Search

This paper designs a novel nonsingular terminal sliding mode control (TSMC) scheme for a class of nonlinear systems under binary encoding transmission. To further ease the communication overheads between the plant and the controller, a dynamic event-triggered mechanism is also introduced to the TSMC design. Sufficient conditions are proposed, respectively, for guaranteeing the reachability to a practical sliding mode and the ultimate boundedness of the closed-loop system, in which the effects from the binary encoding and the dynamic event-triggered protocol are quantified clearly. The Zeno phenomena for the developed dynamic event-triggered mechanism is excluded via an explicit analysis. Finally, the feasibility and effectiveness of the proposed scheme are demonstrated by a numerical example and a real experiment on permanent magnet synchronous motor speed regulation system. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered optimal tracking control of discrete-time multi-agent systems is addressed by using reinforcement learning. In contrast to traditional reinforcement learning-based methods for optimal coordination and control of multi-agent systems with a time-triggered control mechanism, an event-triggered mechanism is proposed to update the controller only when the designed events are triggered, which reduces the computational burden and transmission load. The stability analysis of the closed-loop multi-agent systems with event-triggered controller is described. Further, to implement the proposed scheme, an actor-critic neural network learning structure is proposed to approximate performance indices and to on-line learn the event-triggered optimal control. During the training process, event-triggered weight tuning law has been designed, wherein the weight parameters of the actor neural networks are adjusted only during triggering instances compared with traditional methods with fixed updating periods. Further, a convergence analysis of the actor-critic neural network is provided via Lyapunov method. Finally, two simulation examples show the effectiveness and performance of the obtained event-triggered reinforcement learning controller. [ABSTRACT FROM AUTHOR]

The ultra-low hardware consumption feature of stochastic decoding has made it a potential candidate for the implementation of low-density parity-check(LDPC) decoders. However, the existing stochastic LDPC decoders still suffer from performance degradation and relatively high decoding cycles caused by the correlation among stochastic bit streams. In this paper, we propose Hybrid Stochastic(HS) decoding, which achieves high performance, high throughput, and high hardware efficiency by jointly using our proposed novel stochastic check node(CN) and Two’s Complement(TCS) variable node(VN) to realize Min-Sum Algorithm(MSA) and its enhancements. Fully correlated stochastic bit streams are used to entirely eliminate the indeterminacy caused by the correlation, which results in high performance and fast convergence and inherits the low complexity of stochastic decoders at the same time. We demonstrate the HS decoding by designing a (2048,1723) decoder in a 65 nm process, which achieves the highest Bit-Error-Ratio(BER) performance, highest throughput, and top hardware efficiency among existing stochastic LDPC decoders. We also demonstrate that HS decoding can achieve excellent decoding performance for different code rates and lengths 5G New Radio(NR) LDPC codes. Thus, HS decoding can be adopted in wide applications. [ABSTRACT FROM AUTHOR]

High-quality data transmission synchronization process is frequently expected in light of secure communication mechanisms (SCMs), especially for space laser communication among the satellite constellation. To improve security and reliability during the data transmission processes prominently, control problems of chaotic synchronization synchronously at the same time are explored. In this paper, several novel error synchronization control protocols are proposed to solve these problems. First, by introducing a norm-normalized sign function (NNSF), unique (fixed-) time-synchronized stability is manifested, such that all non-zero state elements reach the origin synchronously at the same time. And upper bounds of synchronous resident time calculated by offered protocols are irrelevant/relevant to initial states of the error systems. Second, integrated with the (fixed-) time-synchronized stability theories, the (fixed-) time-synchronized sliding mode controllers with special convergent performance are established for two representative types of chaotic systems. Third, the ratio-persistent performance plays a dominating role for simultaneous convergence of the errors. Further, the innovation of the algorithms is reflected in that the decrypted signal is completely consistent with the transmitted message signal within synchronized settling time. Finally, in the simulation, not only theoretical verifications, but also practical verifications of image encryption and decryption processes verify the effectiveness of the SCMs. [ABSTRACT FROM AUTHOR]

One key factor affecting the distributed Nash equilibrium (NE) seeking in aggregative games is the unbalanced communication structure for multiple players. Although some results on seeking NE over undirected or weight-balanced graphs were established, how to address the distributed NE seeking problem over general directed communication graphs is still an outstanding challenge. This paper addresses the NE seeking problem for a class of aggregative games with general directed communication graphs. To achieve this objective, two new kinds of distributed discrete-time NE seeking algorithms are developed for aggregative games over fixed digraphs and time-varying digraphs, respectively. In particular, motivated by the heavy-ball method in optimization studies, a momentum term is introduced to the update law of the players’ actions and it is numerically verified that this momentum term accelerates the convergence of the proposed algorithms. For both strongly connected fixed graph and $B$ -strongly connected time-varying graph, it is theoretically proved that the actions of players will converge to the NE of aggregative games for the case of decreasing step-size implemented by the proposed NE seeking algorithms if the cost functions and the aggregation of players satisfy some certain conditions. Finally, the developed NE seeking algorithms are applied to the energy consumption control of plug-in hybrid electric vehicles (PHEVs), which demonstrates the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time stabilization problem for a class of nonlinear systems via event-triggered control. The event-triggered mechanism can be applied to the nonlinear system with the coexistence of low-order and high-order nonlinearities. In order to deal with these low-order and high-order terms as well as achieve the objective of fixed-time stabilization, the initial value of the system is divided into two cases, and then the event-triggered controller is designed, respectively. By switching control theory, it is proved that the nonlinear system is globally fixed-time stable under the designed controller and the Zeno behavior can be excluded. Finally, two simulations show that the proposed technology is effective. [ABSTRACT FROM AUTHOR]

