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This paper investigates the adaptive event-triggered consensus control problem for a class of linear multi-agent systems subject to cyber attacks and communication delays. To effectively alleviate unnecessary signals transmission among the agents and achieve the reasonable allocation of resources, an adaptive event-triggering scheme, whose threshold parameters are adaptively adjusted based on system performance needs, is proposed. The event-triggering scheme can achieve the discontinuous communication and significantly reduce the number of signals transmission while ensuring the desired control performance. By considering the communication scheme and consensus control protocol in a unified framework, the linear multi-agent system is reformulated as a time-delay error system. Sufficient conditions are developed to guarantee the asymptotical stability and security of time-delay error system. Moreover, a co-design for the gain of the controller and the parameters of the adaptive event-triggering scheme is provided. Finally, an example on the tunnel diode circuit system is given to show the effectiveness and advantages of the proposed method.

This paper investigates the output feedback control for nonlinear systems with unknown output functions and unknown growth rates, where the unknown output functions are Lipschitz continuous and the unknown growth rates are time-varying. To deal with this challenging control problem, a time-varying observer is designed and a time-varying scaling transformation is introduced, which can avoid using the derivative information of the output function and can effectively compensate the unknown time-varying growth rate. Then, by combining the time-varying scheme, the backstepping method and the certainty equivalence principle, an output feedback controller is designed to guarantee the boundedness of the closed-loop system states and the global convergence of the original system states. Finally, two simulation examples are given to show the effectiveness of the control scheme.

In this article, a unified identification framework called constrained subspace method for structured state-space models (COSMOS) is presented, where the structure is defined by a user-specified linear or polynomial parametrization. The new approach operates directly from the input and output data, which differs from the traditional two-step method that first obtains a state-space realization followed by the system-parameter estimation. The new identification framework relies on a subspace inspired linear regression problem which may not yield a consistent estimate in the presence of process noise. To alleviate this problem, the linear regression formulation is imposed by structured and low-rank constraints in terms of a finite set of system Markov parameters and the user specified model parameters. The nonconvex nature of the constrained optimization problem is dealt with by transforming the problem into a difference-of-convex optimization problem, which is then handled by the sequential convex programming strategy. Numerical simulation examples show that the proposed identification method is more robust than the classical prediction-error method initialized by random initial values in converging to local minima, but at the cost of heavier computational burden. Funding Agencies|National Natural Science Foundation of China (NSFC)National Natural Science Foundation of China (NSFC) [61991414, 61873301]; National Defense Pre-Research Foundation of China [61403120304]; European Research Council under the European UnionEuropean Research Council (ERC) [339681]

The identification of structured state-space models with multi-diagonal block Teoplitz system matrices is studied in this paper. Due to the non-convex nature of the identification problem, it is difficult to obtain a global optimal solution. To deal with this problem, the concerned state-space model is recasted as an equivalent one with block circulant system matrices and a low-dimension unknown input related term. Then, the identification problem is formulated as a low rank regularized optimization problem which is solved by the sequentially convex programming method. The effectiveness of the proposed identification method is finally verified through numerical simulations.

This paper proposes a multi-hop distributed sensor network localization method based on UWB ranging measurements. Different from the positioning algorithm suitable for known indoor environments, this algorithm is designed for the exploration needs of unknown areas. Based on the barycentric coordinates of the tetrahedron, the expansion of the sensor network is realized through the multi-hop of the UWB sensor. The proposed algorithm can be applied for three-dimensional space. By designing time-varying weight coefficients and combining Taylor expansion, the localization performance is enhanced. Finally, a numerical simulation is given to verify the effectiveness of the proposed algorithm.

The paper investigates the output feedback control for nonlinear systems with non-smooth unknown output functions. The output function only needs to have a generalized derivative (which may not be derivable), and the upper and lower bounds for which may not to be known. The considered system includes more other uncertainties, for example, the integral input-to-state stable (iISS) cascade subsystem, the unknown control direction, the unmeasured states and the external disturbance. To overcome the effects caused by the external disturbance, we first treat the disturbance as an extended state, and a state observer is designed to estimate both the unavailable system states and the external disturbance. In addition, to deal with the challenge raised by the unknown control direction and the non-smooth output function, we choose a special Nussbaum function with a fast growth rate to ensure the integrability for the derivative of the selected Lyapunov function. After that, a dynamic output feedback controller is designed to drive the system states to the origin while keeping the boundedness for all other closed-loop signals. Finally, a simulation example is given to show the effectiveness of the control scheme.

The inertial measurement unit and ultra-wide band signal (IMU-UWB) combined indoor positioning system has a nonlinear state equation and a linear measurement equation. In order to improve the computational efficiency and the localization performance in terms of the estimation accuracy, the federated derivative cubature Kalman filtering (FDCKF) method is proposed by combining the traditional Kalman filtering and the cubature Kalman filtering. By implementing the proposed FDCKF method, the observations of the UWB and the IMU can be effectively fused, particularly, the IMU can be continuously calibrated by UWB so that it does not generate cumulative errors. Finally, the effectiveness of the proposed algorithm is demonstrated through numerical simulations, in which FDCKF was compared with the federated cubature Kalman filter (FCKF) and the federated unscented Kalman filter (FUKF), respectively.

