This paper investigates the identification problem for finite impulse response (FIR) systems with binary-valued observations under event-triggered communication mechanism and data packet dropout. The challenge lies in the inability to distinguish between untriggered events and packet loss when no information is received, which prevents us from obtaining the statistical properties of the binary-valued sequence. A compensation-oriented difference-driven identification (CODD) algorithm is proposed to estimate the parameter by recovering the mean of the original binary-valued sequence, where different values for the observation estimates are assigned when receiving 0, 1 or nothing. Even though, the convergence analysis of the parameter estimate is still challenging since the assigned values are dependent. To tackle this difficulty, the estimate error is divided into two parts: an initial assigned value related part, which is demonstrated to be convergent through the construction of an auxiliary set, and the remaining component, which happens to be a convergent martingale-difference sequence. As a result, the almost sure convergence and the asymptotic normality of the CODD algorithm are established when data packet loss probability is less than [Formula presented]. By calculating the communication rate, it is proven that the difference-driven mechanism can save 50% of the communication cost compared to original binary-valued systems. Furthermore, when data packet loss probability is high, an m-channel compensation-oriented identification (m-CODD) algorithm is constructed by utilizing retransmission of the each observation for m times, which is designed based on the packet loss probability. The properties of m-CODD algorithm including convergence, asymptotic normality and communication rate are established. Numerical simulations are illustrated to show the theoretical results.

This paper investigates the adaptive tracking control problem for AutoRegressive Moving Average (ARMA) systems with quantized observations, explicitly focusing on reference signals composed of non-periodic sequences. The authors propose an adaptive tracking control scheme integrating an adaptive controller with a stochastic approximation-type estimation algorithm. Different from the control scheme for Finite Impulse Response (FIR) systems, the estimation part not only estimates the unknown system parameters but also the unknown system outputs. Next, based on the certainty equivalent principle, the adaptive controller is designed using the above two estimates instead of the actual parameters and system outputs. To tackle the inherent coupling between the two estimates, the authors introduce a novel approach that combines the Lyapunov function method with a backward-shifted polynomial method featuring time-varying coefficients. This approach assists in establishing the mean square convergence of the estimates with a convergence rate of O(1k) under suitable conditions of the step size coefficient. Additionally, this paper shows that the designed adaptive control law can achieve asymptotically optimal tracking of non-periodic reference signals in the mean square sense. Finally, a numerical simulation is presented to validate the theoretical results obtained in this paper.

This paper addresses distributed parameter estimation in stochastic dynamic systems with quantized measurements, constrained by quantized communication and Markovian switching directed topologies. To enable accurate recovery of the original signal from quantized communication signal, a persistent excitation-compliant linear compression encoding method is introduced. Leveraging this encoding, this paper proposes an estimation-fusion type quantized distributed identification algorithm under a stochastic approximation framework. The algorithm operates in two phases: first, it estimates neighboring estimates using quantized communication information, then it creates a fusion estimate by combining these estimates through a consensus-based distributed stochastic approximation approach. To tackle the difficulty caused by the coupling between these two estimates, two combined Lyapunov functions are constructed to analyze the convergence performance. Specifically, the mean-square convergence of the estimates is established under a conditional expectation-type cooperative excitation condition and the union topology containing a spanning tree. Besides, the convergence rate is derived to match the step size's order under suitable step-size coefficients. Furthermore, the impact of communication uncertainties including stochastic communication noise and Markov-switching rate is analyzed on the convergence rate. A numerical example illustrates the theoretical findings and highlights the joint effect of sensors under quantized communication.

This paper investigates joint estimation of the system parameters and packet loss rate for finite impulse response (FIR) systems with binary-valued observations under event-triggered communication and packet loss. To address the trade-off between identifying system parameters, unknown packet loss rate and minimizing communication cost, switching difference-driven communication mechanism is proposed, where the data transmission switches between two strategies. One mode maintains continuous communication to estimate the unknown packet loss rate, while the other follows the difference-driven communication rule to reduce communication cost. Based on this, a joint compensation difference-driven algorithm is developed to jointly estimate the system parameters and the packet loss rate, which is proved to achieve almost sure convergence and asymptotic normality. Besides, the communication rate of the proposed algorithm is characterized. An optimization strategy for data transmission is further formulated to minimize the communication rate while ensuring convergence performance, yielding an optimal rule for selecting transmitted data for packet loss rate estimation. This provides a practical guideline for balancing estimation performance and communication cost in networked systems. Numerical simulations are illustrated to show the theoretical results.