This paper proposes a novel all-digital background calibration of timing mismatch for a time-interleaved analog-to-digital converter (TIADC). For two different calibration strategies, the absolute calibration and the relative calibration, the inductive expressions of modulation matrices are derived from the coefficient matrices provided by different estimation methods. Based on the modulation matrix, we develop a digital calibration architecture, which enables the output of TIADC to be modulated firstly before being corrected, so the costly matrix multiplication processing can be avoided. This calibration architecture also enables the estimation process of each channel to be independent and can be executed simultaneously, which accelerates the convergence speed of the timing mismatch. We show the efficiency of the calibration structure by simulations, including examples from the literature. Numerical simulation demonstrates that the convergence speed of the timing mismatch has been improved by at least 27%, compared with the estimation method depending on the specified reference channel. The result of FPGA-based hardware implementation shows that the spurious-free dynamic range (SFDR) is improved by 37.63dB. [ABSTRACT FROM AUTHOR]

The device faults commonly exist in microgrids (MGs) and often undermine the system stability. However, the existing distributed methods generally design control protocols based on the absence of faults or only considering actuator faults, which limits the applicability of controllers in MG systems. Therefore, this paper investigates an adaptive prescribed fixed-time performance (PFTP) consensus fault-tolerant control (FTC) problem for the voltage regulation of a nonlinear islanded MG inverter-based distributed generators (DGs) with output contactor and actuator fault via the state event-triggered mechanism. The PFTP method for the voltage restoration synchronization error is proposed by constructing a novel performance function to increase the speed of voltage recovery. Based on the estimation properties of fuzzy logic system, an unknown nonlinear function and the residual term of output contactor are estimated. Then, an adaptive state event-triggering mechanism is designed to reduce the communication burden, and its main advantage is that the control protocol is updated in the aperiodic way at the triggering instants. Further, according to backstepping technology, a novel adaptive event-triggered consensus FTC protocol is constructed to achieve the voltage regulation of islanded MG, and a parameter adaptive law is designed to compensate the adverse effects of the output contactor fault. Based on the Lyapunov stability theory, it is proved that the voltage recovery synchronization error satisfies the PFTP, and all signals in the closed-loop systems are cooperatively uniformly ultimately bounded. Finally, the proposed control strategy is also validated by using the MATLAB. [ABSTRACT FROM AUTHOR]

This paper presents an extensive study of linear and logistic regression algorithms implemented with 1T1R memristor crossbars arrays. Using a sophisticated simulation platform that wraps circuit-level simulations of 1T1R crossbars and physics-based models of RRAM (memristors), we elucidate the impact of device variability on algorithm accuracy, convergence rate and precision. Moreover, a smart pulsing strategy is proposed for practical implementation of synaptic weight updates that can accelerate training in real crossbar architectures. Stochastic multi-variable linear regression shows robustness to memristor variability in terms of prediction accuracy but reveals impact on convergence rate and precision. Similarly, the stochastic logistic regression crossbar implementation reveals immunity to memristor variability as determined by negligible effects on image classification accuracy but indicates an impact on training performance manifested as reduced convergence rate and degraded precision. [ABSTRACT FROM AUTHOR]

In this paper, the observer based neural adaptive finite-time containment control strategy for non-strict feedback nonlinear multi-agent systems is studied. The finite-time command filter is used to overcome the explosion of complexity problem and the established fractional power based error compensation signal is applied to compensate the filtering error caused by the filter. The distributed finite-time command filtered backstepping control method combines with the neural adaptive control technology and state observer is given, which ensures the containment control errors reach to the desired neighborhood of the origin in finite-time in the presence of uncertain dynamics and unmeasurable states in the system. The given numerical simulations show the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Implementing finite impulse response (FIR) adaptive filters by employing the sums of signed-powers-of-two (SOPOT) arithmetic may lead to simpler hardware and consequently reduced power consumption. In this paper, one evaluates the effects of SOPOT arithmetic on the adaptive filter’s recursion algorithms. The filters’ coefficients and algorithms’ underlying variables are fully operated using SOPOT arithmetic in the whole iterative process. More specifically, one evaluates convergence rate, numerical stability, and accuracy since using few signed-powers-of-two (SPT) terms propagates numerical errors during the adaptive cycle that may impair the algorithm behavior. The SOPOT approximations are obtained through the technique known as Matching Pursuits with Generalized Bit-Plane (MPGBP) algorithm, with notable cost-performance trade-off and low computational complexity. Results are provided for the Least-Mean-Squares (LMS), the Normalized Least-Mean-Squares (NLMS) and the Recursive-Least-Squares (RLS) algorithms, considering adaptive filters employed for system identification and change detection. [ABSTRACT FROM AUTHOR]

In this paper, a fixed-time backstepping fractional-order sliding mode excitation controller (FTBFOSMEC) is proposed to enhance the performance of multimachine power system. The FTBFOSMEC is designed to achieve semi-global uniform ultimate boundedness of the multimachine power system within an upper limited convergence time irrelated to the initial operating states. The FTBFOSMEC can achieve the attractive qualities of sliding mode control with strong robustness and fast dynamic response, and the merits of backstepping control in globally asymptotic stability for parametric uncertainty. In addition, the FTBFOSMEC can avoid the singularity problem of conventional SMC. Furthermore, the nonlinear first-order filter is carried out to avoid the “explosion complexity” issue of traditional backstepping control. The fractional-order controller is designed to alleviate the chattering phenomenon of the conventional SMC. Meanwhile, the FTBFOSMEC is in a continuous description, which can further eliminate the chattering phenomenon. The Lyapunov function is taken to analyze the stabilization of the multimachine power system under the control of the FTBFOSMEC, and to estimate the upper limit of convergence time. Finally, comparative results, based on the New England 10-machine 39-bus power system, indicate that the power system under the proposed FTBFOSMEC can obtain effective, robust and superior performances compared with the existing control strategies under different operating circumstances. [ABSTRACT FROM AUTHOR]

