Uncertain Part Research Articles

Inverse modeling of energy transports and budgets of the atmosphere

The vertically integrated horizontal energy transports and the vertically integrated vertical energy flux divergence from ERA-40 and ISCCP are not in balance assuming a stationary climate as a time mean over several years. The reasons are the inherent uncertainties in each of the respective data sets. We therefore modify them using a variational approach with a discretization in spherical harmonics to obtain consistent values. The variational approach only modifies the smaller yet more uncertain divergent part of the flow, leaving the large rotational part untouched. From these consistent fields we can calculate posterior covariance matrices of the vertically integrated horizontal energy transport and the vertically integrated vertical energy flux divergence, providing a measure of the uncertainty of the previous calculation. We are able to use these posterior covariance matrices to give an estimate of the uncertainty of the zonally and vertically integrated meridional energy transport, which is about 0.25 PW in the tropics and 0.04 PW in high latitudes, as well as for the vertical energy flux divergence of the atmosphere, which ranges from 2.5 to 5 W/m2 in the tropics to 15–17 W/m2 in high latitudes.

Adaptive Backstepping Control for a Class of Uncertain Nonaffine Nonlinear Time-Varying Delay Systems with Unknown Dead-Zone Nonlinearity

An adaptive backstepping controller is constructed for a class of nonaffine nonlinear time-varying delay systems in strict feedback form with unknown dead zone and unknown control directions. To simplify controller design, nonaffine system is first transformed into an affine system by using mean value theorem and the unknown nonsymmetric dead-zone nonlinearity is treated as a combination of a linear term and a bounded disturbance-like term. Owing to the universal approximation property, fuzzy logic systems (FLSs) are employed to approximate the uncertain nonlinear part in controller design process. By introducing Nussbaum-type function, the a priori knowledge of the control gains signs is not required. By constructing appropriate Lyapunov-Krasovskii functionals, the effect of time-varying delay is compensated. Theoretically, it is proved that this scheme can guarantee that all signals in closed-loop system are semiglobally uniformly ultimately bounded (SUUB) and the tracking error converges to a small neighbourhood of the origin. Finally, the simulation results validate the effectiveness of the proposed scheme.

Travel Time Distribution under Interrupted Flow and Application to Travel Time Reliability

The travel time distribution under interrupted flow based on radio frequency identification–detected data is analyzed. The urban road network studied is in downtown Nanjing, Jiangsu Province, in China, where video cameras and radio frequency equipment are installed at some arterial links to acquire traffic flow data including vehicle type, passing time, and spot speed. On the basis of the radio frequency identification data, the travel time distribution under interrupted flow shows a bimodal curve instead of a unimodal curve. This finding suggests that travel times under interrupted flow are not independent and identically distributed and can be divided into two components, the uninterrupted component and the interrupted component, according to whether vehicles experience intersection control delay. Six convex distribution models are presented, and an expectation–maximization algorithm is adopted to obtain the best distribution for each link unit. The results indicate that travel time under interrupted flow generally follows a bimodal distribution. The parameters of the models can distinguish travel times between those encountering delay and those without delay. This finding demonstrates that intersection control delay plays a predominant role in travel times under interrupted flow. Two main travel time reliability indexes under interrupted flow obtained by the parameters of the bimodal distribution, expected travel time and RATIO, are presented as applications. Expected travel times indicate the average levels of travel times of two independent components, and RATIO represents the ratio of the uncertain part of the travel times. The analysis indicates that these two indexes can capture the characteristics of interrupted-flow travel time reliability, which is mainly caused by link length, number of intersections, and mixed traffic flow.

Uncertainty Modeling and Robust Control for LCL Resonant Inductive Power Transfer System

The LCL resonant inductive power transfer (IPT) system is increasingly used because of its harmonic filtering capabilities, high efficiency at light load, and unity power factor feature. However, the modeling and controller design of this system become extremely difficult because of parameter uncertainty, high-order property, and switching nonlinear property. This paper proposes a frequency and load uncertainty modeling method for the LCL resonant IPT system. By using the linear fractional transformation method, we detach the uncertain part from the system model. A robust control structure with weighting functions is introduced, and a control method using structured singular values is used to enhance the system performance of perturbation rejection and reference tracking. Analysis of the controller performance is provided. The simulation and experimental results verify the robust control method and analysis results. The control method not only guarantees system stability but also improves performance under perturbation.

