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We consider both finite-horizon and infinite-horizon versions of a dynamic game with $N$ selfish players who observe their types privately and take actions that are publicly observed. Players’ types evolve as conditionally independent Markov processes, conditioned on their current actions. Their actions and types jointly determine their instantaneous rewards. In dynamic games with asymmetric information, a widely used concept of equilibrium is perfect Bayesian equilibrium (PBE) which consists of a strategy and belief pair that simultaneously satisfy sequential rationality and belief consistency. In general, there does not exist a universal algorithm that decouples the interdependence of strategies and beliefs over time in calculating PBE. In this paper, for the finite-horizon game with independent types, we develop a two-step backward–forward recursive algorithm that sequentially decomposes the problem (w.r.t. time) to obtain a subset of PBEs, which we refer to as structured Bayesian perfect equilibria (SPBE). In such equilibria, a player's strategy depends on his/her history only through a common public belief and its current private type. The backward recursive part of this algorithm defines an equilibrium generating function. Each period in the backward recursion involves solving a fixed-point equation on the space of probability simplexes for every possible belief on types. Using this function, equilibrium strategies and beliefs are generated through a forward recursion. We then extend this methodology to the infinite-horizon model, where we propose a time-invariant single-shot fixed-point equation, which in conjunction with a forward recursive step, generates the SPBE. Sufficient conditions for the existence of SPBE are provided. With our proposed method, we find equilibria that exhibit signaling behavior. This is illustrated with the help of a concrete public goods example. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate the optimal synchronization problem for a group of generic linear systems with input saturation. To seek the optimal controller, Hamilton–Jacobi–Bellman (HJB) equations involving nonquadratic input energy terms in coupled forms are established. The solutions to these coupled HJB equations are further proven to be optimal and the induced controllers constitute interactive Nash equilibrium. Due to the difficulty to analytically solve HJB equations, especially in coupled forms, and the possible lack of model information of the systems, we apply the data-based off-policy reinforcement learning algorithm to learn the optimal control policies. A byproduct of this off-policy algorithm is shown that it is insensitive to probing noise that is exerted to the system to maintain persistence of excitation condition. In order to implement this off-policy algorithm, we employ actor and critic neural networks to approximate the controllers and the cost functions. Furthermore, the estimated control policies obtained by this presented implementation are proven to converge to the optimal ones under certain conditions. Finally, an illustrative example is provided to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper presents novel modifications to a predictor-based model reference adaptive controller (PMRAC) to compensate for input delays in uncertain nonlinear systems. A delay-dependent stability condition, derived using a Lyapunov–Krasovskii functional, indicates that there exists a time delay margin, below which the asymptotic stability of the tracking and estimation errors as well as the input-to-state stability of the closed-loop system are guaranteed. Numerical experiments are carried out with comparisons to the original PMRAC and to a finite spectrum assignment scheme to validate the theoretical claims. The results also illustrate the effectiveness of the proposed delay compensation formulation and the asymptotic behaviors of the tracking and estimation errors. [ABSTRACT FROM AUTHOR]

A study on the stability of continuous-time difference equations is presented. Based on the construction of the analogous of the delay Lyapunov matrix for this class of equations, a complete type Lyapunov–Krasovskii functional is proposed, that allows to present necessary stability conditions. Examples are given throughout the paper to illustrate these conditions. [ABSTRACT FROM AUTHOR]

Aggregate load modeling is of fundamental importance for systematic analysis and design of various demand response strategies. Instead of keeping track of the trajectories of individual loads, the aggregate modeling problem focuses on characterizing the density evolution of the load population. Most existing models are only applicable to thermostatically controlled loads (TCLs) with first-order linear dynamics. This paper develops a unified aggregate modeling approach that can be used for general TCLs as well as deferrable loads. We propose a deterministic hybrid system model to describe individual load dynamics under demand response rules, and develop a general stochastic hybrid system (SHS) model to capture the population dynamics. We also derive a set of partial differential equations (PDEs) that govern the probability density evolution of the SHS. Our results cannot be obtained using the existing SHS tools in the literature as the proposed SHS model involves both random and deterministic switchings with general switching surfaces in multidimensional domains. The derived PDE model includes many existing aggregate load modeling results as special cases and can be used in many other realistic modeling scenarios that have not been studied in the literature. [ABSTRACT FROM AUTHOR]

This paper considers the backstepping design of state feedback controllers for coupled linear parabolic partial integro-differential equations (PIDEs) of Volterra-type with distinct diffusion coefficients, spatially varying parameters and mixed boundary conditions. The corresponding target system is a cascade of parabolic PDEs with local couplings allowing a direct specification of the closed-loop stability margin. The determination of the state feedback controller leads to kernel equations, which are a system of coupled linear second-order hyperbolic PIDEs with spatially varying coefficients and rather unusual boundary conditions. By extending the method of successive approximations for the scalar case to the considered system class, the well-posedness of these kernel equations is verified by providing a constructive solution procedure. This results in a systematic method for the backstepping control of coupled parabolic PIDEs as well as PDEs. The applicability of the new backstepping design method is confirmed by the stabilization of two coupled parabolic PIDEs with Dirichlet/Robin unactuated boundaries and a coupled Neumann actuation. [ABSTRACT FROM AUTHOR]

