Nonlinear Time-varying Characteristics Research Articles

Advanced emergency braking controller design for pedestrian protection oriented automotive collision avoidance system.

Automotive collision avoidance system, which aims to enhance the active safety of the vehicle, has become a hot research topic in recent years. However, most of the current systems ignore the active protection of pedestrian and other vulnerable groups in the transportation system. An advanced emergency braking control system is studied by taking into account the pedestrians and the vehicles. Three typical braking scenarios are defined and the safety situations are assessed by comparing the current distance between the host vehicle and the obstacle with the critical braking distance. To reflect the nonlinear time-varying characteristics and control effect of the longitudinal dynamics, the vehicle longitudinal dynamics model is established in CarSim. Then the braking controller with the structure of upper and lower layers is designed based on sliding mode control and the single neuron PID control when confronting deceleration or emergency braking conditions. Cosimulations utilizing CarSim and Simulink are finally carried out on a CarSim intelligent vehicle model to explore the effectiveness of the proposed controller. Results display that the designed controller has a good response in preventing colliding with the front vehicle or pedestrian.

Simulation Study on Drawworks Heave Compensation System of Offshore Oil Drilling

In order to improve the efficiency of offshore oil drilling, simplify the structure of drilling rigs, a new type of heave compensaton drawworks is put forward to realize decoupling control of motion and energy saving by hydraulic energy storage. In order to study the compensation performance of drawworks, mathematical simulation model is built, heave compensation displacement controller of fuzzy-PI and bit pressure controller of BP neural network are designed based on the high inertia load and nonlinear time-varying characteristics of the system. The simulation results show that the heave compensation maintains the absolute displacement of the hook within certain premises and reduces the effects on bit pressure by platform heave movement, automatic bit feeding keeps bit pressure steady, the bit pressure is kept in the scope prescribed by work. The research result shows that the device well meets the performance requirements of offshore drilling.

A Fuzzy Self-tuning Controlled Feeding Servo System of Machine Tool

The permanent magnet synchronous motor(PMSM) has been used widely for computer numerically controlled (CNC) feeding servo system (FSS) of machine tools. The control performance of the actual CNC-FSS depends on many parameters, and it has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate model for the actual CNC-FSS, therefore, a fuzzy self-tuning controller (FSC) of CNC-FSS was developed to overcome this problem in this paper. The proposed control strategy was based on fuzzy control theory combined with the conventional proportion integration differentiation (PID) control and its control algorithm. In this paper, we presented design of controller using FSC method and analyzed time response of the intelligent controller. Step response was plotted and analyzed to verify the effectiveness of the proposed controllers. Simulation results show that the FSC has minimum overshoot, minimum transient and steady state parameters, which shows the more effectiveness and efficiency of FSC than conventional PID controller to control the position of the CNC-FSS. DOI: http://dx.doi.org/10.11591/telkomnika.v11i5.2459

Stability Sliding Mode Control for Dual-Motor Independent Drive Electric Vehicle Based on Transient Reference Model

Motors of Independent Drive Electric Vehicle can be controlled independently, which can enrich the method of stability control for vehicle. In order to fully take use of the advantage，The man-vehicle system with eight degrees of freedom ,which includes Magic Formula tire model and driver model based on Preview Follower Theory, was set up by using MATLAB/SIMULINK. Considering the complexity of the driving condition and the nonlinear time-varying characteristics of the vehicle system, a Sliding mode controller based on transient reference model was set up, which selected Yaw rate as Control variable and two motors as actuators. The simulation results show that handling and stability of vehicle can be improved greatly by the Sliding mode controller.

Stability Control for Dual-Motor Independent Drive Electric Vehicle Based on Sliding Mode Control

Two motors of Dual-Motor Independent Drive Electric Vehicle can be controlled independently, which can enrich the method of stability control for vehicle. In order to fully take use of the advantage，The man-vehicle system with eight degrees of freedom ,which includes Magic Formula tire model and driver model based on Preview Follower Theory, was set up by using MATLAB/SIMULINK. Considering the complexity of the driving condition and the nonlinear time-varying characteristics of the vehicle systems, a controller based on Sliding mode variable structure control theory was set up, which selected Yaw rate as Control variable and two motors as actuators. The simulation results show that handling and stability of vehicle can be improved greatly by the Sliding mode controller.

Adaptive Recurrent Network Network Uncertainty Observer Based Integral Backstepping Control for a PMSM Drive System

The permanent magnet synchronous motor (PMSM) is suitable for high-performance servo applications and has been used widely for the industrial robots, computer-numerically-controlled (CNC) machine tools and elevators. The control performance of the actual PMSM drive system depends on many parameters, such as parameter variations, external load disturbance, and friction force. Their relationships are complex and the actual PMSM drive system has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate model for the nonlinear uncertainty and time-varying characteristics of the actual PMSM drive system Therefore, an adaptive recurrent neural network uncertainty observer (ARNNUO) based integral backstepping control system is developed to overcome this problem in this paper. The proposed control strategy is based on integral backstepping control combined with RNN uncertainty observer to estimate the required lumped uncertainty. An adaptive rule of the RNN uncertainty observer is employed to on-line adjust the weights of sigmoidal functions by using the gradient descent method and the backpropagation algorithm in according to Lyapunov function. This ARNNUO has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Experimental results are executed to show the control performance of the proposed control scheme. DOI: http://dx.doi.org/10.11591/ijpeds.v2i2.260

