Showing papers on "Adaptive algorithm published in 2019"

An adaptive variational algorithm for exact molecular simulations on a quantum computer.

Abstract: Quantum simulation of chemical systems is one of the most promising near-term applications of quantum computers. The variational quantum eigensolver, a leading algorithm for molecular simulations on quantum hardware, has a serious limitation in that it typically relies on a pre-selected wavefunction ansatz that results in approximate wavefunctions and energies. Here we present an arbitrarily accurate variational algorithm that, instead of fixing an ansatz upfront, grows it systematically one operator at a time in a way dictated by the molecule being simulated. This generates an ansatz with a small number of parameters, leading to shallow-depth circuits. We present numerical simulations, including for a prototypical strongly correlated molecule, which show that our algorithm performs much better than a unitary coupled cluster approach, in terms of both circuit depth and chemical accuracy. Our results highlight the potential of our adaptive algorithm for exact simulations with present-day and near-term quantum hardware.

Golden ratio algorithms for variational inequalities

Abstract: The paper presents a fully adaptive algorithm for monotone variational inequalities. In each iteration the method uses two previous iterates for an approximation of the local Lipschitz constant without running a linesearch. Thus, every iteration of the method requires only one evaluation of a monotone operator F and a proximal mapping g. The operator F need not be Lipschitz continuous, which also makes the algorithm interesting in the area of composite minimization. The method exhibits an ergodic O(1 / k) convergence rate and R-linear rate under an error bound condition. We discuss possible applications of the method to fixed point problems as well as its different generalizations.

Joint Computing Resource, Power, and Channel Allocations for D2D-Assisted and NOMA-Based Mobile Edge Computing

Abstract: Mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) have been considered as the promising techniques to address the explosively growing computation-intensive applications and accomplish the requirement of massive connectivity in the fifth-generation networks. Moreover, since the computing resources of the edge server are limited, the computing load of the edge server needs to be effectively alleviated. In this paper, by exploiting device-to-device (D2D) communication for enabling user collaboration and reducing the edge server's load, we investigate the D2D-assisted and NOMA-based MEC system. In order to minimize the weighted sum of the energy consumption and delay of all users, we jointly optimize the computing resource, power, and channel allocations. Regarding the computing resource allocation, we propose an adaptive algorithm to find the optimal solution. Regarding the power allocation, we present a novel power allocation algorithm based on the particle swarm optimization (PSO) for the single NOMA group comprised of multiple cellular users. Then, for the matching group comprised of a NOMA group and D2D pairs, we theoretically derive the interval of optimal power allocation and propose a PSO-based algorithm to solve it. Regarding the channel allocation, we propose a one-to-one matching algorithm based on the Pareto improvement and swapping operations and extend the one-to-one matching algorithm to a many-to-one matching scenario. Finally, we propose a scheduling-based joint computing resource, power, and channel allocations algorithm to achieve the joint optimization. The simulation results show that the proposed solution can effectively reduce the weighted sum of the energy consumption and delay of all users.

Distributed Average Tracking for Lipschitz-Type of Nonlinear Dynamical Systems

Abstract: In this paper, a distributed average tracking (DAT) problem is studied for Lipschitz-type of nonlinear dynamical systems. The objective is to design DAT algorithms for locally interactive agents to track the average of multiple reference signals. Here, in both dynamics of agents and reference signals, there is a nonlinear term satisfying a Lipschitz-type condition. Three types of DAT algorithms are designed. First, based on state-dependent-gain design principles, a robust DAT algorithm is developed for solving DAT problems without requiring the same initial condition. Second, by using a gain adaption scheme, an adaptive DAT algorithm is designed to remove the requirement that global information, such as the eigenvalue of the Laplacian and the Lipschitz constant, is known to all agents. Third, to reduce chattering and make the algorithms easier to implement, a couple of continuous DAT algorithms based on time-varying or time-invariant boundary layers are designed, respectively, as a continuous approximation of the aforementioned discontinuous DAT algorithms. Finally, some simulation examples are presented to verify the proposed DAT algorithms.

