Input-to-state learning of recurrent neural networks with delay and disturbance

This article deals with the issue of input-to-state stabilization for recurrent neural networks with delay and external disturbance. The goal is to design a suitable weight-learning law to make the considered network input-to-state stable with a predefined -gain. Based on the solution of linear matrix inequalities, two schemes for the desired learning law are presented via using decay-rate-dependent and decay-rate-independent Lyapunov functionals, respectively. It is shown that, in the absence of external disturbance, the proposed learning law also guarantees the exponential stability of the network. To illustrate the applicability of the present weight-learning law, two numerical examples with simulations are given.     

An enhanced state estimation for linear parabolic PDE systems with mobile sensors

This paper studies an enhanced state estimation problem of distributed parameter processes modeled by a linear parabolic partial differential equation using mobile sensors. The proposed estimation scheme contains a state estimator and the guidance of mobile sensors, where the spatial domain is decomposed into multiple subdomains according to the number of sensors and each sensor is capable of moving within the respective subdomain. The state estimator is desired to make the state estimation error system exponentially stable while providing an performance bound. The mobile sensor guidance is used to enhance the transient performance of the error system. By the Lyapunov direct technique, an integrated design of state estimator and mobile sensor guidance laws is developed in the form of bilinear matrix inequalities (BMIs) to meet the desired design objectives. Moreover, to make the performance bound as small as possible, a suboptimal enhanced state estimation problem is formulated as a BMI optimization one, which can be solved via an iterative linear matrix inequality algorithm. Finally, numerical simulations are given to show the effectiveness of the proposed method.     

Robust nonlinear adaptation algorithms for multitask prediction networks

High fidelity behavior prediction of intelligent agents is critical in many applications, which is challenging due to the stochasticity, heterogeneity, and time-varying nature of agent behaviors. Prediction models that work for one individual may not be applicable to another. Besides, the prediction model trained on the training set may not generalize to the testing set. These challenges motivate the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. This article considers online adaptable multitask prediction for both intention and trajectory. The goal of online adaptation is to improve the performance of both intention and trajectory predictions with only the feedback of the observed trajectory. We first introduce a generic -step adaptation algorithm of the multitask prediction model that updates the model parameters with the trajectory prediction error in recent steps. Inspired by extended Kalman filter (EKF), a base adaptation algorithm modified EKF with forgetting factor (MEKF) is introduced. In order to improve the performance of MEKF, generalized exponential moving average filtering techniques are adopted. Then this article introduces a dynamic multiepoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with moving average and dynamic multiepoch strategy (MEKF_{MA − ME} ). We empirically study the best set of parameters to adapt in the multitask prediction model and demonstrate the effectiveness of the proposed adaptation algorithms to reduce the prediction error.     

Enhanced adaptive control for a benchmark piezoelectric‐actuated system via fuzzy approximation

This paper investigates the problem of the high precision tracking control of piezoelectric actuators (PEAs) without using the inverse of the uncertain hysteresis. Based on fuzzy system approximator and particle swarm optimization (PSO) algorithm, a proposed enhanced adaptive controller is developed. The proposed controller provides fast and robust adaptation simultaneously with guaranteed desired transient performance. Moreover, it has a simple form and requires fewer adaptation parameters. The adaptation gain is determined via PSO algorithm. The proposed controller is tested on a lab‐scale PEA system. Experimental results with comparative studies with different techniques have been developed. The simulation results reveal that the proposed controller outperforms the other controllers in terms of normalized root‐mean‐square and maximum tracking errors for different frequencies.     

Event-triggered adaptive backstepping tracking control for a class of nonlinear fractional order systems

This article investigates the problem of event-trigger based adaptive backstepping control for a class of nonlinear fractional order systems. By introducing an appropriate transformation of frequency distributed model, the fractional-order indirect Lyapunov method with is obtained. In addition, the event-triggered adaptive controller is developed by employing the event-triggered control approach. Meanwhile, by the proposed adaptive control scheme, all the closed-loop signals are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Finally, simulation results are provided to testify the availability of the presented controller.     