This paper addresses the one-bit consensus of controllable linear multi-agent systems (MASs) with communication noises. A consensus algorithm consisting of a communication protocol and a consensus controller is designed. The communication protocol introduces a linear compression encoding function to achieve a one-bit data rate, thereby saving communication costs. The consensus controller with a stabilization term and a consensus term is proposed to ensure the consensus of a potentially unstable but controllable MAS. Specifically, in the consensus term, we adopt an estimation method to overcome the information loss caused by one-bit communications and a decay step to attenuate the effect of communication noise. Two combined Lyapunov functions are constructed to overcome the difficulty arising from the coupling of the control and estimation. By establishing similar iterative structures of these two functions, this paper shows that the MAS can achieve consensus in the mean square sense at the rate of the reciprocal of the iteration number under the case with a connected fixed topology. Moreover, the theoretical results are generalized to the case with jointly connected Markovian switching topologies by establishing a certain equivalence relationship between the Markovian switching topologies and a fixed topology. Two simulation examples are given to validate the algorithm.

This paper studies the consensus problem of high-order multi-agent systems (MASs) with binary-valued communications and switching topologies. To tackle the challenge of unknown states caused by binary-valued communications, this paper constructs an estimation-based consensus algorithm. First, a recursive projection identification algorithm is presented to estimate the neighbors' states dynamically. Then, based on these estimates, a consensus law is designed. By constructing and analyzing two combined Lyapunov functions about estimation error and state error, this paper establishes their relation, to overcome the difficulty resulting from the coupling of the estimation and control and less information due to switching topologies. Under the condition of jointly connected topologies, it is proven that by properly selecting the step coefficient, the estimates of states can converge to the true states with a convergence rate as the reciprocal of the recursion times. Besides, the MAS is proved to achieve weak consensus and the consensus rate is also established as the reciprocal of the recursion times. Finally, a simulation example is given to validate the algorithm.

This letter investigates the one-bit consensus control of multi-agent systems (MASs) with independent and identically distributed and Markovian packet loss. To explore the impact of packet loss on one-bit communication, this letter first quantitatively characterizes the information loss of one-bit communications caused by packet loss, which provides the proportional relationship between one-bit data with and without packet loss in the sense of expectation. Based on quantitative characterizations, a one-bit packet loss consensus algorithm with a packet loss proportional coefficient is proposed to compensate for the information loss, where the coefficient is designed as the reciprocal of the information loss proportion. Furthermore, this letter demonstrates that the proposed algorithm enables the MAS to achieve one-bit consensus in the mean square sense at a rate of O(1/t) with packet loss. Two simulation examples are given to validate the algorithm.

This paper focuses on the problem of quantized distributed estimation with event-triggered communication and packet loss, aiming to reduce the number of transmitted bits. The main challenge lies in the inability to differentiate between an untriggered event and a packet loss occurrence. This paper proposes an event-triggered distributed estimation algorithm with quantized communication and quantized measurement, in which it introduces a one-bit information reconstruction method to deal with packet loss. The almost sure convergence and convergence rate of the proposed algorithm are established. Besides, it is demonstrated that the global average communication bit-rate decreases to zero over time. Moreover, the trade-off between communication rate and convergence rate is revealed, providing guidance for designing the communication rate required to achieve the algorithm’s convergence rate. A numerical example is supplied to validate the findings.

This paper is concerned with parameter identification problem for finite impulse response (FIR) systems with binary-valued observations under low computational complexity. Most of the existing algorithms under binary-valued observations rely on projection operators, which leads to a high computational complexity of much higher than On². In response, this paper introduces a recursive projection-free identification algorithm that incorporates a specialized cut-off coefficient to fully utilize prior information, thereby eliminating the need for projection operators. The algorithm is proved to be mean square and almost surely convergent. Furthermore, to better leverage prior information, an adaptive accelerated coefficient is introduced, resulting in a mean square convergence rate of [Formula presented], which matches the convergence rate with accurate observations. Inspired by the structure of the Cramér–Rao lower bound, the algorithm can be extended to an information-matrix projection-free algorithm by designing adaptive weight coefficients. This extension is proved to be asymptotically efficient for first-order FIR systems, with simulations indicating similar results for high-order FIR systems. Finally, numerical examples are provided to demonstrate the main results.