Logarithmic Quantizer Research Articles

Asymptotical Synchronization of Drive-Response Networks by Sample-Data-Based Event-Triggered Control with Quantization and Cyber-Attacks

This paper addresses the asymptotic synchronization problem for a kind of drive-response complex networks (DRCNs) under cyber-attacks by using network control systems (NCSs). In order to reduce the pressure of communication and save the communication bandwidth on NCSs, some sampled-data-based event-triggered synchronization feedback controllers and logarithmic quantizers are designed by taking into account the effect of the NCSs’ transmission delays. Using Lyapunov stability theories, several sufficient conditions are obtained to guarantee the existence of sampleddata- based event-triggered synchronization controllers for the DRCNs with distributed-delay. Then, the state feedback gains are obtained by solving certain linear matrix inequalities (LMIs). Finally, a numerical example is provided to illustrate the effectiveness of the sampled-data-based eventtriggered control scheme.

Formula omitted] output synchronization of directed coupled reaction-diffusion neural networks via event-triggered quantized control

By designing a quantized controller based on event trigger, this paper considers the problem of H∞ output synchronization for coupled neural networks with reaction-diffusion term and directed topology. Firstly, in this hybrid control strategy, the data is sampled in time domain to exclude the Zeno-behavior before judging whether an event is triggered, and then the event-triggered data instead of the sampling data itself is quantized by a logarithmic quantizer. Secondly, some sufficient conditions for H∞ output synchronization are obtained, in which the dimension of these conditions can be reduced to only depend on the number of neurons, but not on the number of nodes. Finally, a numerical example is given to verify the theoretical results.

Quasisynchronization for Neural Networks With Partial Constrained State Information via Intermittent Control Approach.

This work addresses quasisynchronization (QS) of the master-slave (MS) neural networks (NNs) with mismatched parameters. The logarithmic quantizer and the round-robin protocol (RRP) are used to deal with the limited communication channel (CC) capacity, then the intermittent control strategy is employed to improve the efficiency of CC and the controller. A transmission-dependent controller is designed, and the synchronization error system (SES) is established. The QS with a boundary is ensured for the MS NNs by a developed sufficient condition, and the controller design method is given. A numerical simulation is given to show the effectiveness of the obtained method.

Synchronization for Quantized Semi-Markov Switching Neural Networks in a Finite Time.

Finite-time synchronization (FTS) is discussed for delayed semi-Markov switching neural networks (S-MSNNs) with quantized measurement, in which a logarithmic quantizer is employed. The stochastic phenomena of structural and parametrical changes are modeled by a semi-Markov process whose transition rates are time-varying to depend on the sojourn time. Practical systems subject to unpredictable structural changes, such as quadruple-tank process systems, are described by delayed S-MSNNs. A key issue under the consideration is how to design a feedback controller to guarantee the FTS between the master system and the slave system. For this purpose, by using the weak infinitesimal operator, sufficient conditions are constructed to realize FTS of the resulting error system over a finite-time interval. Then, the solvability conditions for the desired finite-time controller can be determined under a linear matrix inequality framework. Finally, the theoretical findings are illustrated by the quadruple-tank process model.

Observer-Based Finite-Time $H_\infty$ Control for Interconnected Fuzzy Systems With Quantization and Random Network Attacks

This article investigates the observer-based finite-time $H_\infty$ control problem for interconnected fuzzy systems with quantization and random network attacks, where two types of network attacks including denial-of-service (DoS) and fault data injection (FDI) attacks are considered. In order to achieve the occupancy reduction of network resources, the measured outputs of interconnected systems are quantized through a logarithmic quantizer before being transmitted by the communication channel. Combining the effects of quantization and random network attacks, an observer-based control model is first constructed. Then, based on Lyapunov functional approach and stochastic analysis technique, some sufficient conditions are presented such that the considered closed-loop interconnected system is stochastically finite-time bounded with a prescribed disturbance attenuation level, and the gain matrices of fuzzy observer and fuzzy controller can be found by solving an optimization algorithm with linear matrix inequalities constraints. Finally, two simulation examples are given to illustrate the validity of the proposed approach.