LPNN-based approach for LASSO problem via a sequence of regularized minimizations

In compressive sampling theory, the least absolute shrinkage and selection operator (LASSO) is a representative problem. Nevertheless, the non-differentiable constraint impedes the use of Lagrange programming neural networks (LPNNs). We present in this article the -LPNN model, a novel algorithm that tackles the LASSO minimization together with the underlying theory support. First, we design a sequence of smooth constrained optimization problems, by introducing a convenient differentiable approximation to the non-differentiable -norm constraint. Next, we prove that the optimal solutions of the regularized intermediate problems converge to the optimal sparse signal for the LASSO. Then, for every regularized problem from the sequence, the -LPNN dynamic model is derived, and the asymptotic stability of its equilibrium state is established as well. Finally, numerical simulations are carried out to compare the performance of the proposed -LPNN algorithm with both the LASSO-LPNN model and a standard digital method.     

An adaptive learning and control architecture for mitigating sensor and actuator attacks in connected autonomous vehicle platoons

In this paper, we develop an adaptive control algorithm for addressing security for a class of networked vehicles that comprise a formation of human‐driven vehicles sharing kinematic data and an autonomous vehicle in the aft of the vehicle formation receiving data from the preceding vehicles through wireless vehicle‐to‐vehicle communication devices. Specifically, we develop an adaptive controller for mitigating time‐invariant state‐dependent adversarial sensor and actuator attacks while guaranteeing uniform ultimate boundedness of the closed‐loop networked system. Furthermore, an adaptive learning framework is presented for identifying the state space model parameters based on input‐output data. This learning technique utilizes previously stored data as well as current data to identify the system parameters using a relaxed persistence of excitation condition. The effectiveness of the proposed approach is demonstrated by an illustrative numerical example involving a platoon of connected vehicles.     

Robust real-time parameter estimation for linear systems affected by external noises and uncertainties

A robust adaptive parameter estimation method, based on the application of a full-order filter capable of rejecting exogenous disturbances, is proposed in this article. A linear matrix inequality condition is proposed to synthesize the desired robust filter, assuming the presence of a known input control with constraints. The filter uses the output of the system to estimate the desired signal that will be employed in the adaptive estimation procedure and, to assure robustness to exogenous noise and unstructured uncertainties, the guaranteed cost is minimized in the synthesis condition. The filtered signals are then applied to an adaptive procedure to estimate the unknown system's internal parameters, which is also proposed in this article. It is shown that lower values for the guaranteed cost from the disturbance input to the error output of the filter imply more accurate estimations of the parameters. The efficiency of the proposed estimation technique is illustrated through a simulated model and a physical system has been considered to validate real-time estimation.     

Robust adaptive passivity-based PIλD control

In this article, we develop proportional, fractional-integral, and derivative () controllers for the regulation and tracking problems of nonlinear systems. The analytic results are obtained by extending the passivity-based approach to include fractional operators. Robustness under parametric uncertainty is dealt with by a combination with an adaptive scheme. It is also shown their robustness under additive noise and their robustness under uncertainty in the derivation order. The advantages in the controlled system performance and in the control energy consumption in comparison to classic PI and proportional integral derivative controllers are illustrated for the quadratic boost converter and a benchmark system in the literature.     

Finite‐time dissipativity‐based filter design for networked control systems

This paper investigates the problem of finite‐time boundedness and dissipativity‐based filter design for networked control systems together with parameter uncertainties and random packet dropouts. The packet transmission information is defined by using Bernoulli distributed white sequence which characterizes the measurement conditions. Some new sufficient conditions are established to ensure that the filtering error system is stochastically finite‐time bounded and strictly finite‐time dissipative. These sufficient conditions to design the filter parameters are derived by using linear matrix inequalities and reciprocally convex approach. Finally, an example is given to validate the effectiveness of the proposed filter design.     

Mixed and passive filtering for linear switched systems with average dwell time

In this paper, a new performance index is proposed for switched systems. The new performance index can be viewed as the mixed weighted and passivity performance. This new performance index covers the weighted performance and the passivity performance as special cases. Based on this new performance index, the weighted filtering problem and the passive filtering problem of linear switched systems with unstable subsystems are solved in a unified framework. The states of the filtering error system constructed by the augmentation technique will be divergent when unstable subsystems are activated. To overcome this problem, a set of mode‐dependent filters of a Luenberger‐like observer type is constructed. The multiple Lyapunov function approach and the average dwell‐time technique are employed to solve the mixed filtering problem. New sufficient conditions for the existence of mixed and passive filters are developed, which ensure the filtering error system to be asymptotically stable with a prescribed mixed and passivity performance index. Moreover, the desired mixed and passive filters can be constructed by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the applicability and advantage of the obtained results.     