This note considers the identification of large-scale one-dimensional networks consisting of identical LTI dynamical systems. A subspace identification method is developed that only uses local input-output information and does not rely on knowledge about the local state interaction. The proposed identification method estimates the Markov parameters of a locally lifted system, following the state-space realization of a single subsystem. The Markov-parameter estimation is formulated as a rank minimization problem by exploiting the low-rank property and the two-layer Toeplitz structural property in the data equation, whereas the state-space realization of a single subsystem is formulated as a structured low-rank matrix-factorization problem. The effectiveness of the proposed identification method is demonstrated by simulation examples.

Identification of structured state-space (gray-box) model is popular for modeling physical and network systems. Due to the non-convex nature of the gray-box identification problem, good initial parameter estimates are crucial for successful applications. In this paper, the non-convex gray-box identification problem is reformulated as a structured low-rank matrix factorization problem by exploiting the rank and structured properties of a block Hankel matrix constructed by the system impulse response. To address the low-rank optimization problem, it is first transformed into a difference-of-convex (DC) formulation and then solved using the sequentially convex relaxation method. Compared with the classical gray-box identification methods like the prediction-error method (PEM), the new approach turns out to be more robust against converging to non-global minima, as supported by a simulation study. The developed identification can either be directly used for gray-box identification or provide an initial parameter estimate for the PEM.

This note studies the identification of individual systems operating in a large-scale distributed network by considering the interconnection signals between neighboring systems to be unmeasurable. The unmeasurable interconnections act as unknown system inputs to the individual systems in a network, which poses a challenge for the identification problem. A subspace identification framework is proposed in this note for the consistent identification of individual systems using only local input and output information. The key step of this identification framework is the accurate estimation of the unknown system inputs of individual systems using local observations. Sufficient identifiability conditions are provided for the proposed identification framework and a simulation example is given to demonstrate its performance.

The objective of adaptive optics (AO) system control is to design an output feedback controller to reduce the adverse effect of the phase aberration caused by the atmospheric turbulence. As the size of the telescope or AO system becomes larger and larger, how to improve the efficiency of the controller execution becomes an urgent but challenging problem. To this end, this paper presents a structured and sparse controller design method for the large-scale AO systems. A Kronecker structured turbulent phase model, inspired by the frozen-flow movement of the atmospheric turbulence, is developed first, following the design of a sparse controller gain under the $H_{2}$ -norm optimal control framework. Based on the Kronecker structured system matrices and the sparse controller gain, the obtained dynamical controller has a linear execution complexity in the dimension of the turbulent phase, which is even lower than the standard matrix-vector multiplication method. Since the proposed method is a preliminary result, which cannot be directly used in a telescope today, its performance is demonstrated by numerical simulations only.

The identification of affinely parameterized state–space system models is quite popular to model practical physical systems or networked systems, and the traditional identification methods require the measurements of both the input and output data. However, in the presence of partial unknown input, the corresponding system identification problem turns out to be challenging and sometimes unidentifiable. This paper provides the identifiability conditions in terms of the structural properties of the state–space model and presents an identification method which successively estimates the system states and the affinely parameterized system matrices. The estimation of the system matrices boils down to solving a bilinear optimization problem, which is reformulated as a difference-of-convex (DC) optimization problem and handled by the sequential convex programming method. The effectiveness of the proposed identification method is demonstrated numerically by comparing with the Gauss–Newton method and the sequential quadratic programming method.

This paper presents a distributed identification algorithm for large-scale MISO output-error systems. The considered MISO system is decomposed into multiple SISO output-error systems with their outputs being summed up to the measurable system output, and each SISO subsystem performs recursive identification by exchanging parameter estimates with other subsystems. Using the Lyapunov theory, it is proven that the proposed distributed identification algorithm converges asymptotically. Finally, a simulation example is given to demonstrate the effectiveness of the proposed algorithm.

This paper investigates the quantized feedback control for nonlinear feedforward systems with unknown control coefficients and unknown growth rates. The unknown control coefficients are assumed to be bounded, and the unknown growth rate is time-varying. Based on the wellknown backstepping method and the sector bound approach, a time-varying quantized feedback controller calculated from the quantized output is designed, which can achieve the boundedness and convergence of the closed-loop system states. Moreover, a guideline is provided for choosing the parameters of the quantizers. Finally, a simulation example is given to show the effectiveness of the control scheme.