This paper is concerned with bipartite tracking for double-integrator networked systems with signed communication graphs, where both cooperative and antagonistic interactions coexist. A finite-time bipartite tracking framework is established, where followers track either the state or the opposite state of a leader. Different from some conventional results with convergence over an infinite time horizon, the finite-time convergence in this paper is achieved in an accurate manner. Under structurally balanced signed graphs, an integral sliding mode based finite-time bipartite tracking controller is proposed. The construction of an integral sliding mode variable is to ensure that the system dynamics is driven onto a sliding surface in finite-time. On the sliding surface, neighbouring states are used together with the homogeneous technique to guarantee that bipartite tracking is achieved in finite-time. To further realize fixed-time bipartite tracking, a controller is designed by using the integral sliding mode and the bi-limit homogeneous concept. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed controllers. [ABSTRACT FROM AUTHOR]

In this paper, a unified framework is proposed to address the synchronization problem of memristor chaotic systems (MCSs) via the sliding-mode control method. By employing the presented unified framework, the finite-time and fixed-time synchronization of MCSs can be realized simultaneously. On the one hand, based on the Lyapunov stability and sliding-mode control theories, the finite-/fixed-time synchronization results are obtained. It is proved that the trajectories of error states come near and get to the designed sliding-mode surface, stay on it accordingly and approach the origin in a finite/fixed time. On the other hand, we develop an image encryption algorithm as well as its implementation process to show the application of the synchronization. Finally, the theoretical results and the corresponding image encryption application are carried out by numerical simulations and statistical performances. [ABSTRACT FROM AUTHOR]

For massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection has been shown to achieve near-optimal performance but suffers from excessively high complexity due to the large-scale matrix inversion. Being matrix inversion free, detection algorithms based on the Gauss–Seidel (GS) method have been proved more efficient than conventional Neumann series expansion-based ones. In this paper, an efficient GS-based soft-output data detector for massive MIMO and a corresponding VLSI architecture are proposed. To accelerate the convergence of the GS method, a new initial solution is proposed. Several optimizations on the VLSI architecture level are proposed to further reduce the processing latency and area. Our reference implementation results on a Xilinx Virtex-7 XC7VX690T FPGA for a 128 base-station antenna and eight user massive MIMO system show that our GS-based data detector achieves a throughput of 732 Mb/s with close-to-MMSE error-rate performance. Our implementation results demonstrate that the proposed solution has advantages over the existing designs in terms of complexity and efficiency, especially under challenging propagation conditions. [ABSTRACT FROM AUTHOR]

This paper discusses the distributed fixed-time containment control for networked nonlinear systems via the event/self-triggered approaches over directed graphs. A distributed event-triggered control protocol without continuous control updates is first proposed. In order to relieve the chattering effect, a modified distributed event-triggered control law is developed. To further overcome the drawback of continuous state monitoring and reduce the communication frequency between neighboring agents, a distributed chattering-free self-triggered control strategy is accordingly designed. A favorable aspect of our work is that the unnecessary resource utilizations of computation, communication as well as control updates can be saved while sustaining the desired control properties and excluding the Zeno behavior. Another distinct feature of this paper lies in that the containment control objective is realized in fixed time and the estimate of settling time can be prescribed without dependence on initial states of networked agents. Finally, some simulation results are provided to illustrate the effectiveness of the theoretical control schemes. [ABSTRACT FROM AUTHOR]

This paper addresses the finite-time and fixed-time bipartite average tracking (BAT) problems in multi-agent systems (MASs) subject to bounded disturbances. A novel non-smooth protocol is first constructed on the basis of utilizing the finite-time control method, which can realize the finite-time BAT with structurally balanced graph. Then, the fixed-time BAT problem is dealt with nonlinear fixed-time algorithm, the setting time of which is obtained without depending on initial states. By employing Lyapunov stability theory, some sufficient criteria are obtained to achieve the finite-time and fixed-time BAT for MASs with disturbances. Numerical examples are finally developed for illustration. [ABSTRACT FROM AUTHOR]

Piecewise affine (PWA) systems are attractive models that can represent various hybrid systems with local affine subsystems and polyhedral regions due to their universal approximation properties. The identification problem of PWA systems amounts to estimating the number of subsystems, parameters of each subsystem, and the corresponding polyhedral partitions via state-input vectors. In this paper, we propose a novel approach to address the online identification problem of continuous-time PWA systems in state-space form. Specifically, an online active mode recognition algorithm and a generalized integral concurrent learning identifier are presented to acquire the number of subsystems, the switching sequence, and the parameter of each subsystem. In addition, we develop the optimization problem for the polyhedral partition estimation, which is solved by using the estimated switching sequence and subsystem parameters. The effectiveness of the proposed identification approach is demonstrated via simulation results. [ABSTRACT FROM AUTHOR]