Adaptive neural network asymptotic tracking control for a class of stochastic nonlinear systems with unknown control gains and full state constraints

This article addresses the issue of adaptive intelligent asymptotic tracking control for a class of stochastic nonlinear systems with unknown control gains and full state constraints. Unlike the existing systems in the literature in which the prior knowledge of the control gains is available for the controller design, the salient feature of our considered system is that the control gains are allowed to be unknown but have a positive sign. By introducing an auxiliary virtual controller and employing the new properties of Numbness functions, the major technique difficulty arising from the unknown control gains is overcome. At the same time, the -type barrier Lyapunov functions are introduced to prevent the violation of the state constraints. What's more, neural networks' universal online approximation ability and gain suppression inequality technology are combined in the frame of adaptive backstepping design, so that a new control method is proposed, which cannot only realize the asymptotic tracking control in probability, but also meet the requirement of the full state constraints imposed on the system. In the end, the simulation results for a practical example demonstrate the effectiveness of the proposed control method.     

filtering for switched discrete‐time systems under asynchronous switching: A dwell‐time dependent Lyapunov functional method

In this article, the filtering problem for switched discrete‐time linear systems under asynchronous switching is addressed in the framework of dwell time, where ‘asynchronous switching’ covers more general and practical cases, for example, the switching lags caused by mode identification process are taken into consideration. Firstly, a novel dwell‐time dependent Lyapunov function (DTDLF) is introduced to solve stability and ?₂ gain analysis problems. The main advantage of DTDLF approach is that the derived conditions are all convex in system matrices, so it is convenient to be applied into filter design with performance instead of weighted performance as many other previous results. Thus, on the basis of DTLDF, a dwell‐time dependent filter with time‐varying structure is proposed to achieve the desirable non‐weighted filtering performance. It is notable that the proposed approach can also easily characterize the relationships among filtering performance, dwell time, and asynchronous time. Two examples are provided to validate the theoretical findings in this paper. Copyright © 2014 John Wiley & Sons, Ltd.     

A method for rejecting 3k-th harmonics in bandpass 6N-path filters

In this paper, a method for 3 -th ( ) harmonics rejection in 6 -path filters is proposed, and the related analysis is provided. Using a single-ended-input to differential-output structure, the filter selectivity around even harmonics are also suppressed. Accordingly, a proof-of-concept band-pass filter is designed, and postlayout simulations in the 90-nm CMOS technology are carried out, which covers an input frequency range from 200 MHz to 1.2 GHz with a channel bandwidth of 10 to 15.5 MHz. The achieved third harmonic rejection at 1-GHz local oscillator (LO) frequency is about 43 dB. Over the entire radio frequency (RF) range, the in-band IIP3 and noise figure are better than 1.5 dBm and 5.3 dB, respectively. The power consumption of the analog circuitry is 21 mW from the 1.2-V supply, whereas the digital clock generation circuitry consumes between 0.9 and 5.2 mW, depending on the center frequency of the filter.     

Adaptive dynamic programming for tracking design of uncertain nonlinear systems with disturbances and input constraints

In this paper, the tracking controller is designed for uncertain nonlinear systems with external disturbances and input constraints. A discounted nonquadratic function is introduced, which encodes the constrained input into the performance. The key difficulty for tracking control is the requirement of solving the tracking Hamilton‐Jacobi‐Isaacs equation, which is a partial differential equation. It is impossible or extremely difficult to solve analytically even in simple cases. To overcome the difficulty, an online model‐free integral reinforcement learning (IRL) algorithm is proposed to learn online in real time the solution to the tracking Hamilton‐Jacobi‐Isaacs equation without requiring any knowledge of system dynamics. To implement it, critic‐actor‐disturbance neural networks (NNs) are built, and the 3 NNs are updated simultaneously. Stability and convergence analyses are shown by the Lyapunov method. In addition, a robust term is added to the controller to attenuate the effect of NN approximation errors, which leads to the asymptotic stability of the closed‐loop systems. Finally, 2 simulation examples show the effectiveness of the proposed algorithm.     