In this paper, a learning-based predictive control (LPC) scheme is proposed for adaptive optimal control of discrete-time nonlinear systems under stochastic disturbances. The proposed LPC scheme is different from conventional model predictive control (MPC), which uses open-loop optimization or simplified closed-loop optimal control techniques in each horizon. In LPC, the control task in each horizon is formulated as a closed-loop nonlinear optimal control problem and a finite-horizon iterative reinforcement learning (RL) algorithm is developed to obtain the closed-loop optimal/suboptimal solutions. Therefore, in LPC, RL and adaptive dynamic programming (ADP) are used as a new class of closed-loop learning-based optimization techniques for nonlinear predictive control with stochastic disturbances. Moreover, LPC also decomposes the infinite-horizon optimal control problem in previous RL and ADP methods into a series of finite horizon problems, so that the computational costs are reduced and the learning efficiency can be improved. Convergence of the finite-horizon iterative RL algorithm in each prediction horizon and the Lyapunov stability of the closed-loop control system are proved. Moreover, by using successive policy updates between adjoint time horizons, LPC also has lower computational costs than conventional MPC which has independent optimization procedures between two different prediction horizons. Simulation results illustrate that compared with conventional nonlinear MPC as well as ADP, the proposed LPC scheme can obtain a better performance both in terms of policy optimality and computational efficiency. [ABSTRACT FROM AUTHOR]

This paper considers the stability problem of a linear time invariant system in feedback with a string equation. A new Lyapunov functional is proposed using augmented states that enriches and encompasses the classical functionals of the literature. It results in tractable stability conditions expressed in terms of linear matrix inequalities. This methodology follows from the application of the Bessel inequality to the projections over the Legendre polynomials. Numerical examples illustrate the potential of our approach through three scenario: a stable ODE perturbed by the PDE, an unstable open-loop ODE, and an unstable closed-loop ODE stabilized by the PDE. [ABSTRACT FROM AUTHOR]

The problem of low-rank approximation with convex constraints, which appears in data analysis, system identification, model order reduction, low-order controller design, and low-complexity modeling is considered. Given a matrix, the objective is to find a low-rank approximation that meets rank and convex constraints while minimizing the distance to the matrix in the squared Frobenius norm. In many situations, this nonconvex problem is convexified by nuclear-norm regularization. However, we will see that the approximations obtained by this method may be far from optimal. In this paper, we propose an alternative convex relaxation that uses the convex envelope of the squared Frobenius norm and the rank constraint. With this approach, easily verifiable conditions are obtained under which the solutions to the convex relaxation and the original nonconvex problem coincide. A semidefinite programming representation of the convex envelope is derived, which allows us to apply this approach to several known problems. Our example on optimal low-rank Hankel approximation/model reduction illustrates that the proposed convex relaxation performs consistently better than nuclear-norm regularization and may outperform balanced truncation. [ABSTRACT FROM AUTHOR]

The recently proposed DeGroot–Friedkin model describes the dynamical evolution of individual social power in a social network that holds opinion discussions on a sequence of different issues. This paper revisits that model, and uses nonlinear contraction analysis, among other tools, to establish several novel results. First, we show that for a social network with constant topology, each individual's social power converges to its equilibrium value exponentially fast, whereas previous results only concluded asymptotic convergence. Second, when the network topology is dynamic (i.e., the relative interaction matrix may change between any two successive issues), we show that the initial (perceived) social power of each individual is exponentially forgotten. Specifically, individual social power is dependent only on the dynamic network topology, and initial social power is forgotten as a result of sequential opinion discussion. Finally, we provide an explicit upper bound on an individual's social power as the number of issues discussed tends to infinity; this bound depends only on the network topology. Simulations are provided to illustrate our results. [ABSTRACT FROM AUTHOR]

It is well known that for a linear time-invariant plant whose transfer function has relative degree $n^*$ and stable zeros, a model reference adaptive control (MRAC) system ensures that the plant output $y(t)$ asymptotically tracks the output $y_m(t)$ of a stable reference model system of the same relative degree $n^*$ : $\lim _{t \rightarrow \infty } (y(t) - y_m(t)) = 0$. In this note, it is shown that under the same MRAC design conditions without the knowledge of the plant parameters, an MRAC system ensures that the tracking error $e(t)= y(t) - y_m(t)$ has the stronger higher order convergence property: $\lim _{t\rightarrow \infty } \frac{d^i e(t)}{d t^i} = 0$ , for $i=0,1,\ldots, n^*\text{$-$}1$. Such a new MRAC system property leads to several new results of adaptive stabilization and tracking control using either state feedback or output feedback, as clarified in this paper. [ABSTRACT FROM AUTHOR]

This paper studies regional boundary stabilization of nonlinear Schrödinger equation with state delay and bounded internal disturbance. The boundary constrained control law is designed by using the backstepping method. Regional input-to-state stability of the perturbed system with time-delay is established by a Lyapunov function and a generalized Halanay's inequality. Estimates on the set of initial conditions are found starting from which the solutions are exponentially attracted to a bounded set. A numerical example demonstrates the efficiency of the results. [ABSTRACT FROM AUTHOR]