Synchronization of Coupled Chaotic Neurons with Unknown Time Delays via Adaptive Backstepping Control

In this study, an adaptive Neural Network (NN) based backstepping controller is proposed to realize chaos synchronization of two gap junction coupled FitzHugh-Nagumo (FHN) neurons with uncertain time delays. In the designed backstepping controller, a simple Radial Basis Function (RBF) NN is used to approximate the uncertain nonlinear part of the error dynamical system. The weights of the NN are tuned on-line. A Lyapunov-Krasovskii function is designed to overcome the difficulties from the unknown time delays. Moreover, to relax the requirement for boundness of disturbance, an adaptive law to adapt the disturbance in real time is given. According to the Lyapunov stability theory, the stability of the closed error system is guaranteed. The control scheme is robust to the uncertainties such as approximate error, ionic channel noise and external disturbances. Chaos synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.

Adaptive predictive control of periodic non‐linear auto‐regressive moving average systems using nearest‐neighbour compensation

Many practical non-linear systems can be described by non-linear auto-regressive moving average (NARMA) system models, whose stabilisation problem is challenging in the presence of large parametric uncertainties and non-parametric uncertainties. In this work, to address this challenging problem for a wide class of discrete-time NARMA systems, in which there are uncertain periodic parameters as well as uncertain non-linear part with unknown periodic time delays, we develop adaptive predictive control laws using the key ideas of `future outputs prediction' and `nearest-neighbour compensation', among which the former is carried out to overcome the non-causalness problem and the latter novel idea is proposed to completely compensate for the effect of non-linear uncertainties as well as unknown time delays. To achieve the desired asymptotic tracking performance in the presence of semi-parametric uncertainties with time delays, an ` n -step parameter update law' is first designed, based on which an `one-step update law' is then elaborately constructed to obtain smoother closed-loop signals. This study in general develops a systematic adaptive control framework for periodic NARMA systems with guaranteed boundedness stability and asymptotic tracking performance, which are established by rigorous theoretic proof and verified by simulation studies.

Composite control method for stabilizing spacecraft attitude in terms of Rodrigues parameters

In this paper, the attitude stabilization problem of a rigid spacecraft described by Rodrigues parameters is investigated via a composite control strategy, which combines a feedback control law designed by a finite time control technique with a feedforward compensator based on a linear disturbance observer (DOB) method. By choosing a suitable coordinate transformation, the spacecraft dynamics can be divided into three second-order subsystems. Each subsystem includes a certain part and an uncertain part. By using the finite time control technique, a continuous finite time controller is designed for the certain part. The uncertain part is considered to be a lumped disturbance, which is estimated by a DOB, and a corresponding feedforward design is then implemented to compensate the disturbance. Simulation results are employed to confirm the effectiveness of the proposed approach.

Adaptive RBFNN Formation Control of Multi-mobile Robots with Actuator Dynamics

We study the problem of formation control and trajectory tracking for multiple nonholonomic mobile robots with actuator and formation dynamics. An adaptive neural-network (NN) control strategy that integrated kinematic controller with input voltages controller of actuator was proposed. A control law was designed by backstepping technique based on separation-bearing formation control structure of leader-follower. The radial basis function neural network (RBFNN) was adopted to achieve on-line estimation for the dynamics nonlinear uncertain part for follower and leader robots. The adaptive robust controller was adopted to compensate modeling errors of NN. This strategy not only overcomed all kinds of uncertainties of mobile robots, but also ensured the desired trajectory tracking of robot formation in the case of maintaining formation. The stability and convergence of the control system were proved by using the Lyapunov theory. The simulation results showed the effectiveness of this proposed method. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2334

Dynamic velocity feed-forward compensation control with RBF-NN system identification for industrial robots

A dynamic velocity feed-forward compensation control (DVFCC) approach with RBF neural network (RBF-NN) dynamic model identification was presented for the adaptive trajectory tracking of industrial robots. The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model. The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accuracy. The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can convert torque signal into velocity in the standard industrial controller. Then, the need for a torque control interface was avoided in the real-time dynamic control of industrial robot. The simulations and experiments were carried out on a gas cutting manipulator. The results show that the proposed control approach can reduce steady-state error, suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space, especially under highspeed condition.

