An improved car-following model with consideration of the lateral effect and its feedback control research

A car-following model is presented, in which the effects of non-motor vehicles on adjacent lanes are taken into ac- count. A control signal including the velocity differences between the following vehicle and the target vehicle is introduced according to the feedback control theory. The stability condition for the new model is derived. Numerical simulation is used to demonstrate the advantage of the new model including the control signal; the results are consistent with the analytical ones

On the Permanence of a Nonautonomous Nicholson’s Blowflies Model with Feedback Control and Delay

A nonautonomous Nicholson's Blowflies model with feedback control and delay is investigated in this paper. We show that for this system, feedback control variable has no influence on the persistent property of the system.

A population-feedback control based algorithm for well trajectory optimization using proxy model

Wellbore instability is one of the concerns in the field of drilling engineering.This phenomenon is affected by several factors such as azimuth,inclination angle,in-situ stress,mud weight,and rock strength parameters.Among these factors,azimuth,inclination angle,and mud weight are controllable.The objective of this paper is to introduce a new procedure based on elastoplastic theory in wellbore stability solution to determine the optimum well trajectory and global minimum mud pressure required(GMMPR).Genetic algorithm(GA) was applied as a main optimization engine that employs proportional feedback controller to obtain the minimum mud pressure required(MMPR).The feedback function repeatedly calculated and updated the error between the simulated and set point of normalized yielded zone area(NYZA).To reduce computation expenses,an artificial neural network(ANN) was used as a proxy(surrogate model) to approximate the behavior of the actual wellbore model.The methodology was applied to a directional well in southwestern Iranian oilfield.The results demonstrated that the error between the predicted GMMPR and practical safe mud pressure was 4%for elastoplastic method,and 22%for conventional elastic solution.

Confidence domain in the stochastic competition chemostat model with feedback control

This paper studies a stochastically forced chemostat model with feedback control in which two organisms compete for a single growth-limiting substrate. In the deterministic counterpart, previous researches show that the coexistence of two competing organisms may be achieved as a stable positive equilibrium or a stable positive periodic solution by different feedback schedules. In the stochastic case, based on the stochastic sensitivity function technique,we construct the confidence domains for different feedback schedules which allow us to find the configurational arrangements of the stochastic attractors and analyze the dispersion of the random states of the stochastic model.

Permanence of a Nicholson's Blowflies Model with Feedback Control and Multiple Time-varying Delays

This paper covers the dynamic behaviors for a class of Nicholson’s blowflies model with multiple time-varying delay and feedback control. By using the dierential inequality theory, a set of sucient conditions are obtained to ensure the permanence of the system. Our result shows that feedback control variables have no influence on the permanence of the system.

FORCE FEEDBACK MODEL OF ELECTRO-HYDRAULIC SERVO TELE-OPERATION ROBOT BASED ON VELOCITY CONTROL

The tele-operation robotic system which consists of an excavator as the construction robot, and two joysticks for operating the robot from a safe place are useful for performing restoration in damaged areas. In order to accomplish a precise task, the operator needs to feel a realistic sense of task force brought about from a feedback force between the fork glove of slave robot and unfamiliar environment. A novel force feedback model is proposed based on velocity control of cylinder to determine environment force acting on fork glove. Namely, the feedback force is formed by the error of displacement of joystick with velocity and driving force of piston, and the gain is calculated by the driving force and threshold of driving force of hydraulic cylinder. Moreover, the variable gain improved algorithm is developed to overcome the defect for grasping soft object. Experimental results for fork glove freedom of robotic system are provided to demonstrate the developed algorithm is available for grasping soft object.

Robust State Feedback H_∞Control for Dynamic Biped Robot Based on T-S Fuzzy Model

T-S fuzzy model was applied to describe nonlinear system and global fuzzy model was expressed by the form of uncertain system.Based on robust state feedback H_∞control strategy,designed a global asymptotic steady fuzzy model.This control system can use the experimental input-output data pairs for the biped robot learning and walking with dynamic balance.It is proved by simulation result that robust state feedback H_∞control method based on T-S fuzzy model can effectively restrain the effect of model uncertainties and external disturbance acting on biped robot.From these works,we showed the satisfactory performance of joint tracking without any chattering.

The Design of Output Feedback Distributed Model Predictive Controller for a Class of Nonlinear Systems

For a class of nonlinear systems whose states are immeasurable, when the outputs of the system are sampled asynchronously, by introducing a state observer, an output feedback distributed model predictive control algorithm is proposed. It is proved that the errors of estimated states and the actual system's states are bounded. And it is guaranteed that the estimated states of the closed-loop system are ultimately bounded in a region containing the origin. As a result, the states of the actual system are ultimately bounded. A simulation example verifies the effectiveness of the proposed distributed control method.