In this paper, we consider a delay differential inclusion $\dot{x}(t)\in F(t,x_t)$ , where $x_t$ denotes the history function of $x(\cdot)$ along an interval of time. We extend the celebrated Filippov's theorem to this case. Then, we further generalize this theorem to the case when the state variable $x$ is constrained to the closure of an open subset $K\subset \mathbb {R}^n$. Under a new “inward pointing condition,” we give a relaxation result stating that the set of trajectories lying in the interior of the state constraint is dense in the set of constrained trajectories of the convexified inclusion ${\dot{x}(t)\in \mbox{co}\,F(t,x_t)}$. [ABSTRACT FROM AUTHOR]

This paper concentrates on the adaptive critic design (ACD) issue for a class of uncertain multi-input multioutput (MIMO) nonlinear discrete-time systems preceded by unknown backlashlike hysteresis. The considered systems are in a block-triangular pure-feedback form, in which there exist nonaffine functions and couplings between states and inputs. This makes that the ACD-based optimal control becomes very difficult and complicated. To this end, the mean value theorem is employed to transform the original systems into input–output models. Based on the reinforcement learning algorithm, the optimal control strategy is established with an actor-critic structure. Not only the stability of the systems is ensured but also the performance index is minimized. In contrast to the previous results, the main contributions are: 1) it is the first time to build an ACD framework for such MIMO systems with unknown hysteresis and 2) an adaptive auxiliary signal is developed to compensate the influence of hysteresis. In the end, a numerical study is provided to demonstrate the effectiveness of the present method. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) sharpening, which aims at fusing an observable low spatial resolution (LR) HSI (LR-HSI) with a high spatial resolution (HR) multispectral image (HR-MSI) of the same scene to acquire an HR-HSI, has recently attracted much attention. Most of the recent HSI sharpening approaches are based on image priors modeling, which are usually sensitive to the parameters selection and time-consuming. This paper presents a deep HSI sharpening method (named DHSIS) for the fusion of an LR-HSI with an HR-MSI, which directly learns the image priors via deep convolutional neural network-based residual learning. The DHSIS method incorporates the learned deep priors into the LR-HSI and HR-MSI fusion framework. Specifically, we first initialize the HR-HSI from the fusion framework via solving a Sylvester equation. Then, we map the initialized HR-HSI to the reference HR-HSI via deep residual learning to learn the image priors. Finally, the learned image priors are returned to the fusion framework to reconstruct the final HR-HSI. Experimental results demonstrate the superiority of the DHSIS approach over existing state-of-the-art HSI sharpening approaches in terms of reconstruction accuracy and running time. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to provide a generalization of the so-called Simultaneous Long–Short (SLS) stock-trading result in the feedback control literature. The significance of the SLS strategy is that it guarantees a “win” in expected value of profits. Whereas previous work involves use of static linear feedback to establish this robust positive expectation property of the gain–loss function, here we obtain the same result for the more general case of PI control. In this SLS setting, robustness is assured with respect to stock prices governed by a Geometric Brownian Motion whose drift and volatility parameters are unknown to the trader. This technical note also includes numerical simulations. [ABSTRACT FROM AUTHOR]

A smart grid connects several electricity consumers/producers, e.g., wind/solar/storage farms, fossil-fuel plants, industrial/commercial loads, or load-serving aggregators, all modeled as stochastic dynamical systems. In each time period, each consumes/supplies some electrical energy. Each such agent's utility is the benefit accrued from its consumption or the negative of its generation cost. The social welfare, the sum of all these utilities, is the total benefit accrued from all consumption minus the total cost of generation. The independent system operator is charged with maximizing the social welfare subject to total generation equalling consumption in each time period, but without the agents revealing their system states, dynamic models, utility functions, or uncertainties. It has to announce prices after interacting with agents via bid-price interactions. This paper examines the case where the agents respond in a compliant price-taking manner. It is shown that there is an iterative bid-price interaction, where agents respond to price announcements by complying to the requirement to announce their optimal responses according to their true stochastic model, or, in the case where the agents are linear quadratic Gaussian (LQG) systems, according to deterministic versions of their true stochastic models, that leads to the same global maximum value of social welfare attainable if all agents had pooled their information. In the important LQG case, the bid-price iteration is dramatically simple, exchanging only real-valued vectors of future prices and consumptions/generations at each time step. Agents need not even know of the existence of other agents. DC power flow equations can also be incorporated. The results may be of broader interest vis-a-vis general equilibrium theory of economics for stochastic dynamic agents. [ABSTRACT FROM AUTHOR]

We detail in this paper the importance of a change of strategy for the delay robust control of systems composed of two linear first-order hyperbolic equations. One must go back to the classical tradeoff between convergence rate and delay robustness. More precisely, we prove that, for systems with strong reflections, canceling the reflection at the actuated boundary will yield zero delay robustness. Indeed, for such systems, using a backstepping controller, the corresponding target system should preserve a small amount of this reflection to ensure robustness to a small delay in the loop. This implies, in some cases, giving up finite time convergence. [ABSTRACT FROM AUTHOR]

The cooperative robust output regulation problem for a class of second-order nonlinear multiagent systems with an exactly known exosystem has been studied recently. This paper further studies the same problem for the same class of systems subject to an unknown exosystem. We first show that the problem can be converted into the adaptive stabilization problem for an augmented system composed of the given multiagent system and the distributed internal model. Due to the unknown parameters in the exosystem, the augmented system is a nonlinear multi-input system with both linearly parameterized uncertainties and norm-bounded nonlinear uncertainty. By combining the adaptive control technique and the robust control technique, we will solve our problem by a distributed adaptive robust state feedback controller. [ABSTRACT FROM AUTHOR]