Adaptive Robust Tracking Control for Multiple Unknown Fractional-Order Nonlinear Systems

Abstract: By applying the fractional Lyapunov direct method, we investigate the robust consensus tracking problem for a class of uncertain fractional-order multiagent systems with a leader whose input is unknown and bounded. More specifically, multiple fractional-order systems with heterogeneous unknown nonlinearities and external disturbances are considered in this paper, which include the second-order multiagent systems as its special cases. First, a discontinuous neural network-based (NN-based) distributed robust adaptive algorithm is designed to guarantee the consensus tracking error exponentially converges to zero under a fixed topology. Also the derived results are further extended to the case of switching topology by appropriately choosing multiple Lyapunov functions. Second, a continuous NN-based distributed robust adaptive algorithm is further proposed to eliminate the undesirable chattering phenomenon of the discontinuous controller, where the consensus tacking error is uniformly ultimately bounded and can be reduced as small as desired. It is worth noting that all the proposed NN-based robust adaptive algorithms are independent of any global information and thus are fully distributed. Finally, numerical simulations are provided to validate the correctness of the proposed algorithms.

Energy Management of Hybrid Electric Vehicle Using Vehicle Lateral Dynamic in Velocity Prediction

Abstract: Accurately predicting the changes in the speed has a significant impact on the quality of the energy management in hybrid vehicles. Many methods for predicting the speed have been proposed in the literature, but few fully consider vehicle dynamics to predict speed changes. To this end, a new method is introduced to predict the vehicle speed and to perform energy management for hybrid vehicles in situations where lateral dynamics plays a significant role. Based on the tire–road friction coefficient and the GPS signal, the maximum cornering speed of the vehicle, in which each tire force does not saturate, is evaluated. Then, the principle of using less friction braking and using more regenerative braking, the vehicle speed prediction controller is designed. In the end, an optimal control method with a new equivalent factor (EF) adaptive algorithm is designed to distribute the torque of the engine and the motor, as well as the shift schedule of the gearbox. A driver-in-the-loop experiment is used to prove that the vehicle installed with the proposed speed prediction controller has an average 29.1% increase in energy efficiency compared to vehicle that do not have speed prediction controller. And, the EF adaptive algorithm keeps the battery SoC at a reasonable interval.

Event-Triggered Algorithms for Leader–Follower Consensus of Networked Euler–Lagrange Agents

Abstract: This paper proposes three different distributed event-triggered control algorithms to achieve leader–follower consensus for a network of Euler–Lagrange agents. We first propose two model-independent algorithms for a subclass of Euler–Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work studying event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities, which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information, and bounds on the initial conditions. When the Euler–Lagrange agents have dynamics that include the vector of gravitational potential forces, an adaptive algorithm is proposed. This requires more information about the agent dynamics but allows for the estimation of uncertain parameters associated with the agent self-dynamics. For each algorithm, a trigger function is proposed to govern the event update times. The controller is only updated at each event, which ensures that the control input is piecewise constant and thus saves energy resources. We analyze each controller and trigger function to exclude Zeno behavior.

Adaptive Fuzzy Super-Twisting Sliding Mode Control for Microgyroscope

Abstract: This paper proposes a novel adaptive fuzzy super-twisting sliding mode control scheme for microgyroscopes with unknown model uncertainties and external disturbances. Firstly, an adaptive algorithm is used to estimate the unknown parameters and angular velocity of microgyroscopes. Secondly, in order to improve the performance of the system and the superiority of the super-twisting algorithm, this paper utilizes the universal approximation characteristic of the fuzzy system to approach the gain of the super-twisting sliding mode controller and identify the gain of the controller online, realizing the adaptive adjustment of the controller parameters. Simulation results verify the superiority and the effectiveness of the proposed approach, compared with adaptive super-twisting sliding mode control without fuzzy approximation; the proposed method is more effective.