Fuzzy Adaptive Tracking Control of Uncertain Strict-Feedback Nonlinear Systems with Disturbances Based on Generalized Fuzzy Hyperbolic Model

In this paper, a fuzzy adaptive tracking control for uncertain strict-feedback nonlinear systems with unknown bounded disturbances is proposed. The generalized fuzzy hyperbolic model (GFHM) with better approximation performance is used to approximate the unknown nonlinear function in the system. The dynamic surface control (DSC) is used to design the controller, which not only avoids the “explosion of complexity” problem in the process of repeated derivation, but also makes the control system simpler in structure and lower in computational cost because only one adaptive law is designed in the controller design process. Through the Lyapunov stability analysis, all signals in the closed loop system designed in this paper are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the method is verified by a simulation example.

Generalized electro-biothermo-fluidic and dynamicalmodeling of cancer growth: state-feedback controlled cesium therapy approach

This paper develops a generalized dynamical model to describe the interactive dynamics between normal cells, tumor cells, immune cells, drug therapy, electromagnetic field of the human cells, extracellular heat and fluid transfer, and intercellular fractional mass of Oxygen, cell acidity and Pancreatin enzyme. The overall dynamics stability, controllability and observability have been investigated. Moreover, Cesium therapy is considered as a control input to the 11-dimensional dynamics using state-feedback controlled system and pole placement technique. This approach is found to be effective in driving the desired rate of tumor cell kill and converging the system to healthy equilibrium state. Furthermore, the ranges of the system dynamics parameters which lead to instability and growth of tumor cells have been identified. Finally, simulation results are demonstrated to verify the effectiveness of the applied approach which can be implemented successfully to cancer patients.

Output Feedback Dynamic Surface Controller Design for Airbreathing Hypersonic Flight Vehicle

This paper addresses issues related to nonlinear robust output feedback controller design for a nonlinear model of airbreathing hypersonic vehicle. The control objective is to realize robust tracking of velocity and altitude in the presence of immeasurable states, uncertainties and varying flight conditions. A novel reduced order fuzzy observer is proposed to estimate the immeasurable states. Based on the information of observer and the measured states, a new robust output feedback controller combining dynamic surface theory and fuzzy logic system is proposed for airbreathing hypersonic vehicle. The closedloop system is proved to be semi-globally uniformly ultimately bounded (SUUB), and the tracking error can be made small enough by choosing proper gains of the controller, filter and observer. Simulation results from the full nonlinear vehicle model illustrate the effectiveness and good performance of the proposed control scheme. © 2014 Chinese Association of Automation.

An Improved Robust Model Predictive Control Approach to Systems with Linear Fractional Transformation Perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.

A control method for congested traffic in the coupled map car-following model

Based on the pioneer work of Konishi et al, a new control method is presented to suppress the traffic congestion in the coupled map （CM） car-following model under an open boundary. A control signal concluding the velocity differences of the two vehicles in front is put forward. The condition under which the traffic jam can be contained is analyzed. The results axe compared with that presented by Konishi et al [Phys. Rev. 1999 E 60 4000-4007]. The simulation results show that the temporal behavior obtained by our method is better than that by the Konishi＇s et al. method, although both the methods could suppress the traffic jam. The simulation results are consistent with the theoretical analysis.

LMI-based output feedback fuzzy control of chaotic system with uncertainties

This paper studies the robust fuzzy control for nonlinear chaotic system in the presence of parametric uncertainties. An uncertain Takagi-Sugeno （T-S） fuzzy model is employed for fuzzy modelling of an unknown chaotic system. A sufficient condition formulated in terms of linear matrix inequality （LMI） for the existence of fuzzy controller is obtained. Then the output feedback fuzzy-model-based regulator derived from the LMI solutions can guarantee the stability of the closed-loop overall fuzzy system. The T-S fuzzy model of the chaotic Chen system is developed as an example for illustration. The effectiveness of the proposed controller design methodology is finally demonstrated through computer simulations on the uncertain Chen chaotic system.

Adaptive nonlinear model predictive control design of a flexible-link manipulator with uncertain parameters

This paper presents a novel adaptive nonlinear model predictive control design for trajectory tracking of flexible-link manipulators consisting of feedback linearization, linear model predictive control, and unscented Kalman filtering. Reducing the nonlinear system to a linear system by feedback linearization simplifies the optimization problem of the model predictive controller significantly, which, however, is no longer linear in the presence of parameter uncertainties and can potentially lead to an undesired dynamical behaviour. An unscented Kalman filter is used to approximate the dynamics of the prediction model by an online parameter estimation, which leads to an adaptation of the optimization problem in each time step and thus to a better prediction and an improved input action. Finally, a detailed fuzzy-arithmetic analysis is performed in order to quantify the effect of the uncertainties on the control structure and to derive robustness assessments. The control structure is applied to a serial manipulator with two flexible links containing uncertain model parameters and acting in three-dimensional space.