This paper discusses finite/fixed-time (FNT/FXT) synchronization of multi-layer networks based on energy consumption estimation. Firstly, a novel FXT stability theorem of dynamical system is presented. Secondly, sufficient criteria for FNT/FXT synchronization in multi-layer networks are given, and the estimation of the energy consumption are also gained. Especially, the range of the number of layers of network is gained based on the least convergence time and the estimation of the least energy consumption. The results show that shorter synchronization time and less energy consumption does not mean less network layers. Finally, the validity of the theoretical method is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNNs) are increasingly being used in a variety of applications. However, DNNs have huge computational and memory requirements. One way to reduce these requirements is to sparsify DNNs by using smoothed LASSO (Least Absolute Shrinkage and Selection Operator) functions. In this paper, we show that irrespective of error profile, the sparsity values obtained using various smoothed LASSO functions are similar, provided the maximum error of these functions with respect to the LASSO function is the same. We also propose a layer-wise DNN pruning algorithm, where the layers are pruned based on their individual allocated accuracy loss budget, determined by estimates of the reduction in number of multiply-accumulate operations (in convolutional layers) and weights (in fully connected layers). Further, the structured LASSO variants in both convolutional and fully connected layers are explored within the smoothed LASSO framework and the tradeoffs involved are discussed. The efficacy of proposed algorithm in enhancing the sparsity within the allowed degradation in DNN accuracy and results obtained on structured LASSO variants are shown on MNIST, SVHN, CIFAR-10, and Imagenette datasets and on larger networks such as ResNet-50 and Mobilenet. [ABSTRACT FROM AUTHOR]

In this paper, hybrid dynamical systems theory is applied to the analysis and control of switched converters with Pulse-Width Modulated (PWM) inputs. The system is described by a state-space model with continuous flows and discrete jumps, without averaged equations. The modulation effects are captured in full without using time-dependent signals, by enlarging the state vector to include the PWM waveform generation process. Furthermore, the sample-and-hold mechanism associated with the sampling frequency is also taken into account with this approach. A control law is proposed based on a Lyapunov function candidate. Furthermore, convergence sets and the steady state jitter, inherent to PWM-based controllers, are analyzed estimating limit sets for the augmented state. Consequently, output chattering can be bounded. By using hybrid dynamical system theory, the control designer gains a deeper understanding of the effect of modulation in the closed-loop dynamics, avoiding the problems associated with the use averaged models. Experimental results validate the proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes a low-complexity method and architecture to compute the logarithm of complex numbers based on coordinate rotation digital computer (CORDIC). Our method takes advantage of the vector mode of circular CORDIC and hyperbolic CORDIC, which only needs shift-add operations in its hardware implementation. Our architecture has lower design complexity and higher performance compared with conventional architectures. Through software simulation, we show that this method can achieve high precision for logarithm computation, reaching the relative error of 10−7. Finally, we design and implement an example circuit under TSMC 28nm CMOS technology. According to the synthesis report, our architecture has smaller area, lower power consumption, higher precision and wider operation range compared with the alternative architectures. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the behavior of stochastic approximation schemes with set-valued maps in the absence of a stability guarantee. We prove that after a large number of iterations, if the stochastic approximation process enters the domain of attraction of an attracting set, it gets locked into the attracting set with high probability. We demonstrate that the above-mentioned result is an effective instrument for analyzing stochastic approximation schemes in the absence of a stability guarantee, by using it to obtain an alternate criterion for convergence in the presence of a locally attracting set for the mean field and by using it to show that a feedback mechanism, which involves resetting the iterates at regular time intervals, stabilizes the scheme when the mean field possesses a globally attracting set, thereby guaranteeing convergence. The results in this paper build on the works of Borkar, Andrieu et al., and Chen et al., by allowing for the presence of set-valued drift functions. [ABSTRACT FROM AUTHOR]

The frequency-domain adaptive filter (FDAF) has been utilized in various applications where the length of the modeling filter is very long. The convergence behavior of the FDAF in the full-modeling case has been well analyzed. However, because the length of the true filter may be rather long and is unknown in practice, the length of the adaptive filter is usually less than that of the true system. In this paper, only the statistical behavior of the deficient-length constrained FDAF was analyzed for Gaussian inputs. This paper presents a unified approach to the stochastic analysis of a family of the FDAF in an under-modeling situation and for arbitrary input distributions. Specifically, the mean and mean-square convergence behaviors of FDAFs are presented, and the closed-form expression of the steady-state misalignment is given. Our theoretical results provide some new insights into the convergence property of deficient-length FDAFs. In addition, the analysis is carried out in the frequency domain directly. Computer simulations indicate an excellent agreement with our theoretical predictions. [ABSTRACT FROM AUTHOR]

