Shi-Yuan Han

Fault diagnosis and fault-tolerant tracking control for discrete-time systems with faults and delays in actuator and measurement

Abstract In this paper, the fault diagnosis (FD) and fault-tolerant tracking control (FTTC) problem for a class of discrete-time systems with faults and delays in actuator and measurement is investigated. In the first step, a discrete delay-free transformation approach is introduced for an constructed augmented system such that the two-point-boundary-value (TPBV) problem with advanced and delayed items can be avoided. Then, the optimal fault-tolerant tracking controller (OFTTC) is proposed with respect to an equivalent reformed quadratic performance index. Moreover, by using the real-time system output rather than the residual errors, a reduced-order-observer-based fault diagnoser for the augmented system is designed to diagnose faults in actuator and measurement, and solve the physically unrealizable problem of proposed OFTTC. Finally, the effectiveness of the proposed fault diagnoser and OFTTC is illustrated by a realistic design example for industrial electric heater.

Sensor Fault and Delay Tolerant Control for Networked Control Systems Subject to External Disturbances

In this paper, the problem of sensor fault and delay tolerant control problem for a class of networked control systems under external disturbances is investigated. More precisely, the dynamic characteristics of the external disturbance and sensor fault are described as the output of exogenous systems first. The original sensor fault and delay tolerant control problem is reformulated as an equivalence problem with designed available system output and reformed performance index. The feedforward and feedback sensor fault tolerant controller (FFSFTC) can be obtained by utilizing the solutions of Riccati matrix equation and Stein matrix equation. Based on the designed fault diagnoser, the proposed FFSFTC is further reconstructed to compensate for the sensor fault and delayed measurement effects. Finally, numerical examples are provided to illustrate the effectiveness of our proposed FFSFTC with different cases with various types of sensor faults, measurement delays and external disturbances.

Comparison between genetic algorithm and differential evolution algorithm applied to one dimensional bin-packing problem

The one dimensional bin-packing problem (BPP) based on the genetic algorithm (GA) and differential evolution (DE) is studied. First of all, we propose the mathematical model of one BPP. In the next step, in order to obtain the solution of BPP, we design the detailed processes and strategies for one BPP based on the GA and DE algorithms and analyze the mainly different between two algorithms. Finally, some results from simulations are illustrated to prove the effectiveness of the proposed algorithms and verified the discussed conclusion.

Prediction of protein structure classes

Prediction of protein special structural plays a significant role to better recognize the protein folding patterns. Multiple prediction methods may be used to predict the structures based on the information of sequences and biostatistics. The accuracy, nevertheless, is strongly affected by the efficiency of classification, the robustness of model and other factors. In our research, flexible neutral tree (FNT), a novel classification model, is employed as the base classifiers. The alterable structural tree take advantage of the selection of available features, aims to improve the efficiency. To examine the performance and efficiency of such algorithm combination, an ASTRAL dataset is selected as the test dataset. Our results show that a higher prediction accuracy could be achieved compared with other methods, the structure of the classification model for prediction of protein structural may make incremental improvements possible.

Classification of Concrete Strength Grade Using Nearest Neighbor Partitioning

Concrete is an important building material in the field of civil engineering. As an important factor, the strength of concrete affects its quality directly. Although conventional methods are made to forecast concrete strength, the classification of its grade is still an important issue in terms of non-uniformity of mortar and the complexity of curing condition. In this study, the classification of strength grade is implemented by employing the nearest neighbor partitioning method-based neural network classifier, which not only produces flexible decision boundaries but also eliminates centroid-based constraints and further enlarges the opportunity for finding optimal solutions. Experimental results manifest that the adopted method improves the performance of concrete grade classification.

Dynamical Analysis of a Stochastic Neuron Spiking Activity in the Biological Experiment and Its Simulation by I Na , P + I K Model

An irregular on-off like spiking activity is observed in the rat neural pacemaker experiment with the changes of extracellular calcium concentration. The spiking activity is simulated using a minimal model, the stochastic INa,P + I K model. The nonlinear time series analysis on ISI series shows the similar stochastic dynamical features of both experimental and simulated results. The power spectrum and SNR analysis suggests that this spiking activity is the autonomous stochastic resonance induced by noise near a subcritical Hopf bifurcation. Thus, it becomes easy for us to compare different stochastic firing patterns observed in the same experiment and stimulated under one same model. Besides, some deterministic-like characteristics by the analysis results on ISI series were also explained in this paper.