Investigation on full vehicle height control algorithm using feedback linearization method

Aiming to improve the control accuracy of the vehicle height for the air suspension system,deeply analyzing the processes of variable mass gas thermodynamics and vehicle dynamics,a nonlinear height control model of the air suspension vehicle was built. To deal with the nonlinear characteristic existing in the lifting and lowering processes,the nonlinear model of vehicle height control was linearized by using a feedback linearization method. Then,based on the linear full vehicle model,the sliding model controller was designed to achieve the control variables. Finally,the nonlinear control algorithm in the original coordinates can be achieved by the inverse transformation of coordinates. To validate the accuracy and effectiveness of the sliding mode controller,the height control processes were simulated in Matlab,i. e.,the lifting and lowering processes of the air suspension vehicle were taken when vehicle was in stationary and driving at a constant speed. The simulation results show that,compared to other controllers,the designed sliding model controller based on the feedback linearization can effectively solve the ＂overshoot＂problem,existing in the height control process,and force the vehicle height to reach the desired value,so as to greatly improve the speed and accuracy of the height control process. Besides,the sliding mode controller can well regulate the roll and pitch motions of the vehicle body,thereby improving the vehicle＇s ride comfort.

A control method applied to mixed traffic flow for the coupled-map car-following model

In light of previous work [Phys. Rev. E 60 4000 （1999）], a modified coupled-map car-following model is proposed by considering the headways of two successive vehicles in front of a considered vehicle described by the optimal velocity function. The non-jam conditions are given on the basis of control theory. Through simulation, we find that our model can exhibit a better effect as p = 0.65, which is a parameter in the optimal velocity function. The control scheme, which was proposed by Zhao and Gao, is introduced into the modified model and the feedback gain range is determined. In addition, a modified control method is applied to a mixed traffic system that consists of two types of vehicle. The range of gains is also obtained by theoretical analysis. Comparisons between our method and that of Zhao and Gao are carried out, and the corresponding numerical simulation results demonstrate that the temporal behavior of traffic flow obtained using our method is better than that proposed by Zhao and Gao in mixed traffic systems.

Feedback control strategy of longitudinal temperature and finished carbonization time for coke oven and its application

Based on the detailed analysis of the third coke oven in BaoSteel, a feedbackcontrol strategy of longitudinal temperature and finished carbonization time of coke ovens wasproposed and it was applied to the third coke oven in BaoSteel. As a result, the ratio of theinstance that the absolute deviation of the longitudinal temperature is within +- 7 deg C and thefinished carbonization time within +- 10 min is more than 80 percent, having acquired the patentsaving effect of an energy consumption lowered by 2.92 percent. At the same time, it can provide anexample for the same coke ovens inside and outside the nation.

Control Schemes to Reduce Risk of Extinction in the Lotka-Volterra Predator-Prey Model

The Lotka-Volterra predator-prey model is widely used in many disciplines such as ecology and economics. The model consists of a pair of first-order nonlinear differential equations. In this paper, we first analyze the dynamics, equilibria and steady state oscillation contours of the differential equations and study in particular a well-known problem of a high risk that the prey and/or predator may end up with extinction. We then introduce exogenous control to reduce the risk of extinction. We propose two control schemes. The first scheme, referred as convergence guaranteed scheme, achieves very fine granular control of the prey and predator populations, in terms of the final state and convergence dynamics, at the cost of sophisticated implementation. The second scheme, referred as on-off scheme, is very easy to implement and drive the populations to steady state oscillation that is far from the risk of extinction. Finally we investigate the robustness of these two schemes against parameter mismatch and observe that the on-off scheme is much more robust. Hence, we conclude that while the convergence guaranteed scheme achieves theoretically optimal performance, the on-off scheme is more attractive for practical applications.

Control Chaos of a Unidirectionally Traffic Coupled Map Lattice Model

Spatiotemporal chaos is studied by using the unidirectional traffic coupled lattice model with hyperbolic tangent local map. The coupled map lattice (CML) model can simulate the complex traffic flow phenomenon which is similar to the traditional traffic flow model. The nonlinear feedback method is used to study the control of the chaotic system of the unidirectionally traffic coupled map lattice model. The stability of spatiotemporal chaos in the coupled map lattice is realized. The results of numerical simulation show that there is a relationship between control results and control parameters when controlling spatiotemporal chaos to a uniform stable state in a certain phase space compression parameter region.