Time-driven Methods Research Articles

Output feedback control of LTI systems using quantized periodic event-triggering and 1-bit data transmission

This paper presents a novel framework for periodic event-triggered control (PETC) coupled with dynamic quantization for linear systems. Unlike traditional time-driven control methods, our approach leverages event-based mechanisms to judiciously update control actions, thus minimizing computational load and network traffic. We introduce a two-level dynamic quantizer for encoding feedback information with a single bit, thereby enhancing resource efficiency. The proposed PETC mechanism decides the transmission instants based on the quantized output samples. The resulting system is modeled as a hybrid dynamical system to capture both continuous and discrete dynamics. Sufficient conditions for ensuring the stability of the closed-loop system are presented in the form of a linear matrix inequality. Through numerical simulations, we demonstrate that our approach captures the initial output within a finite time and significantly reduces data transmissions compared to traditional methods. This paper makes key contributions in the integration of dynamic quantization with PETC, leading to resource-efficient and stable control systems.

Non-stationary data reorganization for weighted wind turbine icing monitoring with Gaussian mixture model

The ice accretion on blades is a serious problem affecting the working performance of the wind turbine. Due to the non-stationary characteristic of the wind turbine, the fault information of icing can usually be buried in the normal change of wind power generation process, which makes the icing monitoring a challenging task. In this paper, a novel data reorganization for weighted wind turbine icing monitoring method is developed to handle this problem. Specifically, a novel condition driven analysis concept to replace the traditional time-driven methods is proposed for the non-stationary wind power generation process. The data can be divided into multiple wind speed slices (WSSs), i.e., condition slices, by cutting indicator variable into multiple equal intervals, where each condition slice can characterize certain characteristics for the concerned operating condition. Subsequently, by evaluating the similarity of WSSs, different wind speed modes (WSMs) are revealed by a step-wise sequential condition partition algorithm. In this way, the process characteristics can be similar within the same WSM while quite different for different WSMs. Further, the distribution of each WSM is then evaluated by Gaussian mixture model and weighted monitoring method is designed by weighting the monitoring statistics with the posterior probabilities of each monitored sample belonging to different Gaussian components. Two real data sets are used to validate the effectiveness of the proposed method. In comparison with other methods, comprehensive results illustrate that the proposed method can transform the non-stationary process into different WSMs, and closely describe the process variations, based on which the accurate icing monitoring results can be achieved.

Collision-Free Trajectory Design for 2-D Persistent Monitoring Using Second-Order Agents

This article considers a 2-D persistent monitoring problem by controlling movements of second-order agents to minimize some uncertainty metric associated with targets in a dynamic environment. In contrast to common sensing models depending only on the distance from a target, we introduce an active sensing model, which considers the distance between an agent and a target, as well as the agent's velocity. We propose an objective function, which can achieve a collision-free agent trajectory by penalizing all possible collisions. Applying structural properties of the optimal control derived from the Hamiltonian analysis, we limit agent trajectories to a simpler parametric form under a family of 2-D curves depending on the problem setting, e.g., ellipses and Fourier trajectories. Our collision-free trajectories are optimized through an event-driven infinitesimal perturbation analysis and gradient descent method. Although the solution is generally locally optimal, this method is computationally efficient and offers an alternative to other traditional time-driven methods. Finally, simulation examples are provided to demonstrate our proposed results.