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

Adaptive Fuzzy Sliding-Mode Controller for Control of the Strip Casting Process

It is well known that the casting and rolling processes of strip casting are integrated into a single production step for the manufacture of thin steel strips directly from molten metal. The quality of the strip casting process depends on many process parameters, such as the level of molten steel in the pool, solidification position, and roll gap. Their dynamic relationships are complex and the strip casting process has non-linear uncertainty and time-varying characteristics, so it is difficult to establish an accurate process model for designing a model-based controller to monitor strip quality. Here, a model-free adaptive fuzzy sliding-mode controller, which combines an adaptive rule with fuzzy and sliding-mode control, is proposed to monitor the level of molten metal. The proposed control strategy has online learning ability for responding to the system's non-linear and time-varying behaviours during control of the molten steel level. Since this model-free controller has a simple control structure and a small number of control parameters, it is easy to implement. Simulation results based on the dynamic model and parameters of a semi-experimental system are executed to show the control performance of the proposed intelligent controller.

Self-organizing fuzzy controller for the molten steel level control of a twin-roll strip casting process

The twin-roll strip casting process is a steel-strip production method that combines continuous casting and hot rolling process in a single operation. The quality of strip casting process depends on many process parameters, such as molten steel level in the tundish, solidification position and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. Hence, it is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free control strategy is employed to overcome this problem. The self-organizing fuzzy control (SOFC) strategy is developed to control the molten steel level of a strip casting process. It has on-line learning ability and the rule tables can be modified automatically and continuously for responding to the system's nonlinear and time-varying behaviors. Since this model-free controller has simple control structure and small number of control parameters, it is easy to implement. Simulation results based on semi-experimental system dynamic model and parameters are executed to show the control performance of the proposed intelligent controller.

Noise Analysis and Simulation Method for a Single-Slope ADC With CDS in a CMOS Image Sensor

Many mixed-signal circuits are nonlinear time-varying systems whose noise estimation cannot be obtained from the conventional frequency domain noise simulation (FNS). Although the transient noise simulation (TNS) supported by a commercial simulator takes into account nonlinear time-varying characteristics of the circuit, its simulation time is unacceptably long to obtain meaningful noise estimation results. Since the single-slope analog-to-digital converter with correlated double sampling (CDS/SS-ADC) in a CMOS image sensor (CIS) is composed of several operation phases in which the circuit topologies are different from each other, the noise cannot be estimated by the conventional FNS. This paper presents a noise estimation method for the CDS/SS-ADC that uses the FNS results while the transient noise behavior is taken into account. The proposed method provides noise estimation results closer to that of the TNS than the conventional FNS, whereas the simulation time is about the same as that of the FNS.

Energy-Balance Modeling and Discrete Control for Single-Phase Grid-Connected PV Central Inverters

This paper presents a two-control loop design considering the nonlinear time-varying characteristics of a single-phase grid-connected photovoltaic (PV) full-bridge central inverter. The control scheme design is based on the energy-balance modeling of the PV system and enables the design of a voltage loop linear discrete controller ensuring the stability of the system for the whole range of PV array operating conditions. A set of experimental results carried out on a laboratory prototype is provided to validate the proposed approach.

Lyapunov-based decentralized adaptive control for laser beam tracking systems

A successful controller design is crucial for establishing and maintaining an optical link between free-space communication stations engaged in a laser communication session. This task is quite difficult due to nonlinear behavior, cross-coupled dynamics, and time-varying characteristics of all known beam steering technologies. A novel adaptive control technique utilizing Lyapunov function to ensure global asymptotic stability of the system, thus resulting in a highly robust system performance, is developed. The technique applied to a piezo-electric mirror setup results in a highly efficient controller design that does not require prior knowledge of system dynamics, while providing independent access to azimuth and elevation positions of the laser beam. The basic algorithm is presented and the results of its application are demonstrated.

Dynamics of Hypoid Gear Transmission With Nonlinear Time-Varying Mesh Characteristics

The coupled translation-rotation vibratory response of hypoid geared rotor system due to loaded transmission error excitation is studied by employing a generalized 3-dimensional dynamic model. The formulation includes the effects of backlash nonlinearity as well as time-dependent mesh position and line-of-action vectors. Its mesh coupling is derived from a quasi-static, 3-dimensional, loaded tooth contact analysis model that accounts for the precise gear geometry and profile modifications. The numerical simulations show significant tooth separation and occurrence of multi-jump phenomenon in the predicted response spectra under certain lightly loaded operating conditions. Also, resonant modes contributing to the response spectra are identified, and cases with super-harmonics are illustrated. The computational results are then analyzed to quantify the extent of non-linear and time-varying factors.

A mathematical analysis of the mortality kinetics of Drosophila melanogaster exposed to gamma radiation

A mathematical model with nonlinear time-varying characteristics has been developed which describes the relationship between the kinetics of natural aging and radiation-induced delayed mortality. Based on this model, it appears that there is an immediate effect of radiation which is continuously, but nonlinearly, increasing in severity. Two phases appear in this variation, corresponding to the two phases (plateau and dying phase) of the mortality curves for control populations. Accordingly, S/ E (survival time post-irradiation/further expectation of life) can best be interpreted as an increasing function during the plateau phase of normal mortality curves, which levels off during the ensuing dying phase.