTRACT FROM AUTHOR]

This article analyzes the propagation of feedback noise in multisampled dc–dc power converters. The analytical calculations for noise attenuation, strictly valid for linear time-invariant systems, are found to offer good predictions for power converters, after suppressing the decimation effects of the digital pulsewidth modulator (DPWM). For control systems that employ a proportional-integral controller, without any digital low-pass filters, the nonlinearity caused by the DPWM decimation is found to saturate the noise attenuation properties, as the multisampling factor $N$ is increased. Strong noise attenuation is enabled using antialiasing digital filters, with a small impact on the dynamic response. For control systems that employ a proportional-integral-derivative controller, increase of the multisampling factor, without any digital filters, causes the amplification of the noise power. Therefore, a greater attenuation of high-frequency components is required to provide a significant noise reduction in the control bandwidth. Even with these antialiasing digital filters, the dynamic response of multisampled control systems is improved compared to double-update. This is proven by the analytical and experimental comparison of various multisampled control strategies in terms of dynamic response and noise attenuation capabilities. The experimental results, from a buck-type converter, match well with simulations and analytical calculations. [ABSTRACT FROM AUTHOR]

This article involves the problems of stabilization and guaranteed cost control for a class of delayed continuous-time nonlinear systems. The sufficient conditions for the existence of stabilization controllers and guaranteed cost controllers are investigated by constructing a new Lyapunov matrix-based Lyapunov–Krasovskii functional. These conditions can be easily verified by using standard compute software (e.g., the toolbox YALMIP of MATLAB), since they are expressed as simple linear matrix inequalities. Furthermore, the feasible solutions of these linear matrix inequalities are used to describe the explicit forms of stabilization controllers and guaranteed cost controllers. Finally, the validity of the proposed Lyapunov matrix-based LKF method is tested by two numerical examples. [ABSTRACT FROM AUTHOR]

In this article, we investigate the synchronization problem of continuous-time linear systems with time-varying output couplings. Both bidirectional and unidirectional couplings are studied. In the former one, each pair of connected subsystems can both measure the outputs consisting of the relative information, whereas in the unidirectional case, it is not necessarily the case and a specific scenario when the output matrices have a common matrix factor is considered. The distributed synchronization protocols are designed and the convergence conditions of the resulting system are established making use of switched systems theory based on the detectability assumption and appropriate graph connectivity conditions. Finally, numerical examples are given to verify the derived results. [ABSTRACT FROM AUTHOR]

This article presents a method for structured robust control design for systems with a mixture of parametric and dynamic uncertainty. The proposed method alternates between an analysis step and a synthesis step. Samples of the parametric uncertainty are computed during the analysis steps, thus yielding an array of uncertain systems containing only dynamic uncertainty. The controller is then synthesized on this array of uncertain models. This synthesis step itself involves an alternation between constructing a D-scale for each of the uncertain systems and tuning a single controller for the entire collection of scaled plants. The controller tuning is performed using structured control design techniques. The proposed method is utilized to design a flutter suppression controller for a flexible aircraft. The aircraft dynamics are described by both a high-fidelity and a reduced-order model. The design objectives for flutter suppression are to achieve robust stabilization in the presence of mixed uncertainty. The proposed structured design method yields a single, low-order, linear time-invariant (LTI) controller, which increases the flutter speed by 15%. Additional robustness analyses and high-fidelity simulations are provided to assess the controller performance. [ABSTRACT FROM AUTHOR]

We present a method for dynamics-driven, user-interface design for a human–automation system via sensor selection. We define the user interface to be the output of a multiple-input–multiple-output (MIMO) linear time-invariant (LTI) system and formulate the design problem as one of selecting an output matrix from a given set of candidate output matrices. Necessary conditions for situation awareness are captured as additional constraints on the selection of the output matrix. These constraints depend on the level of trust the human has in the automation. We show that the resulting user-interface design problem is a combinatorial, set-cardinality minimization problem with set function constraints. We propose tractable algorithms to compute optimal or suboptimal solutions with suboptimality bounds. Our approaches exploit monotonicity and submodularity present in the design problem and rely on constraint programming and submodular maximization. We apply this method to the IEEE 118-bus, to construct correct-by-design interfaces under various operating scenarios. [ABSTRACT FROM AUTHOR]

In the realm of lithium-ion (Li-ion) battery modeling, owing to its simplicity, the single particle model (SPM) has long been considered to be a promising reduced-order model (ROM) candidate to usher in the era of physics-inspired models (PIMs) in embedded applications. However, at high load currents, the standard SPM exhibits poor accuracies in computing the cell’s terminal voltage, thereby rendering it unsuitable as the plant model in state-estimation tasks. A comprehensive evaluation of the salient electrolyte-enhanced SPMs from the literature reveals that current solutions are either mathematically intractable or overly simplistic. For the ionic concentration in the electrolyte, the well-known quadratic approximation model, which straddles the boundary of computational complexity and mathematical tractability, reveals poor temporal performance, particularly at the current collector interfaces. In this work, we retain the spatial dynamics of the quadratic approximation model while proposing a novel approach using system identification techniques for its temporal dynamics. By employing linear approximations for the relevant subsystems, we identify discrete-time transfer functions of the debiased number of moles per unit area of lithium ions in the electrolyte within each electrode region, that yield improved spatio-temporal accuracies for the electrolyte concentration profile. We then augment the standard SPM with the new system identification-based electrolyte dynamics to arrive at an electrolyte-enhanced composite SPM (EECSPM). Finally, we demonstrate the superior performance of the EECSPM compared with the incumbent state of the art, thereby representing a concrete advancement toward the goal of using PIMs in real-time applications. [ABSTRACT FROM AUTHOR]

Wave energy converters (WECs) need to be optimally controlled to be commercially viable. These controllers often require an estimate of the (unmeasurable) wave excitation force. To date, observers for WECs are often based upon ‘complex’ techniques, which are counter-intuitive in their design, additionally requiring an explicit model to describe the excitation as part of an (augmented) system. The latter imposes strong assumptions on the design of each observer, while also implying an additional computational burden associated with the necessity of augmenting the WEC model to include the dynamics of the input. We propose a simple and effective excitation force estimator based on linear time-invariant (LTI) theory, without the need for an explicit model of the input. In particular, we re-formulate the unknown-input estimation problem as a tracking control-loop, so that a wide-variety of LTI design techniques (arising from either classical or modern control theory) can be used to compute an estimate of the excitation force. We demonstrate performance, simplicity, and intuitive appeal of the proposed observer, by means of a case study based on a realistic computational fluid dynamics simulation, comparing the technique against a large set of WEC observers, showing that the approach is able to outperform available estimators. [ABSTRACT FROM AUTHOR]