Event-Based HVAC Control—A Complexity-Based Approach

The optimal control of the heating, ventilation, and air-conditioning (HVAC) system in buildings has a significant energy saving potential and therefore is of a great practical interest. An event-based HVAC control adjusts control actions when certain events occur, which may be faster and more scalable than state-based or time-driven control methods. However, events may capture either local or global changes in the rooms. The choice of events is a tradeoff between the computational efficiency and the control performance. This challenging problem remains open. We consider this as an important problem in this paper and make three major contributions. First, we define local and global events for the HVAC control problem. The complexity of these event-based control policies is defined. Second, based on hypothesis testing, we develop a method to select events that capture a sufficient state information and with a relatively small event space. Third, we demonstrate the performance of this method on two groups of examples, including one group of small-scale problems for the proof of concept and the other group of large-scale problems in the HVAC control. It is shown that our method outperforms the Levin search, which is a traditional complexity-based search method and finds event-based HVAC control policies with a good performance. Note to Practitioners —When there are multiple rooms in a building, the HVAC control may achieve a significant energy saving and an indoor comfort satisfaction in the same time through exploring the coupling among the rooms. By appropriately defining the events, the size of the event space is usually much smaller than the state space. Therefore, an event-based control is more scalable and preferred in practice. Local events capture the state changes of rooms in a small neighborhood, which leads to a small event space but limited information. Global events capture the state changes of rooms in a large neighborhood, which leads to more information but a large event space. It remains open how to select events in large-scale HVAC control problems, especially when the computing budget is limited. In this paper, we define the complexity of an event-based control policy by the number of neighboring rooms considered. We develop a method based on hypothesis testing to select events with a proper complexity in order to achieve a good system performance. The performance of this method is demonstrated on an HVAC control problem.

Event-driven Optimal Control of Complex HVAC Systems Based on COP·mins

Abstract This paper tries to answer the question: in what condition an optimization should be done? Previous methods use “time” or “PLR” to trigger the control actions. However, they are both indirect performance indicators. This paper proposes a direct performance indicator, COP·mins, based on which a COP·mins-based event-driven optimal control strategy was developed. The energy performance and computational load of different optimization strategies were compared by simulations. Result shows that the energy and computational savings of the proposed method are higher than time-driven and PLR-based event-driven methods. Meanwhile, it can enable the robustness under various load types. COP·mins is also a general parameter and is applicable in other systems where COP is the main concern. It is found that event definitions are critical in event-driven optimal control.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Fast Compartmental Monte Carlo Simulation of Population Balance Models: Application to Nanoparticle Formation in Nonhomogeneous Conditions

A new compartmental Monte Carlo (CMC) algorithm is introduced for the stochastic simulation of population balance models in spatially heterogeneous systems. The heterogeneities are modeled using a network of compartments. The algorithm is based on a new stochastic procedure called particle bundle flow (PBF) to model the stochastic transfer of particles between compartments in a given time interval (a time-driven algorithm). Different from other time-driven methods, the accuracy of the PBF is independent of the particle concentration. The validity of the PBF method is demonstrated by construction and confirmed with numerical experiments. A new strategy for time step control is developed to set bounds on the calculation of the time steps during the simulation. The CMC algorithm, based on the combination of the PBF algorithm with the τ point ensemble Monte Carlo algorithm, is a general-purpose methodology that can be applied to any network of compartments. The computational speed and the low computational load of this algorithm allow the solution of problems that may be intractable otherwise. A new hybrid strategy for the solution of problems with stochastic fluctuations and disparate time scales is also developed in this work. The CMC is applied to study the formation of nanoparticles in a large reactor utilizing a two-compartment model. The Monte Carlo ability to track single events is utilized to study the impact of turbulence and the stability factor on the generation of large particles.

A Hybrid Algorithm of Event-Driven and Time-Driven Methods for Simulations of Granular Flows

AbstractThe classical discrete element approach (DEM) based on Newtonian dynamics can be divided into two major groups, event-driven methods (EDM) and time-driven methods (TDM). Generally speaking, TDM simulations are suited for cases with high volume fractions where there are collisions between multiple objects. EDM simulations are suited for cases with low volume fractions from the viewpoint of CPU time. A method combining EDM and TDM called Hybrid Algorithm of event-driven and time-driven methods (HAET) is presented in this paper. The HAET method employs TDM for the areas with high volume fractions and EDM for the remaining areas with low volume fractions. It can decrease the CPU time for simulating granular flows with strongly non-uniform volume fractions. In addition, a modified EDM algorithm using a constant time as the lower time step limit is presented. Finally, an example is presented to demonstrate the hybrid algorithm.

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision.