Settlement Prediction of Roadbed Based on Mixture Model with Exponential Curve and ANN

A mixture method based on exponential curve and ANN is presented according to settlement prediction of roadbed with measured data. Based on this method, the rule of roadbed settlement is classified into sure part and uncertain part. Exponential curve is used to model the sure part, and ANN to model the uncertain part, thus the mixture settlement model can be obtained. Prediction results show that the mixture model has advantages of high precision and small network scale; it provides a new method for settlement prediction of roadbed.

Adaptive Neural-Network Control of Mobile Robot Formations Including Actuator Dynamics

For the formation control problem of multiple nonholonomic mobile robots with actuator and formation dynamics, this paper propsed a new control strategy that integrated kinematic controller with input voltages controller of actuator. This control law was designed by backstepping technique based on formation control structure of leader-follower. The RBFNN was adopted to achieve on-line estimation for the dynamics nonlinear uncertain part for follower and leader robots. The adaptive robust controller was adopted to compensate modeling errors of neural network. This strategy not only solved the problem of parameters and non-parameter uncertainties of mobile robots, but also ensured the desired trajectory tracking of robot formation in the case of maintaining formation. The stability and convergence of the control system were proved by using the Lyapunov theory. The simulation results showed the effectiveness of this proposed method.

Enhanced Disturbance-Observer-Based Control for a Class of Time-Delay System with Uncertain Sinusoidal Disturbances

This paper is concerned with disturbance-observer-based control (DOBC) for a class of time-delay systems with uncertain sinusoidal disturbances. The disturbances are decomposed as precise and uncertain parts using nonlinear disturbance observer (DO) after appropriate coordinate transformation. And then the two parts can be compensated by corresponding controller, respectively, such that the classic DOBC method is extended to uncertain disturbance rejection. One novel feature of the proposed method is that even if the precise disturbance parameters are inaccessible, the merits of DOBC can be inherited. By integrating the disturbance observers with feedback control laws with time delay, the disturbances can be rejected and the desired dynamic performances can be guaranteed. Finally, simulations for a flight control system are given to demonstrate the effectiveness of the results.

A New Hyperchaotic System and the Synchronization Using Active Variable Universe Adaptive Fuzzy Controller

This paper presents a new hyperchaotic system by introducing an additional state variable into Lorenz system. The system’s characteristics, including the dissipativity, equilibrium, and Lyapunov exponents, are studied. A controller is developed which consists of an active control term and a variable universe adaptive fuzzy system. By using this controller, the synchronization of the new hyperchaotic systems with uncertain linear part is accomplished according to Lyapunov’s direct method. Simulation results illustrate the effectiveness of the proposed method.

Exponential stability and robust H∞ control of a class of discrete-time switched non-linear systems with time-varying delays via T-S fuzzy model

This paper deals with stability and robust H∞ control of discrete-time switched non-linear systems with time-varying delays. The T-S fuzzy models are utilised to represent each sub-non-linear system. Thus, with two level functions, namely, crisp switching functions and local fuzzy weighting functions, we introduce a discrete-time switched fuzzy systems, which inherently contain the features of the switched hybrid systems and T-S fuzzy systems. Piecewise fuzzy weighting-dependent Lyapunov–Krasovskii functionals (PFLKFs) and average dwell-time approach are utilised in this paper for the exponentially stability analysis and controller design, and with free fuzzy weighting matrix scheme, switching control laws are obtained such that H∞ performance is satisfied. The conditions of stability and the control laws are given in the form of linear matrix inequalities (LMIs) that are numerically feasible. The state decay estimate is explicitly given. A numerical example and the control of delayed single link robot arm with uncertain part are given to demonstrate the efficiency of the proposed method.