This paper presents a fully digital implementation of a memristor hardware (HW) simulator, as the core of an emulator, based on a behavioral model of voltage-controlled threshold-type bipolar memristors. Compared to other analog solutions, the proposed digital design is compact, easily reconfigurable, demonstrates very good matching with the mathematical model on which it is based, and complies with all the required features for memristor emulators. We validated its functionality using Altera Quartus II and ModelSim tools targeting low-cost yet powerful field-programmable gate array families. We tested its suitability for complex memristive circuits as well as its synapse functioning in artificial neural networks, implementing examples of associative memory and unsupervised learning of spatiotemporal correlations in parallel input streams using a simplified spike-timing-dependent plasticity. We provide the full circuit schematics of all our digital circuit designs and comment on the required HW resources and their scaling trends, thus presenting a design framework for applications based on our HW simulator. [ABSTRACT FROM AUTHOR]

Feature selection aims to select a subset of features from high-dimensional data according to a predefined selecting criterion. Sparse learning has been proven to be a powerful technique in feature selection. Sparse regularizer, as a key component of sparse learning, has been studied for several years. Although convex regularizers have been used in many works, there are some cases where nonconvex regularizers outperform convex regularizers. To make the process of selecting relevant features more effective, we propose a novel nonconvex sparse metric on matrices as the sparsity regularization in this paper. The new nonconvex regularizer could be written as the difference of the $\ell _{2,1}$ norm and the Frobenius ($\ell _{2,2}$) norm, which is named the $\ell _{2,1-2}$. To find the solution of the resulting nonconvex formula, we design an iterative algorithm in the framework of ConCave–Convex Procedure (CCCP) and prove its strong global convergence. An adopted alternating direction method of multipliers is embedded to solve the sequence of convex subproblems in CCCP efficiently. Using the scaled cluster indictors of data points as pseudolabels, we also apply $\ell _{2,1-2}$ to the unsupervised case. To the best of our knowledge, it is the first work considering nonconvex regularization for matrices in the unsupervised learning scenario. Numerical experiments are performed on real-world data sets to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The decision-making process, which is regarded as cognitive and ubiquitous, has been exploited in diverse fields, such as psychology, economics, and artificial intelligence. This paper considers the problem of modeling agent cognition in a class of game-theoretic decision-making scenarios called extensive games. We present a novel framework in which artificial neural networks are incorporated to simulate agent cognition regarding the structure of the underlying game and the goodness of the game situations therein. An algorithmic procedure is investigated to describe the process for solving games with cognition, and then, a new equilibrium concept is proposed as a refinement of the classical one—subgame perfect equilibrium—by involving players’ cognitive reasoning. Moreover, a series of results concerning the computational complexity, soundness, and completeness of the algorithm, as well as the existence of an equilibrium solution, is obtained. This framework, which is shown to be general enough to model the way in which AlphaGo plays Go, may offer a means for bridging the gap between theoretical models and practical problem-solving. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of designing optimal sampled-data state feedback control for continuous-time Markov jump linear systems. Stability and performance robustness against polytopic uncertainty acting on the system parameters including the transition rate matrix are analyzed. The main goal is to characterize the optimal solution of this class of problems in the context of ${\mathcal H}_2$ and ${\mathcal H}_\infty$ performances. The theoretical achievements are based on the direct application of the celebrated Bellman's Principle of Optimality expressed in terms of the dynamic programming equation applied to the time interval corresponding to two successive sampling instants. The design conditions are expressed through differential linear matrix inequalities. Examples are solved for illustration. [ABSTRACT FROM AUTHOR]

In this paper, we prove a theorem of linearized asymptotic stability for nonlinear fractional differential equations with a time delay. By using the method of linearization of a nonlinear equation along an orbit (Lyapunov's first method), we show that an equilibrium of a nonlinear Caputo fractional delay differential equation is asymptotically stable if its linearization at the equilibrium is asymptotically stable. Our approach is based on a technique that converts the linear part of the equation into a diagonal one. Then by using the properties of generalized Mittag-Leffler functions, the construction of an associated Lyapunov–Perron operator, and the Banach contraction mapping theorem, we obtain the desired result. [ABSTRACT FROM AUTHOR]

This paper proposes models of learning processes in teams of individuals who collectively execute a sequence of tasks and whose actions are determined by individual skill levels and networks of interpersonal appraisals and influence. The closely-related proposed models have increasing complexity, starting with a centralized manager-based assignment and learning model, and finishing with a social model of interpersonal appraisal, assignments, learning, and influences. We show how rational optimal behavior arises along the task sequence for each model, and discuss conditions of suboptimality. Our models are grounded in replicator dynamics from evolutionary games, influence networks from mathematical sociology, and transactive memory systems from organization science. [ABSTRACT FROM AUTHOR]