An efficient hardware implementation of the elliptic curve cryptographic processor over prime field,

Elliptic curve cryptography (ECC) schemes are widely adopted for the digital signature applications due to their key sizes, hardware resources, and higher security per bit than Rivest-Shamir-Adleman (RSA). In this work, we proposed a new hardware architecture for elliptic curve scalar multiplication (ECSM) in Jacobian coordinates over prime field, . This is a combination of point doubling and point addition architecture, implemented using resource sharing concept to achieve high speed and low hardware resources, which is synthesized both in field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC). The proposed ECSM takes 1.76 and 2.44 ms on Virtex-7 FPGA platform over 224-bit and 256-bit prime field, respectively. Similarly, ASIC (GF 40 nm complementary metal-oxide semiconductor [CMOS]) technology implementation provides energy efficient with a latency of 0.46 and 0.6 ms over prime field and , respectively. This design provides better area-delay product and high throughput value in both FPGA and ASIC when compared with other designs.     

A robust adaptive control architecture for disturbance rejection and uncertainty suppression with L ∞ transient and steady‐state performance guarantees

In this paper, a new adaptive control architecture for linear and nonlinear uncertain dynamical systems is developed to address the problem of high‐gain adaptive control. Specifically, the proposed framework involves a new and novel controller architecture involving a modification term in the update law that minimizes an error criterion involving the distance between the weighted regressor vector and the weighted system error states. This modification term allows for fast adaptation without hindering system robustness. In particular, we show that the governing tracking closed‐loop system error equation approximates a Hurwitz linear time‐invariant dynamical system with input–output signals. This key feature of our framework allows for robust stability analysis of the proposed adaptive control law using system theory. We further show that by properly choosing the design parameters in the modification term, we can guarantee a desired bandwidth of the adaptive controller, guaranteed transient closed‐loop performance, and an a priori characterization of the size of the ultimate bound of the closed‐loop system trajectories. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach. Copyright © 2012 John Wiley & Sons, Ltd.     

FPGA-based system for effective IQ imbalance mitigation of RF power amplifiers

To provide an adequate signal integrity to a power amplifier (PA), we propose a digital system for the degradation at the transmitter path, and it is implemented on a field-programmable gate array (FPGA) board. The proposed system offers the following features: A -ary quadrature amplitude modulation (QAM) digital signal generation and in-phase/quadrature (IQ) imbalance mitigation, and by default, it performs as a predistortion model extraction from PA-measured data. The simulations and tests provided are performed to effectively verify the PA linearity by using 256-QAM signals. The nonlinearities are predicted as a reliable solution for linearizing the PA from measurements of AM/AM and AM/PM conversion curves. The performance is evaluated in terms of linearity, computation complexity, and FPGA hardware synthesis according to a dependability compliance of digital signal processing. Finally, the model is validated with input/output data observations to linearize the model with a fitting normalized mean squared error (NMSE) of around  dB, a spurious free dynamic range of 40 dBm, and an adjacent channel power ratio reduction by  dBm, for a class-AB broadband radio frequency PA GaN HEMT of 10 W working at 2.34 GHz.     

Efficient implementation of mixed-precision multiply-accumulator unit for AI algorithms

Many of the modern deep learning, machine learning, and artificial intelligence algorithms use adders, multipliers, and multiply-accumulators (MACs) with mixed precisions. In general, fixed-point and floating-point adders, multipliers, and MACs are used in mixed-precision hardware, such that the above algorithm can choose appropriate hardware for its processing. This paper proposes an efficient mixed-precision MAC circuit that can perform fixed-point and floating-point operations. The proposed design uses Han-Carlson adder with late carry or end-around carry in its accumulator design; as a result, delay and energy of the circuit reduce by and respectively, when compared with the existing designs from the literature.     

A new method to realize nonrational driving-point functions

This paper presents a new synthesis procedure for nonrational driving-point functions by defining and using the operator. The operator is defined, and its properties are explored. Applying the Stieltjes transform on the operator, the Padé approximant or the continued fraction form of the nonrational network function can be achieved with reduced computational complexity. Thus, the classical Foster and Cauer form or other techniques may be applied to synthesize network functions. The application of this work is demonstrated by considering certain functions such as the square root, inverse tangent, logarithm, and Lambert's W function. A set of conditions called synthesis criteria is proposed, which should be satisfied by a nonrational function to be realizable.