Minimizing Manual Image Segmentation Turn-Around Time for Neuronal Reconstruction by Embracing Uncertainty

The ability to automatically segment an image into distinct regions is a critical aspect in many visual processing applications. Because inaccuracies often exist in automatic segmentation, manual segmentation is necessary in some application domains to correct mistakes, such as required in the reconstruction of neuronal processes from microscopic images. The goal of the automated segmentation tool is traditionally to produce the highest-quality segmentation, where quality is measured by the similarity to actual ground truth, so as to minimize the volume of manual correction necessary. Manual correction is generally orders-of-magnitude more time consuming than automated segmentation, often making handling large images intractable. Therefore, we propose a more relevant goal: minimizing the turn-around time of automated/manual segmentation while attaining a level of similarity with ground truth. It is not always necessary to inspect every aspect of an image to generate a useful segmentation. As such, we propose a strategy to guide manual segmentation to the most uncertain parts of segmentation. Our contributions include 1) a probabilistic measure that evaluates segmentation without ground truth and 2) a methodology that leverages these probabilistic measures to significantly reduce manual correction while maintaining segmentation quality.

Nonlinear vibration semi-active control of automotive steering using magneto-rheological damper

Traffic accidents are often caused by vibration of automotive steering because the vibration can make a vehicle run like a snake. A novel semi-active vibration control strategy of automotive steering with magneto-rheological (MR) damper is proposed in this paper. An adaptive RBF neural sliding mode controller is designed for the vibration system. It is showed that an equivalent dynamic model for the vibration system is established by using Lagrange method, and then treats it as actual system partially. A feedback control law is designed to make this nominal model stable. Uncertain part of system and outside disturbance are estimated using RBF neural network, and their upper boundary is obtained automatically. By constructing reasonable switch function, state variables can arrive at origin asymptotically along the sliding mode. Strong robust character of control system is proved by stability analysis and a numerical simulation example is performed to support this control scheme.

Geant4 simulation of a filtered X-ray source for radiation damage studies

Geant4 low energy extensions have been used to simulate the X-ray spectra of industrial X-ray tubes with filters for removing the uncertain low energy part of the spectrum in a controlled way. The results are compared with precisely measured X-ray spectra using a silicon drift detector. Furthermore, this paper shows how the different dose rates in silicon and silicon dioxide layers of an electronic device can be deduced from the simulations.

Fault detection for nonlinear networked control systems based on fuzzy observer

Security and reliability must be focused on control systems firstly, and fault detection and diagnosis (FDD) is the main theory and technology. Now, there are many positive results in FDD for linear networked control systems (LNCSs), but nonlinear networked control systems (NNCSs) are less involved. Based on the T-S fuzzy-modeling theory, NNCSs are modeled and network random time-delays are changed into the unknown bounded uncertain part without changing its structure. Then a fuzzy state observer is designed and an observer-based fault detection approach for an NNCS is presented. The main results are given and the relative theories are proved in detail. Finally, some simulation results are given and demonstrate the proposed method is effective.

A Parameter Varying Observer for the Enclosed Mass in a Spark Ignited Engine

In this paper, a high gain observer is designed for a parameter varying polytopic model to estimate the enclosed mass in the combustion chamber of a spark ignited engine. The high gain strategy allows the design of an observer that handles the uncertain part of the system. The observer uses the cylinder pressure measurement during the compression stroke to estimate the enclosed mass. An engine compression model is used as a virtual engine to build the observer. The results of the observer are compared to the virtual engine model and a good agreement between the observed variables and the model was obtained.

Uncertainty in bipolar preference problems

Preferences and uncertainty are common in many real-life problems. In this article, we focus on bipolar preferences and uncertainty modelled via uncontrollable variables, and we assume that uncontrollable variables are specified by possibility distributions over their domains. To tackle such problems, we concentrate on uncertain bipolar problems with totally ordered preferences, and we eliminate the uncertain part of the problem, while making sure that some desirable properties hold about the robustness of the problem and its relationship with the preference of the optimal solutions. We also consider several semantics to order the solutions according to different attitudes with respect to the notions of preference and robustness.