Periodic optimal control problems (POCPs) based on dynamic models holding invariants are often problematic to treat using standard numerical methods. The difficulty stems from a failure of standard constraint qualifications and typically hinders the convergence of the numerical solver, or even defeats it. Optimization problems having weak constraint qualifications can be treated using dedicated solvers, at the price of a more involved algorithmic. In this paper, we analyze the constraint qualification of POCPs holding invariants, and propose three simple and computationally inexpensive modifications of the formulation that allow for a recovery of linear independence constraint qualification, while not affecting the second-order sufficient conditions for optimality. Hence, the resulting POCP can be tackled via standard solvers, without special treatment. The application of these approaches is detailed for the case of POCPs holding index-reduced differential-algebraic equations and representations of the $\mathrm{SO}\left(3\right)$ Lie group. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have emerged as a powerful illustrative diagram for the brain. Unveiling the mechanism of neural-dynamic evolution is one of the crucial steps toward understanding how the brain works and evolves. Inspired by the universal existence of impulses in many real systems, this paper formulates a type of hybrid NNs (HNNs) with impulses, time delays, and interval uncertainties, and studies its global dynamic evolution by a robust interval analysis. The HNNs incorporate both continuous-time implementation and impulsive jump in mutual activations, where time delays and interval uncertainties are represented simultaneously. By constructing a Banach contraction mapping, the existence and uniqueness of the equilibrium of the HNN model are proved and analyzed in detail. Based on nonsmooth Lyapunov functions and delayed impulsive differential equations, new criteria are derived for ensuring the global robust exponential stability of the HNNs. Convergence analysis together with illustrative examples show the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

In this paper, the effect of input noise, output node stochastic, and recurrent state noise on the Wang $k$ WTA is analyzed. Here, we assume that noise exists at the recurrent state $y(t)$ and it can either be additive or multiplicative. Besides, its dynamical change (i.e., $dy/dt$) is corrupted by noise as well. In sequel, we model the dynamics of $y(t)$ as a stochastic differential equation and show that the stochastic behavior of $y(t)$ is equivalent to an Ito diffusion. Its stationary distribution is a Gibbs distribution, whose modality depends on the noise condition. With moderate input noise and very small recurrent state noise, the distribution is single modal and hence $y(\infty)$ has high probability varying within the input values of the $k$ and $k+1$ winners (i.e., correct output). With small input noise and large recurrent state noise, the distribution could be multimodal and hence $y(\infty)$ could have probability varying outside the input values of the $k$ and $k+1$ winners (i.e., incorrect output). In this regard, we further derive the conditions that the $k$ WTA has high probability giving correct output. Our results reveal that recurrent state noise could have severe effect on Wang $k$ WTA. But, input noise and output node stochastic could alleviate such an effect. [ABSTRACT FROM AUTHOR]

This paper proposes a Bayesian nonparametric regression model of panel data for sequential pattern classification. The proposed method provides a flexible and parsimonious model that allows both time-independent spatial variables and time-dependent exogenous variables to be predictors. Not only this method improves the accuracy of parameter estimation for limited data, but also it facilitates model interpretation by identifying statistically significant predictors with hypothesis testing. Moreover, as the data length approaches infinity, posterior consistency of the model is guaranteed for general data-generating processes under regular conditions. The resulting model of panel data can also be used for sequential classification. The proposed method has been tested by numerical simulation, then validated on an econometric public data set, and subsequently validated for detection of combustion instabilities with experimental data that have been generated in a laboratory environment. [ABSTRACT FROM AUTHOR]

In this paper, the disturbance attenuation problem is formulated and solved for a class of linear hybrid systems in the presence of periodic jumps. The results are achieved, both in the finite and infinite horizon cases, by borrowing ideas from the theory of dynamic games. In the considered formulation, independent disturbances affecting the continuous-time and the discrete-time components of the hybrid system are allowed. Moreover, the analysis is carried out by introducing easily verifiable conditions, involving the definition of a Monodromy Riccati Equation , i.e., a classical Riccati equation defined for the one-period discrete-time equivalent model. Interestingly, as a by-product, the main statements essentially characterize the solution of zero-sum noncooperative dynamic games for periodic linear hybrid systems, which is of interest per se. [ABSTRACT FROM AUTHOR]

This paper solves the problem of boundary feedback stabilization of a class of coupled ordinary differential equations–hyperbolic equations with boundary, trace, and integral nonlocal terms. Using the backstepping approach, the controller is designed by formulating an integral operator, whose kernel is required to satisfy a coupled hyperbolic partial integral differential equation. By applying the method of successive approximations, the kernel's well-posedness is given. We prove the exponential stability of the origin of the system in a suitable Hilbert space. Moreover, a wave system with nonlocal terms is stabilized by applying the above result. [ABSTRACT FROM AUTHOR]

In this paper, we describe a possible generalization of the Wasserstein-2 metric, originally defined on the space of scalar probability densities, to the space of Hermitian matrices with trace one and to the space of matrix-valued probability densities. Our approach follows a control-theoretic optimization formulation of the Wasserstein-2 metric, having its roots in fluid dynamics, and utilizes certain results from the quantum mechanics of open systems, in particular the Lindblad equation. It allows determining the gradient flow for the quantum entropy relative to this matricial Wasserstein metric. [ABSTRACT FROM AUTHOR]