Underwater Dynamic Visual Servoing for a Soft Robot Arm With Online Distortion Correction

Abstract: Bioinspired soft robots have generated increasing attentions due to their optimal performance in specific environments stemming from their unique morphology and sensorimotor capabilities. This also raises new challenges in modeling and control due to their unconventional soft mechanism. In this paper, we aim to improve the controllability of an octopus tentacle-like soft robot arm operating in an underwater environment and to reproduce the perception-guided tracking performance of its biological counterpart when preying on and pursuing a target. To this end, we design an adaptive visual servoing controller for trajectory tracking tasks, taking into account system dynamics with environment interactions. Unlike on-the-ground visual servoing controller, the underwater environment exerts multirefraction effects on the image projection process, increasing difficulties in obtaining an accurate camera projection model. This paper thus proposes an adaptive method that can online correct image distortion caused by the deflection of light rays passing through different mediums to compensate for refraction effects. The adaptive algorithm can simultaneously estimate unknown camera parameters to completely avoid complex offline underwater camera calibration. The proposed adaptive controller validates the trajectory tracking performance experimentally and position and image velocity.

qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor

Abstract: Quantum simulation, one of the most promising applications of a quantum computer, is currently being explored intensely using the variational quantum eigensolver. The feasibility and performance of this algorithm depend critically on the form of the wavefunction ansatz. Recently in Nat. Commun. 10, 3007 (2019), an algorithm termed ADAPT-VQE was introduced to build system-adapted ans\"atze with substantially fewer variational parameters compared to other approaches. This algorithm relies heavily on a predefined operator pool with which it builds the ansatz. However, Nat. Commun. 10, 3007 (2019) did not provide a prescription for how to select the pool, how many operators it must contain, or whether the resulting ansatz will succeed in converging to the ground state. In addition, the pool used in that work leads to state preparation circuits that are too deep for a practical application on near-term devices. Here, we address all these key outstanding issues of the algorithm. We present a hardware-efficient variant of ADAPT-VQE that drastically reduces circuit depths using an operator pool that is guaranteed to contain the operators necessary to construct exact ans\"atze. Moreover, we show that the minimal pool size that achieves this scales linearly with the number of qubits. Through numerical simulations on $\text{H}_4$, LiH and $\text{H}_6$, we show that our algorithm ("qubit-ADAPT") reduces the circuit depth by an order of magnitude while maintaining the same accuracy as the original ADAPT-VQE. A central result of our approach is that the additional measurement overhead of qubit-ADAPT compared to fixed-ansatz variational algorithms scales only linearly with the number of qubits. Our work provides a crucial step forward in running algorithms on near-term quantum devices.

Optimal phase space sampling for Monte Carlo simulations of Heisenberg spin systems

Abstract: We present an adaptive algorithm for the optimal phase space sampling in Monte Carlo simulations of 3D Heisenberg spin systems. Based on a golden rule of the Metropolis algorithm which states that an acceptance rate of [Formula: see text] is ideal to efficiently sample the phase space, the algorithm adaptively modifies a cone-based spin update method keeping the acceptance rate close to [Formula: see text]. We have assessed the efficiency of the adaptive algorithm through four different tests and contrasted its performance with that of other common spin update methods. In systems at low and high temperatures and anisotropies, the adaptive algorithm proved to be the most efficient for magnetization reversal and for the convergence to equilibrium of the thermal averages and the coercivity in hysteresis calculations. Thus, the adaptive algorithm can be used to significantly reduce the computational cost in Monte Carlo simulations of 3D Heisenberg spin systems.

Adaptive Trajectory Tracking Control of a Parallel Ankle Rehabilitation Robot With Joint-Space Force Distribution

Abstract: This paper proposes an adaptive trajectory tracking control strategy implemented on a parallel ankle rehabilitation robot with joint-space force distribution. This device is redundantly actuated by four pneumatic muscles (PMs) with three rotational degrees of freedom. Accurate trajectory tracking is achieved through a cascade controller with the position feedback in task space and force feedback in joint space, which enhances training safety by controlling each PM to be in tension in an appropriate level. At a high level, an adaptive algorithm is proposed to enable movement intention-directed trajectory adaptation. This can further help to improve training safety and encourage human–robot engagement. The pilot tests were conducted with an injured human ankle. The statistical data show that normalized root mean square deviation (NRMSD) values of trajectory tracking are all less than 2.3% and the PM force tracking being always controlled in tension, demonstrating its potential in assisting ankle therapy.