Resilient H-infinity filtering for networked nonlinear Markovian jump systems with randomly occurring distributed delay and sensor saturation

The H∞ filtering problem for a class of networked nonlinear Markovian jump systems subject to randomly occurring distributed delays, nonlinearities, quantization effects, missing measurements and sensor saturation is investigated in this paper. The measurement missing phenomenon is characterized via a random variable obeying the Bernoulli stochastic distribution. Moreover, due to bandwidth limitations, the measurement output is quantized using a logarithmic quantizer and then transmitted to the filter. Further, the output measurements are affected by sensor saturation since the communication links between the system and the filter are unreliable and is described by sector nonlinearities. The objective of this work is to design a quantized resilient filter that guarantees not only the stochastic stability of the augmented filtering error system but also a prespecified level of H∞ performance. Sufficient conditions for the existence of desired filter are established with the aid of proper Lyapunov–Krasovskii functional and linear matrix inequality approach together with stochastic analysis theory. Finally, a numerical example is presented to validate the developed theoretical results.

Quantized Feedback Control Based on Spherical Polar Coordinate Quantizer

Different from uniform quantizer and logarithmic quantizer, this article proposes a novel quantizer for the design problems of linear systems with quantized feedback. This quantizer is based on spherical polar coordinates and has a desired relation between the quantized vector and the corresponding quantization error. Utilizing the proposed quantizer with infinite data rate, the quadratic stabilization problems of linear systems via state feedback under quantization can be converted, respectively, to the same problems of the corresponding robust control systems with sector bound uncertainties. From a practical point of view, we are more concerned with quantized feedback control with finite data rate, so a quantizer with finite data rate is proposed for the abovementioned problems. Different from the quantizer with infinite data rate, a time-varying bounded quantization region needs to be determined to contain the quantized vector as the system evolves. Under this circumstance, the vector needed to be quantized cannot be quantized directly, so we give a quantization method to solve this problem.

Adaptive Control with Quantized Inputs Processed by Lipschitz Logarithmic Quantizer

In this paper, the design of adaptive control for a class of control systems with quantized inputs is investigated. According to the stability analysis result of adaptive control design, the quantized errors act as a great potential unstable risk for a class of sophisticated multi-channel system with quantized inputs. Thus, we establish Lipschitz logarithmic quantizer considering package loss problem in data transmission to eliminate quantized errors, and the superiority of this new quantizer is then proved by the asymptotically convergent mathematic expectation of quantized errors. Especially, a typical under-actuated multi-channel system, quadrotor UAV, is introduced to exhibit the effectiveness of the whole control scheme and the advancement compared with previous quantized control designs.

Recursive filtering for stochastic parameter systems with measurement quantizations and packet disorders

In this paper, the recursive filtering problem is put forward for stochastic parameter systems subject to quantization effects and packet disorders. Before entering communication networks, measurement outputs are quantized by logarithmic quantizers. The packet disorders result from transmission delays which are provoked by communication constraints and occur randomly in the sensor-to-filter channel. In case of measurement quantizations and packet disorders, the objective of this paper is to devise a novel recursive filter approach that is capable of 1) guaranteeing desired upper bounds on the resultant filtering error covariances; and 2) minimizing such upper bounds by acquiring appropriate filter gains. Furthermore, sufficient conditions are established to ensure the mean-square boundedness of filtering errors by means of stochastic analysis techniques. At last, simulations are given to validate the applicability of our designed approach.

Static Output Feedback Quantized Control for Fuzzy Markovian Switching Singularly Perturbed Systems With Deception Attacks

This article focuses on static output feedback control for fuzzy Markovian switching singularly perturbed systems (FMSSPSs) with deception attacks and asynchronous quantized measurement output. Different from the previous work, both the logarithmic quantizer and the static output feedback controller are dependent on the operation system; by means of hidden Markov models, their modes run asynchronously with that of FMSSPSs. Additionally, the deception attacks are guided by a Bernoulli variable, and nonlinear characteristics are modeled by the Takagi–Sugeno fuzzy model. By resorting to a mode-dependent Lyapunov functional, several criteria are acquired and strictly <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\mathscr {Q},\mathscr {S},\mathscr {R})\text{--}\gamma$</tex-math></inline-formula> -dissipative of FMSSPSs can be ensured. Finally, a dc motor model is expressed to illustrate the effectiveness of the asynchronous control scheme.