In this paper, a control technique named optimized backstepping is first proposed by implementing tracking control for a class of strict-feedback systems, which considers optimization as a design philosophy of the high-order system control. The basic idea is that designing the actual and virtual controls of backstepping is the optimized solutions of the corresponding subsystems so that overall control of the high-order system is optimized. In general, optimization control is designed based on the solution of Hamilton–Jacobi–Bellman equation, but solving the equation is very difficult due to the inherent nonlinearity and intractability. In order to overcome the difficulty, the neural network (NN)-based reinforcement learning strategy of actor–critic architecture is used. In every backstepping step, the actor and critic NNs are constructed for executing control behavior and evaluating control performance, respectively. According to the Lyapunov stability theorem, it is proven that the desired control performance can be obtained. Finally, a simulation example is carried out to further demonstrate the effectiveness of the proposed control approach. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive dynamic programming-based near optimal boundary controller is developed for partial differential equations (PDEs) modeled by the uncertain Burgers’ equation under Neumann boundary condition in 2-D. Initially, Hamilton–Jacobi–Bellman equation is derived in infinite-dimensional space. Subsequently, a novel neural network (NN) identifier is introduced to approximate the nonlinear dynamics in the 2-D PDE. The optimal control input is derived by online estimation of the value function through an additional NN-based forward-in-time estimation and approximated dynamic model. Novel update laws are developed for estimation of the identifier and value function online. The designed control policy can be applied using a finite number of actuators at the boundaries. Local ultimate boundedness of the closed-loop system is studied in detail using Lyapunov theory. Simulation results confirm the optimizing performance of the proposed controller on an unstable 2-D Burgers’ equation. [ABSTRACT FROM AUTHOR]

Scalable causal discovery is an essential technology to a wide spectrum of applications, including biomedical studies and social network evolution analysis. To tackle the difficulty of high dimensionality, a number of solutions are proposed in the literature, generally dividing the original variable domain into smaller subdomains by computation intensive partitioning strategies. These approaches usually suffer significant structural errors when the partitioning strategies fail to recognize true causal edges across the output subdomains. Such a structural error accumulates quickly with the growing depth of recursive partitioning, due to the lack of correction mechanism over causally connected variables when they are wrongly divided into two subdomains, finally jeopardizing the robustness of the integrated results. This paper proposes a completely different strategy to solve the problem, powered by a lightweight random partitioning scheme together with a carefully designed merging algorithm over results from the random partitions. Based on the randomness properties of the partitioning scheme, we design a suite of tricks for the merging algorithm, in order to support propagation-based significance enhancement, maximal acyclic subgraph causal ordering, and order-sensitive redundancy elimination. Theoretical studies as well as empirical evaluations verify the genericity, effectiveness, and scalability of our proposal on both simulated and real-world causal structures when the scheme is used in combination with a variety of causal solvers known effective on smaller domains. [ABSTRACT FROM AUTHOR]

The model approximation problem is studied in this paper for switched genetic regulatory networks (GRNs) with time-varying delays. We focus on constructing a reduced-order model to approximate the high-order GRNs considered under the switching signal subject to certain constraints, such that the approximation error system between the original and reduced-order systems is exponentially stable with a disturbance attenuation performance. The stability conditions and the disturbance attenuation performance are established by utilizing two integral inequality bounding techniques and the average dwell-time method for the approximation error system. Then, the solvability conditions for the reduced-order models for the GRNs are also established using the projection method. Furthermore, the model approximation problem can be transferred into a sequential minimization problem that is subject to linear matrix inequality constraints by using the cone complementarity algorithm. Finally, several examples are provided to illustrate the effectiveness and the advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

Two important drawbacks of parallel coupled LC quadrature oscillators (P-QOSC) which have not heretofore been addressed thoroughly in the literature are obviated simultaneously in this paper. The first, has to do with the inevitable off-tune operation of the tank which results in quality factor (Q) degradation, and hence, phase noise deterioration. The other, stems from process, voltage, and temperature (PVT) variations which exacerbate quadrature accuracy. Here, a feedback loop is presented which generates a control signal by mixing the quadrature outputs and tunes the injection strength in the coupled oscillators autonomously. A complex coupling factor is implemented which compensates the phase error to prohibit phase noise degradation. The operation of proposed loop is comprehensively studied mathematically. Measurement results of a 0.18 \mu{\text m} CMOS prototype with an active area of 0.5 {\text{mm}}^{2}$ and an overall power consumption of 5 mW corroborate the functionality of the proposed method. Finally, experimental results prove FoM of 185 dB for the core oscillator and a quadrature phase error of less than 0.8^{\circ}. [ABSTRACT FROM PUBLISHER]

A new approach to augment existing noise models in a relaxation oscillator by including noise spikes during state change, an important aspect hitherto not covered in existing noise models, is proposed. Such noise spike is due to regeneration at the jump and this paper attempts to offer an explanation by viewing state change as a physical phase change in a thermodynamic system, which is also due to regeneration. The connection is established by viewing the relaxation oscillator as a combination of bistable circuit (exhibits metastablility) and a timing capacitor. Similarly the thermodynamic system exhibits physical phase change (metastability). Formula is derived to predict the “magnitude” of the noise spike, as a function of regeneration parameter, and hence design parameter such as gm, R, {I}_{0}. Simulations (Eldo) on circuits and simulations (Metropolis) on thermodynamic system as well as measurements on circuits fabricated in 0.13 \mu$m CMOS technology are consistent with theory. [ABSTRACT FROM PUBLISHER]