Clock skew and component mismatches in continuous-time DACs introduce two types of error: static error and dynamic error. Both types of error typically limit the performance of practical, high-resolution, and continuous-time DACs, but most prior calibration techniques primarily reduce only static error. An exception is a recently published mismatch noise cancellation (MNC) technique that adaptively measures and cancels both types of error over the DAC’s first Nyquist band. However, a disadvantage of the technique is that it requires an oversampling ADC that operates at several times the DAC’s Nyquist rate to prevent convergence error that would otherwise be caused by aliasing. This paper presents a sub-sampling version of the MNC technique that avoids this limitation at the expense of a lower calibration convergence rate. As proven in the paper, the subsampling MNC technique allows aliasing to occur, but in such a way that convergence error is avoided. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time time-varying formation control problems for heterogeneous multi-agent systems (MASs) composed of multiple Unmanned Ground Vehicles (UGVs) and multiple Unmanned Aerial Vehicles (UAVs) in the presence of actuator faults, parameter uncertainties, matched and mismatched disturbances. Besides achieving the desired formation configurations, each follower can also track the position trajectory produced by the virtual leader within fixed time simultaneously. The difference dynamic characteristics between the heterogeneous agents leads to unbalanced interaction of lumped uncertainties in the communication network, which increases the difficulty of collaborative control. To estimate the mismatched disturbances and lumped uncertainties, a fixed-time observer for each follower is designed, which can guarantee the estimation errors converge to the origin in fixed settling time. Subsequently, by utilizing the backstepping technique and the fixed-time stability theory, an observer-based distributed fixed-time formation controller for each follower in the X - Y axes and the observer-based decentralized fixed-time tracking controllers for follower-UAVs in the Z axes are presented, which are shown to be fixed-time stable even under the influence of actuator faults and mismatched disturbances. Moreover, the fixed-time results can ensure the convergence time is independent of initial conditions. Finally, numerical simulations demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Dropout and DropConnect are two techniques to facilitate the regularization of neural network models, having achieved the state-of-the-art results in several benchmarks. In this paper, to improve the generalization capability of spiking neural networks (SNNs), the two drop techniques are first applied to the state-of-the-art SpikeProp learning algorithm resulting in two improved learning algorithms called SPDO (SpikeProp with Dropout) and SPDC (SpikeProp with DropConnect). In view that a higher membrane potential of a biological neuron implies a higher probability of neural activation, three adaptive drop algorithms, SpikeProp with Adaptive Dropout (SPADO), SpikeProp with Adaptive DropConnect (SPADC), and SpikeProp with Group Adaptive Drop (SPGAD), are proposed by adaptively adjusting the keep probability for training SNNs. A convergence theorem for SPDC is proven under the assumptions of the bounded norm of connection weights and a finite number of equilibria. In addition, the five proposed algorithms are carried out in a collaborative neurodynamic optimization framework to improve the learning performance of SNNs. The experimental results on the four benchmark data sets demonstrate that the three adaptive algorithms converge faster than SpikeProp, SPDO, and SPDC, and the generalization errors of the five proposed algorithms are significantly smaller than that of SpikeProp. Furthermore, the experimental results also show that the five algorithms based on collaborative neurodynamic optimization can be improved in terms of several measures. [ABSTRACT FROM AUTHOR]

To increase lifespan and optimize capacity utilization of lithium-ion (Li-ion) battery, it is imperative to predict its states accurately. This paper primarily focuses on offering an improved solution for estimating one of the most important battery states known as state of charge (SOC). After extensive analysis of the current state-of-the-art methods, we propose a new adaptive observer based approach for precise estimation of battery SOC using super twisting algorithm. The proposed approach avoids overestimation of gains while ensuring finite-time convergence of the states. For choosing the associated adaptive gains, it does not demand any information on upper bounds of the uncertainty and its derivative, except for their existence. Unlike most of the sliding mode methods employed for SOC estimation, the proposed approach provides continuous control injection resulting in reduced chattering and eliminates the need for low pass filter that leads to improved accuracy and response time. Lyapunov stability theory is utilized to prove the robustness and the error convergence of the proposed observer. The presented algorithm is executed on a lithium-polymer battery and the obtained results demonstrate that it outperforms the well-established approaches in terms of accuracy, chattering, robustness, computational complexity, and convergence time. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of bipartite consensus tracking for multi-agent systems with both cooperative and antagonistic interactions. These interactions among vertices in a signed digraph are first investigated with the signs of the weighted products of associated paths in their augmented undirected graph. To improve the convergence rate, a finite-time protocol and a fixed-time protocol are developed to solve the bipartite consensus tracking problem with a finite-time and a settling time independent of any initial states, respectively. Moreover, an M-matrix based method is well proposed to estimate all parameters in the constructed Lyapunov function, therefore, the upper bounds of the settling times for finite-time and fixed-time cases can be analytically determined. Finally, numerical examples are provided to demonstrate the effectiveness of proposed methods. [ABSTRACT FROM AUTHOR]

In this paper, the locally fixed-time and globally fixed-time $\mathcal {H}_{\infty }$ control problems for the port-controlled Hamiltonian (PCH) systems are investigated via the interconnection and damping assignment passivity-based control (IDA-PBC) technique. Compared with finite-time stabilization, where the convergence time of the closed-loop system's states relies on the initial values, the settling time of fixed-time stabilization can be adjusted to achieve desired equilibrium point regardless of initial conditions. The concepts of fixed-time $\mathcal {H}_{\infty }$ control, fixed-time stability region (or region of attraction), and fixed-time stability boundary are presented in this paper, and the criterions of globally fixed-time attractivity of a prespecified locally fixed-time stability region are obtained. Combining the locally fixed-time stability of an equilibrium point and the globally fixed-time attractivity of a prespecified fixed-time stability region, the globally fixed-time $\mathcal {H}_{\infty }$ control problem of PCH system is effectively solved. Two novel control laws are designed to deal with the globally fixed-time $\mathcal {H}_{\infty }$ control problem, and the conservativeness in estimating the settling time is also briefly discussed. An illustrative example shows that the theoretical results obtained in this paper work very well in the fixed-time $\mathcal {H}_{\infty }$ control design for PCH systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose a reduced complexity parallel least mean square structure (RC-pLMS) for adaptive beamforming and its pipelined hardware implementation. RC-pLMS is formed by two least mean square (LMS) stages operating in parallel (pLMS), where the overall error signal is derived as a combination of individual stage errors. The pLMS is further simplified to remove the second independent set of weights resulting in a reduced complexity pLMS (RC-pLMS) design. In order to obtain a pipelined hardware architecture of our proposed RC-pLMS algorithm, we applied the delay and sum relaxation technique (DRC-pLMS). Convergence, stability and quantization effect analysis are performed to determine the upper bound of the step size and assess the behavior of the system. Computer simulations demonstrate the outstanding performance of the proposed RC-pLMS in providing accelerated convergence and reduced error floor while preserving a LMS identical $O(N)$ complexity, for an antenna array of $N$ elements. Synthesis and implementation results show that the proposed design achieves a significant increase in the maximum operating frequency over other variants with minimal resource usage. Additionally, the resulting beam radiation pattern show that the finite precision DRC-pLMS implementation presents similar behavior of the infinite precision theoretical results. [ABSTRACT FROM AUTHOR]