MXQN:Mixed quantization for reducing bit-width of weights and activations in deep convolutional neural networks

Quantization, which involves bit-width reduction, is considered as one of the most effective approaches to rapidly and energy-efficiently deploy deep convolutional neural networks (DCNNs) on resource-constrained embedded hardware. However, bit-width reduction on the weights and activations of DCNNs seriously degrades accuracy. To solve this problem, in this paper we propose a mixed hardware-friendly quantization (MXQN) method that applies fixed-point quantization and logarithmic quantization for DCNNs without the necessity to retrain and fine-tune the DCNN. Our MXQN algorithm is a multi-staged process where, first, we employ a signal-to-quantization-noise ratio (SQNR) process as the metric to estimate the interplay between the parameter quantization errors of each layer and the overall model prediction accuracy. Then, we utilize a fixed-point quantization process to quantize weights, and depending on the SQNR metric we empirically select either a logarithmic or a fixed-point quantization process to quantize activations. For improved accuracy, we propose an optimized logarithmic quantization scheme that affords a fine-grained step size. We evaluate the performance of MXQN utilizing the VGG16 network on the MNIST, CIFAR-10, CIFAR-100, and the ImageNet datasets, as well as VGG19 and ResNet (ResNet18, ResNet34, ResNet50) networks on the ImageNet, and demonstrate that the MXQN-quantized DCNN despite not being retrained and fine-tuned, it still achieves high accuracy close to the original DCNN.

Design and Experimental Development of Wireless Iterative Learning Fault Estimation Algorithm With Quantization and Packet Losses

In this paper, a wireless iterative learning fault estimation algorithm (WILFEA) is proposed and validated experimentally with the aim to achieve perfect tracking of a prescribed reference trajectory for systems with packet losses and quantizer measurements that operate repetitively. First, state variables, Markov chain process of random packet losses, and a logarithmic quantizer are considered to establish an extended-state-space system model. Next, based on this model, sufficient conditions for linear repetitive processes are developed with the Lyapunov-Krasovskii technique and $H_{\infty }$ approach is applied to calculate the observer gain and the learning gain. Then, WILFEA based fault estimation is constructed. Compared with the existing methods, the proposed WILFEA improves the fault estimation performance in the current iteration by consider both state error and fault estimation error. Finally, the simulation and experimental results are used for DC-Servomotor system to illustrate the effectiveness of the proposed approach using Matlab/simulink software, LabVIEW Software, ZigBee Xbee and Arduino board.

Adaptive Fixed-Time 6-DOF Coordinated Control of Multiple Spacecraft Formation Flying with Input Quantization

This paper investigates the fixed-time coordinated control problem of six-degree-of-freedom (6-DOF) dynamic model for multiple spacecraft formation flying (SFF) with input quantization, where the communication topology is assumed directed. Firstly, a new multispacecraft nonsingular fixed-time terminal sliding mode vector is derived by using neighborhood state information. Secondly, a hysteretic quantizer is utilized to quantify control force and torque. Utilizing such a quantizer not only can reduce the required communication rate but also can eliminate the control chattering phenomenon induced by the logarithmic quantizer. Thirdly, a 6-DOF fixed-time coordinated control strategy with adaptive tuning laws is proposed, such that the practical fixed-time stability of the controlled system is ensured in the presence of both upper bounds of unknown external disturbances. It theoretically proves that the relative tracking errors of attitude and position can converge into the regions in a fixed time. Finally, a numerical example is exploited to show the usefulness of the theoretical results.

Observer‐based distributed hybrid‐triggered H ∞ control for sensor networked systems with input quantisation

In this technical note, the problem of observer-based controller design is considered for sensor networked systems subject to distributed hybrid-triggered transmission scheme and input quantisation. A novel observer model based on coupled transmission signals from multi-channel is first proposed to estimate unavailable state variables and exploited in the feedback protocol. In order to provide a trade-off between saving network resources and improving system performance, a distributed hybrid-triggered transmission scheme with communication delays is adopted to update the observer input signals. For the sake of further reducing the occupancy of network resources, the control input signal is quantised by a logarithmic quantiser. Then, by making use of the Lyapunov stability theory, the sufficient conditions guaranteeing the system asymptotically stable are presented. Moreover, the explicit expressions of observer and controller parameters are achieved by solving a set of linear matrix inequalities. Finally, two simulation examples are provided to illustrate the rationality of the proposed control method.