In this paper, loss mechanisms of monolithic high frequency Class-E PAs are studied. The switching behavior is analyzed to understand the discrepancy between common design equations and optimal design values as frequency scales up. Analytical results are verified by spice simulations. In addition, a design recipe based on loss analysis and effective output duty cycle is proposed for mm-waves PAs to boost their power added efficiency (PAE). The design approach is adopted to compare different technology nodes including the 130 nm, 90 nm, and 65 nm bulk CMOS. Newer technologies show better PAE only at the mm-waves regime. Simulations show that the PAE is boosted from 31% to 40% when the proposed design methodology is applied at 60 GHz using the 65 nm node. [ABSTRACT FROM PUBLISHER]

All-digital frequency synthesis architectures with phase and polar $\Sigma$– $\Delta$ modulation feedback loops are introduced as a means to overcome the inherent spectral-quality limitations of forward-dithered all-digital frequency synthesizers represented by the family of pulse direct digital synthesizers. The spectrum of dithered pulse direct digital synthesizers is derived first, with and without modulation, as well as that of their variations with multiple dithered paths and with colored dithering resulting in lower noise level. The limited dynamic range achieved by forward-dithered all-digital frequency synthesizers motivates the introduction of phase and polar $\Sigma$– $\Delta$ modulation feedback loop architectures which are modeled and analyzed to derive their noise transfer functions. Extensive MATLAB simulation illustrates the suppressed near-in noise and the high spurs-free dynamic range the two $\Sigma$– $\Delta$ schemes can achieve and the wideband spurs-free output of the polar one. Detailed analysis and implementation aspects of the techniques are outside the scope of the paper. [ABSTRACT FROM AUTHOR]

This paper presents comprehensive analyses of power-performance tradeoffs in current-mode logic (CML)-based transmitters via equation-based optimization and provides practical design guidelines that can help achieving optimum power efficiency. An accurate equation-based optimization framework was developed for this purpose, and using this framework, key circuit-level design parameters were found with specific design specifications and process technologies. The proposed optimization framework can determine optimum clock edge-rate (i.e., ratio of rise/fall time to clock period) and inter-stage voltage swings that enable significant power savings without affecting performance. Various transmitter design tradeoffs were analyzed for 45-nm, 65-nm, and 90-nm technologies considering data rate, clock edge-rate, and inter-stage swing optimization. Based on the analysis results, we determined the most energy-efficient data rate that can be achieved with acceptable power overhead for each technology node. Also, we identified the optimal clock edge-rate and inter-stage voltage swings in CML gates that achieved minimum transmitter power dissipation. Our analysis results indicate that, when properly optimized, overall transmitter power consumption can be reduced by up to 43% with inter-stage swing optimization with all other design constraints held constant. [ABSTRACT FROM AUTHOR]

The charge pump phase locked loop (CP-PLL) is widely used subsystem in modern mixed-signal electronic systems that are utilized in digital and wireless applications such as clock generation, synchronization and frequency synthesis. In the classical mode, the combination of a current switched charge pump and a digital phase and frequency detector (CP-PFD) circuits produces an ideal pulse width modulated constant current during one sampling period, which permits a suitable transient performance. Nevertheless, many commercially used CP-PLL chips (e.g., 4046 family) have a voltage switched charge pump (VSCP) because the design of the constant voltage source is easier than the constant current generator and it is a low cost solution. However, the VSCP delivers a non-constant pump current, which varies during one sampling period related to the electrical load of the loop filter (LF). This effect results in a varying gain of the control system affecting significantly its tracking ability. Furthermore, due to its hybrid structure, the simulation of the CP-PLL at high frequencies is challenging and the analysis of its transient characteristics during its non-linear acquisition procedure is not easy. In this paper, a first ever exact and nonlinear model based on the phase equations of the second order voltage switched charge pump phase locked loop (VSCP-PLL) is established by using an event driven (ED) technique. This exact model is then simplified by using a step-wise quasi-constant current approximation during one sampling period to obtain the analytical phase equations. The derived ED-model is validated at transistor level simulations using 130 nm CMOS process. Furthermore, some typical nonlinear features of the VSCP-PLL are explored and the developed ED-models are compared with the quasi-time-continuous (QTC) theory. [ABSTRACT FROM PUBLISHER]

Recently, by replacing the parallel LC “resonator” in Chua's circuit with a lossless transmission line that is terminated by a short circuit, a nonlinear transmission line oscillator is proposed and is named as a “time-delayed Chua's circuit.” In a transmission line oscillator, a linear wave travels along a piece of the transmission line and interacts with terminating electrical components, i.e., a boundary-value problem for a hyperbolic partial differential equation with boundary conditions. Furthermore, a fixed time delay arises due to the propagation time through the transmission line. Delay-induced dynamics are particularly important in technologies where high-speed computers must take into account transmission line effects including propagation delay to prevent undesirable self-oscillations. In this paper, the equation describing Chua's circuit with a lossless transmission line is reduced to a differential-difference equation with one delay (i.e., neutral-type differential equation). By using suitable Lyapunov functionals, we reduce the problem of stability and uniform ultimate boundedness to a scalar ordinary differential inequality. Soon afterwards, we derive conditions for global exponential stability of an equilibrium point. The oscillation of small amplitude in the system are studied by perturbation theory, and a general approach is presented for equations of this type for finding the expansion of the oscillation to any order in terms of the coefficient of the fundamental frequency. The frequency-amplitude relations are derived to second order. Finally, we also give some numerical examples to verify the correctness of our analytic results by computer simulations. [ABSTRACT FROM AUTHOR]