Sparsified kernel adaptive filters (SKAFs) is an attractive filtering solution with low memory and computational complexity. Most of existing SKAFs are based on the mean square error (MSE) criterion under Gaussian noise assumption for its simplicity and convenience. When the assumption deviates largely from the underlying truth, the performance of these methods could degrade significantly. In this paper, we propose a novel SKAF, named as projected kernel least mean p-power algorithm (PKLMP), based on the mean p-power error (MPE) criterion and vector projection (VP) method. We provide convergence analyses in terms of the stead-state MSE, based on a Taylor expansion method, and derive the lower and upper bounds for the steady-state excess MSE. We also conduct mean convergence analysis for PKLMP, and derive convergence conditions. To exploit the information in the desired outputs, we further derive a modified PKLMP by smoothing the desired signal. Finally, a simple and effective online variable kernel centers strategy is proposed to improve the filtering performance of the proposed KAFs. Simulation results under a static function estimation, a chaotic time-series prediction, and two real-world time-series predictions are conducted and validate the effectiveness of the proposed PKLMP algorithms. [ABSTRACT FROM AUTHOR]

This paper studies consensus problems of multi-agent systems with binary-valued communications. Different from most existing works, the agents considered in this paper can only get binary-valued observations of its neighbors’ states with random noises. A consensus algorithm is proposed: first, each agent estimates its neighbors’ states by the recursive projection algorithm; then, each agent designs the control timely based on the estimates. It is proved that the estimates of the states can converge to the true states with a faster convergence rate than that in the parameter estimation. Moreover, the states of the agents can achieve mean-square consensus, and the corresponding consensus speed can achieve $O(1/t)$ under certain conditions. Finally, simulations are given to demonstrate the theoretical results. [ABSTRACT FROM AUTHOR]

This paper introduces a new, reference trajectory following, sliding-mode control strategy for disturbed discrete-time systems. The strategy uses an external trajectory generator based on a switching type reaching law. The trajectory following strategy not only ensures all the properties of the quasi-sliding-mode as defined by Gao et al., but with a certain choice of the control parameters it also guarantees a significant reduction of the quasi-sliding-mode band width and the errors of all the state variables. This paper also considers the problem of error calculation in the discrete-time, reaching law based sliding-mode control systems. It is shown that the limitation of the sliding variable in the sliding phase directly results in bounded errors of all state variables. The results are verified with a simulation example. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the proper orthogonal decomposition (POD) method to numerically solve the forward Kolmogorov equation (FKE). Our method aims to explore the low-dimensional structures in the solution space of the FKE and to develop efficient numerical methods. As an important application and our primary motivation to study the POD method to FKE, we solve the nonlinear filtering (NLF) problems with a real-time algorithm proposed by Yau and Yau combined with the POD method. This algorithm is referred as POD algorithm in this paper. Our POD algorithm consists of offline and online stages. In the offline stage, we construct a small number of POD basis functions that capture the dynamics of the system and compute propagation of the POD basis functions under the FKE operator. In the online stage, we synchronize the coming observations in a real-time manner. Its convergence analysis has also been discussed. Some numerical experiments of the NLF problems are performed to illustrate the feasibility of our algorithm and to verify the convergence rate. Our numerical results show that the POD algorithm provides considerable computational savings over existing numerical methods. [ABSTRACT FROM AUTHOR]

For massive multiuser multiple-input multiple-output (MIMO) systems, linear detectors, such as minimum mean square error (MMSE), suffer from unbearable computational pressure due to the large-scale matrix inversions. Various iterative detectors, such as Jacobi and steepest descent (SD) as well as their enhanced variants, are applied to improve the performance and complexity trade-off. However, their benefits would not maintain when: 1) the number of users is close to that of the base station antennas and 2) channel correlation is considered. To address this problem, an iterative detector based on SD and Barzilai–Borwein (BB) algorithms entitled SDBB is introduced in this paper. Furthermore, a novel enhanced SDBB (ESDBB) detector is proposed, which combines SDBB with SD to achieve better performance in the challenging scenarios. Both theoretical and numerical results are detailed to demonstrate the advantages of ESDBB in balancing the performance and complexity. Specifically, attaining faster convergence and better bit error rate (BER) results, ESDBB outperforms methods, including adaptive Jacobi and successive over relaxation (SOR). An efficient hardware architecture for the ESDBB detector is also proposed. Implementation results on a Xilinx Virtex-7 XC7VX690T FPGA show the advantages of the proposed ESDBB detector compared to the state of the art (SOA) in terms of throughput (31.3 Mb/s) and efficiency. [ABSTRACT FROM AUTHOR]