Adaptive Unscented Kalman Filter Based Estimation and Filtering for Dynamic Positioning with Model Uncertainties

Abstract: A novel adaptive unscented Kalman filter (AUKF) is presented and applied to ship dynamic positioning (DP) system with model uncertainties of time-varying noise statistics, model mismatch and slow varying drift forces. The adaptive algorithm is proposed to simultaneously online adapt the process and measurement noise covariance by adopting the main principle of covariance matching. The measurement noise covariance is adapted based on residual covariance matching method, and then the process noise covariance is adjusted by using adaptive scaling factor. Simulation comparisons among the proposed RQAUKF, the strong tracking UKF (RSTAUKF) and the standard UKF show that the proposed RQAUKF can effectively improve the estimation accuracy and stability, and can assist the controller to obtain better control performance.

Adaptive Robust Triple-Step Control for Compensating Cogging Torque and Model Uncertainty in a DC Motor

Abstract: Eliminating the influence of cogging torque and model uncertainty on the tracking control of a dc motor when its speed varies nonperiodically is a challenge. In this paper, an adaptive robust triple-step control method is proposed for compensating cogging torque and model uncertainty. First, a new presentation of the cogging torque and a simplified model of the friction torque are presented to facilitate the online estimation of the unknown model parameters. The load torque, motor disturbance, and model errors are considered as model uncertainty. Based on these considerations, a control-oriented model that contains unknown parameters and model uncertainty is obtained. Second, benefitting from the new presentation, an adaptive algorithm is employed to identify the unknown parameters online. The model uncertainty is estimated by an extended state observer. Third, the model-based triple-step nonlinear method is extended to a system with both parameter uncertainty and model uncertainty, and an adaptive robust triple-step nonlinear controller is derived. The robust stability of the closed-loop system is proven in the framework of Lyapunov theory. Finally, the effectiveness and the satisfactory control performance of this controller are evaluated through comparative experiments on a J60LYS05 motor.

Adaptive RBF-AR Models Based on Multi-Innovation Least Squares Method

Abstract: In the previous work, the parameters of radial basis function network based autoregressive (RBF-AR) models are estimated offline and no longer updated afterward. In this letter, an adaptive learning algorithm is proposed for the RBF-AR models. The proposed strategy is that the nonlinear parameters are previously determined by an off-line variable projection method; and once new samples are available, the linear parameters are updated. The linear adaptive algorithm adopted in this letter is the multi-innovation least squares method, due to its high performance. The simulation results show that with the adaption of the linear parameters, the prediction performance of the RBF-AR models may be significantly improved, which demonstrates the effectiveness of the proposed algorithm.

Adaptive moment estimation for polynomial nonlinear equalizer in PAM8-based optical interconnects.

Abstract: Adaptive moment estimation (Adam) is a popular optimization method to estimate large-scale parameters in neural networks. This paper proposes the first use of Adam algorithm to fast and stably converge large-scale tap coefficients of polynomial nonlinear equalizer (PNLE) for 129-Gbit/s PAM8-based optical interconnects. PNLE is one of simplified Volterra nonlinear equalizer for making a trade-off between complexity and performance. Different from serial least-mean square (LMS) adaptive algorithm, Adam algorithm is a parallel processing algorithm, which can obtain globally optimal tap coefficients without being trapped in locally optimal tap coefficients. Timing error is one of the main obstacles to the PAM systems with high baud rate and high modulation order. Owing to parallel processing and global optimization, Adam algorithm has much better performance on resisting the timing error, which can achieve faster, more-stable and lower-MSE convergence compared to LMS adaptive algorithm. In conclusion, Adam algorithm shows great potential for converging the tap coefficients of PNLE in PAM8-based optical interconnects.