A Memory-Efficient CNN Accelerator Using Segmented Logarithmic Quantization and Multi-Cluster Architecture

This brief presents a memory-efficient CNN accelerator design for resource-constrained devices in Internet of Things (IoT) and autonomous systems. A segmented logarithmic (SegLog) quantization method is exploited to mitigate the on-chip memory and bandwidth requirements, thus accommodating more processing elements (PEs) in a given chip area to organize a reconfigurable multi-cluster architecture. The evaluation results show that SegLog quantization can achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.4\times $ </tex-math></inline-formula> model compression with less than 2.5% accuracy loss on various CNNs. An ASIC implementation with 168 PEs configuration is validated in a 40-nm CMOS process, with 2.54 TOPs/W energy efficiency and 0.8 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> chip area reported. The accelerator has also been implemented on FPGA with 1512 PEs configured and 468 kB on-chip memory, achieving a 1.29 GOPs/kB memory efficiency. Compared with the state-of-the-art accelerators, our ASIC implementation enhances area efficiency and arithmetic intensity by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.94\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.62\times $ </tex-math></inline-formula> , while the FPGA implementation achieves the memory efficiency improvement by a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.34\times $ </tex-math></inline-formula> .

Periodic Event-Triggered Control for Linear Systems in the Presence of Cone-Bounded Nonlinear Inputs: A Discrete-Time Approach

This paper presents an observer-based periodic event-triggered strategy for linear systems subject to input cone-bounded nonlinearities. Considering a discrete-time framework, conditions in the form of linear matrix inequalities are derived to ensure global or regional stability of the origin of the closed-loop system under the event-triggered control strategy. These conditions are cast into convex optimization problems to determine the event-triggering function parameters, aiming at reducing the number of control updates with respect to periodic implementations. Both the emulation and the co-design problems are addressed. Numerical examples with logarithmic quantization and saturation nonlinearities are presented to illustrate the method.

An event-triggered recursive state estimation approach for time-varying nonlinear complex networks with quantization effects

This paper proposes a novel robust extended Kalman filter (REKF) for a class of discrete time-varying nonlinear complex networks with event-triggered communication and quantization effects. In this approach, it is assumed that each sensor transmits data to its estimator over two redundant communication channels. Before the data from each sensor is sent to its estimator, an event-triggered strategy is employed to reduce the wastage of communication bandwidth and unnecessary executions. Afterwards, a logarithmic quantizer is implemented to quantize data. The estimator parameters for each node are derived separately into two Riccati-like difference equations so that the achieved upper bound is minimized by the proposed estimator parameters. Finally, the simulation results are included to illustrate the performance of the derived filtering method.

Global Stabilization of Memristive Neural Networks with Leakage and Time-Varying Delays Via Quantized Sliding-Mode Controller

This pape investigates the global stabilization of memristive neural networks (MNNs) with leakage and time-varying delays via quantized sliding-mode controller. The leakage delay is considered in the MNNs. Sliding mode controller is imported to ensure global stabilization of delayed MNNs. We also introduce two quantization schemes with uniform quantizer and logarithmic quantizer. Our goal is to deal with errors before and after quantization. We give some simulations and comparisons between two quantizers in the end of this paper.

Observer-based control for singular Markov jump systems with actuator saturation and time-varying transition rates

This paper investigates the issue of observer-based control for singular Markov jump systems (SMJSs) with time-varying delay, actuator saturation and time-varying transition rates. Firstly, the state feedback controller is proposed, which based on the state observer. The time-varying transition rates are quantized by the logarithmic quantizer. In addition, the appropriate Lyapunov function is considered to ensure that the original system and the error system are singular stochastic admissible (SSA) with mixed H∞ and passivity performance. Finally, four examples are provided to expound the consistency of the technique.

Synchronization of Switched Discrete-Time Neural Networks via Quantized Output Control With Actuator Fault.

This article considers global exponential synchronization almost surely (GES a.s.) for a class of switched discrete-time neural networks (DTNNs). The considered system switches from one mode to another according to transition probability (TP) and evolves with mode-dependent average dwell time (MDADT), i.e., TP-based MDADT switching, which is more practical than classical average dwell time (ADT) switching. The logarithmic quantization technique is utilized to design mode-dependent quantized output controllers (QOCs). Noticing that external perturbations are unavoidable, actuator fault (AF) is also considered. New Lyapunov-Krasovskii functionals and analytical techniques are developed to obtain sufficient conditions to guarantee the GES a.s. It is discovered that the TP matrix plays an important role in achieving the GES a.s., the upper bound of the dwell time (DT) of unsynchronized subsystems can be very large, and the lower bound of the DT of synchronized subsystems can be very small. An algorithm is given to design the control gains, and an optimal algorithm is provided for reducing conservatism of the given results. Numerical examples demonstrate the effectiveness and the merits of the theoretical analysis.