This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probably, scores of papers. As clarified later, unarguably, the FTA of Markov chains, in general, and of LA, in particular, is far more complex than the asymptotic steady-state analysis. Such an FTA provides rigid bounds for the time required for the LA to attain to a given convergence accuracy. We concentrate on the FTA of the Discretized Pursuit Automaton (DPA), which is probably one of the fastest and most accurate reported LA. Although such an analysis was carried out many years ago, we record that the previous work is flawed. More specifically, in all brevity, the flaw lies in the wrongly “derived” monotonic behavior of the LA after a certain number of iterations. Rather, we claim that the property should be invoked is the submartingale property. This renders the proof to be much more involved and deep. In this paper, we rectify the flaw and reestablish the FTA based on such a submartingale phenomenon. More importantly, from the derived analysis, we are able to discover and clarify, for the first time, the underlying dilemma between the DPA’s exploitation and exploration properties. We also nontrivially confirm the existence of the optimal learning rate, which yields a better comprehension of the DPA itself. [ABSTRACT FROM AUTHOR]

This paper proposes a new concurrent learning-based adaptive control algorithm. The main objective behind our proposition is to relax the persistent excitation requirement for the stability guarantee, while providing the ability to identify time-varying parameters. To achieve the objective, this paper designs a directional forgetting algorithm, which is then integrated with the adaptive law. The theoretical stability analysis shows that the tracking and parameter estimation error is exponentially stable with the signal only finitely excited, not persistently excited. The analysis also shows that the proposed algorithm can guarantee the stability under time-varying parameters. Moreover, the necessary and sufficient conditions for the stability given the time-varying parameters are derived. The results of numerical simulations confirm the validity of the theoretical analysis results and demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The paper considers normalized gradient descent (NGD), a natural modification of classical gradient descent (GD) in optimization problems. It is shown that, contrary to GD, NGD escapes saddle points “quickly.” A serious shortcoming of GD in nonconvex problems is that it can take arbitrarily long to escape from the neighborhood of a saddle point. In practice, this issue can significantly slow the convergence of GD, particularly in high-dimensional nonconvex problems. The paper focuses on continuous-time dynamics. It is shown that 1) NGD “almost never” converges to saddle points and 2) the time required for NGD to escape from a ball of radius $r$ about a saddle point $x^*$ is at most $5\sqrt{\kappa }r$ , where $\kappa$ is the condition number of the Hessian of $f$ at $x^*$. As a simple application of these results, a global convergence-time bound is established for NGD under mild assumptions. [ABSTRACT FROM AUTHOR]

This paper considers a class of reinforcement-based learning (namely, perturbed learning automata) and provides a stochastic-stability analysis in repeatedly played, positive-utility, finite strategic-form games. Prior work in this class of learning dynamics primarily analyzes asymptotic convergence through stochastic approximations, where convergence can be associated with the limit points of an ordinary-differential equation (ODE). However, analyzing global convergence through an ODE-approximation requires the existence of a Lyapunov or a potential function, which naturally restricts the analysis to a fine class of games. To overcome these limitations, this paper introduces an alternative framework for analyzing asymptotic convergence that is based upon an explicit characterization of the invariant probability measure of the induced Markov chain. We further provide a methodology for computing the invariant probability measure in positive-utility games, together with an illustration in the context of coordination games. [ABSTRACT FROM AUTHOR]

This paper presents an all-digital background calibration technique for the time skew mismatch in time-interleaved ADCs (TIADCs). The technique jointly estimates all of the time skew values by processing the outputs of a bank of correlators. A low-complexity sampling sequence intervention technique, suitable for successive approximation register (SAR) ADC architectures, is proposed to overcome the limitations associated with blind estimation. A two-stage digital correction mechanism based on the Taylor series is proposed to satisfy the target high-precision correction. A quantitative study is performed regarding the requirements imposed on the digital correction circuit in order to satisfy the target performance and yield, and a corresponding filter design method is proposed, which is tailored to meet these requirements. Mitchell’s logarithmic multiplier is adopted for the implementation of the principal multipliers in both the estimation and correction mechanisms, leading to a 25% area and power reduction in the estimation circuit. The proposed calibration is synthesized using a TSMC 28-nm HPL process targeting a 2.4-GHz sampling frequency for an eight-sub-ADC system. The calibration block occupies 0.03 mm2 and consumes 11 mW. The algorithm maintains the SNDR above 65 dB for a sinusoidal input within the target bandwidth. [ABSTRACT FROM AUTHOR]

Recently, Thompson sampling has been shown to achieve good theoretical performance guarantees for stochastic control problems with parameter uncertainty when the state, control, and parameter spaces are all finite. Much less is known however about the performance of Thompson sampling when applied to continuous or more general spaces, which constitutes an important class of problems in practice. In this paper, we study Thompson sampling when applied to a broad class of average cost stochastic control problems where the state, control, and parameter spaces are all general measurable spaces. The main contributions of our paper are establishing theoretical performance guarantees for Thompson sampling as measured by: first, expected posterior sampling error; and second, average per period regret. [ABSTRACT FROM AUTHOR]

This paper deals with the estimation of convergence rate and domain of attraction of control-Lyapunov functions in Lyapunov-based control. This pair of estimation problems has been considered only for input-affine systems with constraints on the input norm. In this paper, we propose a novel optimization framework to address the estimation of convergence rate and domain of attraction. Specifically, we formulate the estimation problems as min–max bilevel programs for the decay rate of the Lyapunov function, where the inner problem can be resolved using Karush–Kuhn–Tucker optimality conditions, and the resulting single-level programs can be transformed into and solved as mixed-integer nonlinear programs. The proposed approach is applicable to systems with input-nonaffinity or more general forms of input constraints under an input-convexity assumption. [ABSTRACT FROM AUTHOR]