Fast adaptive switching technique of impulsive noise removal in color images

Abstract: In the paper, a family of switching filters designed for the impulsive noise removal in color images is analyzed. The framework of the proposed denoising techniques is based on the concept of cumulated distances between the processed pixel and its neighbors. To increase the filtering efficiency, a robust scheme, in which the sum of distances to only the most similar pixels of the neighborhood serves as a measure of impulsiveness, was elaborated. As this trimmed measure is dependent on the image local structure, an adaptive mechanism was also incorporated. Additionally, a very fast design, which enables image denoising in practical applications, is proposed and the choice of the filter output, which is used to replace the noisy pixels, is discussed. The described family of filters was evaluated on a large set of natural test images and compared with the state-of-the-art restoration methods. The analysis of the achieved results shows that the novel filters outperform the existing techniques in terms of both denoising accuracy and computational complexity. In this way, the proposed techniques can be recommended for the application in various image and video enhancement tasks.

Adaptive Multi-index Collocation for Uncertainty Quantification and Sensitivity Analysis

Abstract: In this paper, we present an adaptive algorithm to construct response surface approximations of high-fidelity models using a hierarchy of lower fidelity models. Our algorithm is based on multi-index stochastic collocation and automatically balances physical discretization error and response surface error to construct an approximation of model outputs. This surrogate can be used for uncertainty quantification (UQ) and sensitivity analysis (SA) at a fraction of the cost of a purely high-fidelity approach. We demonstrate the effectiveness of our algorithm on a canonical test problem from the UQ literature and a complex multi-physics model that simulates the performance of an integrated nozzle for an unmanned aerospace vehicle. We find that, when the input-output response is sufficiently smooth, our algorithm produces approximations that can be over two orders of magnitude more accurate than single fidelity approximations for a fixed computational budget.

Multiscale modeling and decoding algorithms for spike-field activity

Abstract: Objective Behavior is encoded across multiple spatiotemporal scales of brain activity. Modern technology can simultaneously record various scales, from spiking of individual neurons to large neural populations measured with field activity. This capability necessitates developing multiscale modeling and decoding algorithms for spike-field activity, which is challenging because of the fundamental differences in statistical characteristics and time-scales of these signals. Spikes are binary-valued with a millisecond time-scale while fields are continuous-valued with slower time-scales. Approach We develop a multiscale encoding model, adaptive learning algorithm, and decoder that explicitly incorporate the different statistical profiles and time-scales of spikes and fields. The multiscale model consists of combined point process and Gaussian process likelihood functions. The multiscale filter (MSF) for decoding runs at the millisecond time-scale of spikes while adding information from fields at their slower time-scales. The adaptive algorithm learns all spike-field multiscale model parameters simultaneously, in real time, and at their different time-scales. Main results We validated the multiscale framework within motor tasks using both closed-loop brain-machine interface (BMI) simulations and non-human primate (NHP) spike and local field potential (LFP) motor cortical activity during a naturalistic 3D reach task. Our closed-loop simulations show that the MSF can add information across scales and that the adaptive MSF can accurately learn all parameters in real time. We also decoded the seven joint angular trajectories of the NHP arm using spike-LFP activity. These data showed that the MSF outperformed single-scale decoding, this improvement was due to the addition of information across scales rather than the dominance of one scale and was largest in the low-information regime, and the improvement was similar regardless of the degree of overlap between spike and LFP channels. Significance This multiscale framework provides a tool to study encoding across scales and may help enhance future neurotechnologies such as motor BMIs.

UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization

Abstract: We propose a novel adaptive, accelerated algorithm for the stochastic constrained convex optimization setting.Our method, which is inspired by the Mirror-Prox method, \emph{simultaneously} achieves the optimal rates for smooth/non-smooth problems with either deterministic/stochastic first-order oracles. This is done without any prior knowledge of the smoothness nor the noise properties of the problem. To the best of our knowledge, this is the first adaptive, unified algorithm that achieves the optimal rates in the constrained setting. We demonstrate the practical performance of our framework through extensive numerical experiments.