This paper presents a systematic study of pole and zero sensitivity minimization for state-space digital filters in several different yet related settings. First, a new weighted measure for pole and zero sensitivity for state-space digital filters is proposed and the problem of minimizing this measure is investigated. To this end, two efficient iterative techniques for minimizing this measure are developed by employing a quasi-Newton algorithm and relying on a recursive matrix equation, respectively. Furthermore, minimization of the proposed sensitivity measure subject to l2-scaling constraints is examined by extending the two aforementioned solution methods—one converts the constrained optimization problem at hand into an unconstrained problem and solves it using a quasi-Newton algorithm, while the other relaxes the constraints into a single constraint on matrix trace and solves the relaxed problem with an effective matrix iteration scheme. In addition, a simple yet novel method for the minimization of a zero sensitivity measure subject to minimal pole sensitivity is explored by pursuing an optimal coordinate transformation matrix. Simulation studies are presented to demonstrate the validity and effectiveness of the proposed techniques. [ABSTRACT FROM PUBLISHER]

In this paper, we consider nonlinear stochastic systems and intersect ideas from nonlinear control theory and numerical analysis. In particular, we use the idea of relative degree. This concept guarantees smoothness properties of the output and this, in turn, allows one to establish properties that are unique to the control-theoretic perspective. The contributions of the current paper are threefold. Firstly, we define different error measures that extend the ideas of local and global approximation errors for nonlinear stochastic systems. Secondly, we demonstrate that the concept of relative degree plays a key role in obtaining higher order of accuracy for integration procedures compared to Euler-Maruyama integration. We show that a particular state-space model, named STTS model, has an improved order of accuracy when compared to an Euler-Maruyama approximation, at no significant extra computational cost. Thirdly, we show that a further approximation to the STTS model, named MSTTS model, while retaining the order of local errors, has explicit sampling zero dynamics, associated with the noise processes, that have no continuous-time counterpart. The extra zero dynamics are shown to be a function of the Euler-Frobenius polynomials. To the best of the authors' knowledge, this is the first reference to sampling zero dynamics for stochastic nonlinear systems. [ABSTRACT FROM AUTHOR]

In this paper, we explore and describe the electromechanical coupling which results from eKEH conditioning circuits implementing a rectangular QV cycle, including but not limited to the charge pump and Bennet’s doubler circuits. We present numerical and semi-analytical analyses describing the nonlinear relationship between the oscillating mass and the conditioning circuit. We believe this is a poorly understood facet of the device and, as we will portray, affects the potential harvested energy. An approach to determine the frequency shift due to the electromechanical coupling is presented and compared with novel experimental results. We provide some examples of bifurcation behavior and show that the only source of nonlinearity is in the coupling between the electrical and mechanical domains. This work continues from the electrical analysis presented in Part 1, providing a full insight into the complex behavior of the electromechanical coupling. [ABSTRACT FROM PUBLISHER]

In this paper, we discuss the coexistence and dynamical behaviors of multiple equilibrium points for recurrent neural networks with a class of discontinuous nonmonotonic piecewise linear activation functions. It is proved that under some conditions, such n -neuron neural networks can have at least 5^{n} equilibrium points, 3^{n}$ of which are locally stable and the others are unstable, based on the contraction mapping theorem and the theory of strict diagonal dominance matrix. The investigation shows that the neural networks with the discontinuous activation functions introduced in this paper can have both more total equilibrium points and more locally stable equilibrium points than the ones with continuous Mexican-hat-type activation function or discontinuous two-level activation functions. An illustrative example with computer simulations is presented to verify the theoretical analysis. [ABSTRACT FROM PUBLISHER]

This paper presents a projection neural network described by a dynamic system for solving constrained quadratic minimax programming problems. Sufficient conditions based on a linear matrix inequality are provided for global convergence of the proposed neural network. Compared with some of the existing neural networks for quadratic minimax optimization, the proposed neural network in this paper is capable of solving more general constrained quadratic minimax optimization problems, and the designed neural network does not include any parameter. Moreover, the neural network has lower model complexities, the number of state variables of which is equal to that of the dimension of the optimization problems. The simulation results on numerical examples are discussed to demonstrate the effectiveness and characteristics of the proposed neural network. [ABSTRACT FROM PUBLISHER]

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of $n$ independent parts (subnetworks), where $n$ is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network. [ABSTRACT FROM PUBLISHER]

Comparator power consumption is a major bottleneck to the power efficiency of a high resolution successive approximation register (SAR) analog-to-digital converter (ADC) used in low-power applications. This paper analyzes theoretically the optimal comparators that need to be used to achieve a desired overall performance at minimum power levels. A simple and accurate mathematical model of the SAR ADC that is amenable to analysis is first presented. The mathematical formulation suggests that the comparison power allocated to each bit step need not be the same. The optimization problem, therefore, is solved so that the comparator power budget is optimally distributed among all bit steps. Simulation results show that up to 50% and 60% power savings can be achieved when 10 and 12 comparators are employed for 10 b and 12 b SAR ADCs, respectively. To reduce the implementation complexity, comparator noise allocation problem is also solved when fewer than N comparators are employed in an N-bit SAR ADC. Simulation results suggest that two comparators is sufficient to achieve near ideal performance in 10 b and 12 b SAR ADCs. [ABSTRACT FROM AUTHOR]