This paper addresses the distributed estimation problem in a set membership framework. The agents of a network collect measurements which are affected by bounded errors, thus implying that the unknown parameters to be estimated belong to a suitable feasible set. Two distributed algorithms are considered, based on projections of the estimate of each agent onto its local feasible set. The main contribution of the paper is to show that such algorithms are asymptotic interpolatory estimators, i.e., they converge to an element of the global feasible set, under the assumption that the feasible set associated to each measurement is convex. The proposed techniques are demonstrated on a distributed linear regression estimation problem. [ABSTRACT FROM AUTHOR]

This paper presents a mean absolute deviation (MAD)-based background timing-skew calibration technique for time-interleaved (TI) ADCs. It uses a single comparator-based window detector to select input samples near the zero crossing and computes their MAD value, which is used as an indicator for the timing skew. The proposed technique has low computation complexity, as it only requires taking absolute value and averaging. It also has a fast convergence speed when operating in the background with unknown random inputs, as the MAD value can be accurately estimated with a small number of samples. A prototype 10-bit 2-way TI-SAR ADC is built in 40-nm CMOS to verify the proposed technique. It achieves over 52 dB of SNDR over the entire Nyquist band. Operating at 600 MS/s, it consumes 4.7 mW, which translates to a Walden figure-of-merit of 24-fJ/conversion-step. [ABSTRACT FROM AUTHOR]

Within the set of stochastic, indecomposable, aperiodic (SIA) matrices, the class of Sarymsakov matrices is the largest known subset that is closed under matrix multiplication, and more critically whose compact subsets are all consensus sets. This paper shows that a larger subset with these two properties can be obtained by generalizing the standard definition for Sarymsakov matrices. The generalization is achieved by introducing the notion of the SIA index of a stochastic matrix, whose value is 1 for Sarymsakov matrices, and then exploring those stochastic matrices with larger SIA indices. In addition to constructing the larger set, this paper introduces another class of generalized Sarymsakov matrices, which contains matrices that are not SIA, and studies their products. Sufficient conditions are provided for an infinite product of matrices from this class, converging to a rank-one matrix. Finally, as an application of the results just described and to confirm their usefulness, a necessary and sufficient combinatorial condition, the “avoiding set condition,” for deciding whether or not a compact set of stochastic matrices is a consensus set is revisited. In addition, a necessary and sufficient combinatorial condition is established for deciding whether or not a compact set of doubly stochastic matrices is a consensus set. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of the discrete-time linear extended state observer (ESO) for a disturbed nonlinear discrete-time system with uncertainties. Since the performance of the ESO is dominated by the choice of the gain parameter, the goal of this paper is to analyze the behavior of the observation error when the gain parameter varies from zero to infinity. We show that for the ESO considered, there exists a certain threshold such that if the gain parameter is smaller than this threshold, the observation error at steady state will become unbounded. An upper bound on the asymptotic observation error is also proposed, which is proved to be bounded and convergent utilizing set-theoretic techniques. For the case where the closed-loop matrix of the ESO is nonnegative, a tighter upper bound on the asymptotic observation error is also proposed. The proposed theoretic results are illustrated by the simulation results and the experimental verifications on a direct current motor loading platform. [ABSTRACT FROM AUTHOR]

A quantized iterative learning control (QILC) for continuous-time multi-agent systems with finite-leveled sigma-delta ( $\Sigma \Delta$ ) quantization and random packet losses is first proposed in this paper. To realize the digital communication between signals and utilize limited communication bandwidth effectively, we introduce the $\Sigma \Delta$ quantizer with limited communication data rate (quantization bits) into the control field and for the design of the QILC in this paper. In addition, the packet losses are also first considered into the QILC, which makes the controller more close to the practical engineering applications. Since the nonlinearity and randomness introduced by the quantization and packet losses, a decreasing learning gain is utilized with the help of the non-smooth analysis and mathematical expectation for the analysis of convergence. Accurate tracking in the sense of expectation can be obtained based on randomly small number of quantization bits, even merely one bit of quantization information. Numerical simulations are given to show the effectiveness of the proposed protocol. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the global nonfragile Mittag–Leffler synchronization and the global synchronization in finite time for fractional-order discontinuous neural networks, where activation functions are discontinuous at 0, or modeled as a local Hölder functions with the nonlinear growth property in a neighborhood of 0. First, two lemmas concerned with the convergence with respect to an absolutely continuous function are developed. Second, a new property, which introduces an inequality of the fractional derivative for the variable upper limit integral with respect to the nonsmooth integrable function, is presented and applied in the synchronization results’ analysis. In addition, under the fractional Filippov differential inclusion framework, by utilizing the Lur’e Postnikov-type Lyapunov functional, nonsmooth analysis method, and the convergence properties developed in this paper, the synchronization conditions are derived in the form of linear matrix inequalities. Moreover, the upper bound of the setting time for the global nonfragile synchronization in finite time is calculated accurately. Finally, two illustrations are presented to verify the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]

This paper studies the distributed average tracking (DAT) problem for multiple reference signals described by the second-order nonlinear dynamical systems. Leveraging the state-dependent gain design and the adaptive control approaches, a couple of DAT algorithms are developed in this paper, which are named finite-time and adaptive-gain DAT algorithms. Based on the finite-time one, the states of the physical agents in this paper can track the average of the time-varying reference signals within a finite settling time. Furthermore, the finite settling time is also estimated by considering a well-designed Lyapunov function in this paper. Compared with asymptotical DAT algorithms, the proposed finite-time algorithm not only solve finite-time DAT problems but also ensure states of physical agents to achieve an accurate average of the multiple signals. Then, an adaptive-gain DAT algorithm is designed. Based on the adaptive-gain one, the DAT problem is solved without global information. Thus, it is fully distributed. Finally, numerical simulations show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]