The inverse optimal control for finite-horizon discrete-time linear quadratic regulators is investigated in this paper, which is to estimate the parameters in the objective function using noisy measurements of partial optimal states only. By the Pontryagin’s minimum principle, the concerned inverse optimal control problem is recast as the identification of a parameterized causal-and-anticausal mixed system excited by boundary conditions. Sufficient identifiability conditions for the unknown parameters are provided in terms of the system model itself, rather than relying on the exact values of optimal states or control inputs. In addition, an elegant algebraic solution is provided for the concerned identification problem that is inherently a challenging optimization problem with trilinear equality constraints, and it can recover the true parameters (up to a scalar ambiguity) in the absence of measurement noise or can consistently identify the parameters (up to a scalar ambiguity) in the presence of white measurement noise. The presented algebraic solution relies on recursive matrix calculations so that its computational burden is much less than directly solving a high-dimensional non-convex optimization problem as done in many existing works. The effectiveness of the proposed method as well as its noise sensitivity issue is illustrated by simulation examples.

This note studies the identification of a network comprised of interconnected clusters of LTI systems. Each cluster consists of homogeneous dynamical systems, and its interconnections with the rest of the network are unmeasurable. A subspace identification method is proposed for identifying a single cluster using only local input and output data. With the topology of the concerned cluster being available, all the LTI systems within the cluster are decoupled by taking a transformation on the state, input and output data. To deal with the unmeasurable interconnections between the concerned cluster and the rest of the network, the Markov parameters of the decoupled LTI systems are identified first by solving a nuclear-norm regularized convex optimization, following the state-space realization of a single LTI system within the cluster by solving another nuclear-norm regularized optimization problem. The effectiveness of the proposed identification method is demonstrated by a simulation example.

The concerned phase retrieval problem is to estimate a vector from a number of magnitude (phaseless) measurements. Due to the non-convexity of the phase retrieval problem, the semi-definite relaxation technique is usually adopted, which enables the non-convex phase retrieval problem to be addressed by semi-definite constrained optimization. Following the spirit of the classic system identification, this paper shows that the phase retrieval problem can be addressed by solving a leastsquares optimization problem only, without the semi-definite constraint. A sufficient condition for the exact phase retrieval is provided, which is analogous to the signal persistent excitation in the system identification field. In view of the connection between the phase-retrieval problem and the Wiener system identification problem, the provided solution is extended to solve the Wiener system identification problem with an absolute operator at the system output. Finally, the effectiveness of the presented algorithm is demonstrated by numerical simulations.

Using Maximum Likelihood (or Prediction Error) methods to identify linear state space model is a prime technique. The likelihood function is a nonconvex function and care must be exercised in the numerical maximization. Here the focus will be on affine parameterizations which allow some special techniques and algorithms. Three approaches to formulate and perform the maximization are described in this contribution: (1) The standard and well known Gauss-Newton iterative search, (2) a scheme based on the EM (expectation-maximization) technique, which becomes especially simple in the affine parameterization case, and (3) a new approach based on lifting the problem to a higher dimension in the parameter space and introducing rank constraints.

The identification of a 1D heterogenous network with unmeasurable interconnections between neighboring systems is studied in this paper. For a large-scale networked system, it is usually computationally prohibitive to identify the global system in a centralized manner. To cope with this problem, the local identification of a network using local input-output data is considered in this paper, and a subspace identification method is developed for the identification of individual systems operating in the network. A simulation example is given to validate the proposed identification method.

This paper considers the identification of a network consisting of discrete-time LTI systems that are interconnected by their unmeasurable states. For a large-scale network, the computational burden prevents a centralized solution. To cope with this problem, a subspace-based local identification approach using local observations is presented, which consists of subspace intersection operations in both the temporal and spatial domains. Sufficient conditions are provided for the consistent identification of the presented identification approach. Finally, the implementation of this approach on 1D network is specially investigated and numerical simulations are provided to show its effectiveness.

In this paper, we study the deterministic blind identification of multiple channel state-space models having a common unknown input using measured output signals that are perturbed by additive white noise sequences. Different from traditional blind identification problems, the considered system is an autoregressive system rather than an FIR system; hence, the concerned identification problem is more challenging but possibly having a wider scope of application. Two blind identification methods are presented for multi-channel autoregressive systems. A cross-relation identification method is developed by exploiting the mutual references among different channels. It requires at least three channel systems with square and stably invertible transfer matrices. Moreover, a general subspace identification method is developed for which two channel systems are sufficient for the blind identification; however, it requires the additive noises to have identical variances and the transfer matrices having no transmission zeros. Finally, numerical simulations are carried out to demonstrate the performance of the proposed identification algorithms.

This paper studies the identification of ARMA systems with colored measurement noises using finite-level quantized observations. Compared with the case under colorless noises, this problem is more challenging. Our approach is to jointly design an adaptive quantizer and a recursive estimator to identify system parameters. Specifically, the quantizer uses the latest estimate to adjust its thresholds, and the estimator is updated by using quantized observations. To accommodate the temporal correlations of quantization errors and measurement noises, we construct a second-order statistics equivalent system, from which the original ARMA system is identified. The associated identifiability problem and convergence are analyzed as well. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.