Analysis of four Monte Carlo methods for the solution of population balances in dispersed systems

Monte Carlo (MC) constitutes an important class of methods for the numerical solution of the general dynamic equation (GDE) in particulate systems. We compare four such methods in a series of seven test cases that cover typical particulate mechanisms. The four MC methods studied are: time-driven direct simulation Monte Carlo (DSMC), stepwise constant-volume Monte Carlo, constant number Monte Carlo, and multi-Monte Carlo (MMC) method. These MC's are introduced briefly and applied numerically to simulate pure coagulation, breakage, condensation/evaporation (surface growth/dissolution), nucleation, and settling (deposition). We find that when run with comparable number of particles, all methods compute the size distribution within comparable levels of error. Because each method uses different approaches for advancing time, a wider margin of error is observed in the time evolution of the number and mass concentration, with event-driven methods generally providing better accuracy than time-driven methods. The computational cost depends on algorithmic details but generally, event-driven methods perform faster than time-driven methods. Overall, very good accuracy can be achieved using reasonably small numbers of simulation particles, O(10 3), requiring computational times of the order 10 2−10 3 s on a typical desktop computer.

Event-driven molecular dynamics in parallel

Although event-driven algorithms have been shown to be far more efficient than time-driven methods such as conventional molecular dynamics, they have not become as popular. The main obstacle seems to be the difficulty of parallelizing event-driven molecular dynamics. Several basic ideas have been discussed in recent years, but to our knowledge no complete implementation has been published yet. In this paper we present a parallel event-driven algorithm including dynamic load-balancing, which can be easily implemented on any computer architecture. To simplify matters our explanations refer to a basic multi-particle system of hard spheres, but can be extended easily to a wide variety of possible models.

Complexity Made Simple - At a Small Price

Fundamental complexity limits prevent us from directly solving many design, control, and optimization problems. Yet, there are ways to solve such hard problems by exploiting their specific structure, by asking the “right” questions, and by challenging some conventional approaches. First, slow, inefficient, and intrusive trial-and-error techniques can sometimes be bypassed through simple thought experiments constructed at a “small price.” In particular, for Discrete Event Systems this can be systematically accomplished through the theory of Perturbation Analysis. Second, decomposition and abstraction methods can often provide accurate solutions or significantly simplify a hard problem at the “small price” of some loss of accuracy. Finally, conventional time-driven methods for sampling, control, and communication can be replaced by event-driven techniques with the proper events triggering these actions.

Numerical Methods for Modeling Water Quality in Distribution Systems: A Comparison

A comparison is made between the formulation and computational performance of four numerical methods for modeling the transient behavior of water quality in drinking-water-distribution systems. Two are Eulerian-based (the finite-difference and discrete-volume methods) and two are Lagrangian-based (the time-driven and event-driven methods). The Eulerian approaches move water between fixed grid points or volume segments in pipes as time is advanced in uniform increments. The Lagrangian methods update conditions in variable-sized segments of water at either uniform time increments or only at times when a new segment reaches a downstream pipe junction. Each method is encoded into an existing distribution-system simulation model and run on several pipe networks of varying size under equal accuracy tolerances. Results show that the accuracies of the methods are comparable. The Lagrangian methods are more efficient for simulating chemical transport. For modeling water age, the time-driven Lagrangian method is the most time-efficient while the Eulerian methods are more memory-efficient.

Application of sampling methodologies to network traffic characterization

The relative performance of different data collection methods in the assessment of various traffic parameters is significant when the amount of data generated by a complete trace of a traffic interval is computationally overwhelming, and even capturing summary statistics for all traffic is impractical. This paper presents a study of the performance of various methods of sampling in answering questions related to wide area network traffic characterization. Using a packet trace from a network environment that aggregates traffic from a large number of sources, we simulate various sampling approaches, including time-driven and event-driven methods, with both random and deterministic selection patterns, at a variety of granularities. Using several metrics which indicate the similarity between two distributions, we then compare the sampled traces to the parent population. Our results revealed that the time-triggered techniques did not perform as well as the packet-triggered ones. Furthermore, the performance differences within each class (packet-based or time-based techniques) are small.