Optimal Disturbance Rejection with Zero Steady-State Error for Nonlinear Vehicle Suspension Systems under Persistent Road Disturbances

The road disturbance rejection problem for vehicle active suspension involving the nonlinear characteristics is researched in this paper. A continuous-time state space of nonlinear vehicle active suspension is established first, in which the road disturbance is generated from the output of an introduced exosystem based on the ground displacement power spectral density. After that, based on the dynamics of road roughness and the internal model principle, a disturbance compensator with zero steady-state error is designed, which is related to the dynamic characteristics of road disturbance and independent of the control system model. By combining the vehicle active suspension system and the designed road disturbance compensator, an augmented system is obtained without explicit indication of road disturbance. Then by solving a series of decoupled nonlinear two-point-boundary-value problem and employing an iterative computing algorithm, an approximation optimal road disturbance rejection controller is obtained. Finally, the simulation results illustrate that the proposed approximation optimal road disturbance rejection controller can reduce the values of sprung mass acceleration, tire deflection, suspension deflection, and energy consumption and compensate the nonlinear behaviors of vehicle active suspension effectively.

A Stochastic Neural Firing Generated at a Hopf Bifurcation and Its Biological Relevance.

The integer multiple firing patterns, generated in the rabbit depressor baroreceptors under the different static blood pressure, were observed between the resting state and the periodic firing and were characterized to be stochastic but not chaotic by a series of nonlinear time series estimations. These patterns exhibited very similar characteristics to those observed in the experimental neural pacemaker. Using I na,p + I K models with dynamics of a supercritical Hopf bifurcation, we successfully simulated the bifurcation process of firing patterns and observed the induction of the integer multiple firing patterns by adding noise. The results strongly suggest that the integer multiple firing rhythms generated by rabbit baroreceptors result from the interplay between noise and the system’s dynamics. Because of the important normal physiological function of baroreceptors, the biological significance of noise and the noise-induced firing rhythms at a Hopf bifurcation is interesting to be addressed.

Safety Inter-vehicle Policy Based on the Longitudinal Dynamics Behaviors

The analysis of the safety inter-vehicle distance plays important roles for driving assistant system, which can give the warning signal to drivers timely. In order to provide the drivers a warning signal about an impending collision reasonable and timely, safety inter-vehicle policies between host vehicle and preceding vehicle are proposed based on the longitudinal dynamics behaviors in this paper. First, by analyzing the driving force and resistance force generated from the road surface, the basis safety inter-vehicle distance is designed, in which the surface friction coefficient and the air’s visibility are considered. Taken the driving state of preceding vehicle into consideration, including braking hard until to a complete stop and braking with constant deceleration, the safety inter-vehicle policies are derived from the basis safety inter-vehicle distance, which is composed of the sliding distance, duration distance and deceleration distance. Finally, by comparing with the classic safety distances, the effectiveness and elasticity of the proposed inter-vehicle policies are illustrated.

Global Adaptive and Local Scheduling Control for Smart Isolated Intersection Based on Real-Time Phase Saturability

Linking real-time phase saturability directly to the traffic signal control, the global adaptive control scheme for traffic light loop and the local scheduling control strategy for phase green time are proposed in this paper for real-time traffic signal control systems with multiple phases. First, applying the real-time phase saturability of each phase as the time-varying weight factor, an elastic scheduling model is designed to describe the competitive relationship among the different phase in a traffic light loop. Then the traffic green time scheduling problem in a traffic light loop is formulated as a trade-off optimization problem between the green light time and real-time phase saturability for each phase. By solving a quadratic programming problem that seeks the minimum value of the sum of the squared deviation between the green time and the maximum allowable green time in each phase, the allocated green time in next traffic loop is obtained. If the total real-time traffic load exceeds the allocable maximum value or unreaches the allocable minimum value, the proportional global adaptive control schemes are triggered for rearranging traffic light loop time. Undertaking different traffic flow conditions, the effectiveness of proposed adaptive control schemes and scheduling strategies are illustrated compared with the classic average allocating method.