Adaptive Control Technology Research Articles

Assessing the energy impact of traffic management and vehicle hybridisation

We provide a review of methodologies previously used to evaluate impacts of transportation systems and changes in transportation infrastructure on energy consumption. We present a new framework that allows estimating the energy impacts of managed traffic lanes in the context of vehicle automation. The presented framework relies on two major components, an integrated transportation system simulator and a powertrain simulator. For the transportation system simulator we propose using integrated transportation system simulator POLARIS. For the powertrain simulator we use AUTONOMIE, a tool funded by the US Department of Energy. Both tools are developed at Argonne National Laboratory. We demonstrate our approach by modelling a transportation corridor along a major highway. Two scenarios are considered, unmanaged, when both trucks and cars use all the lanes of the highway and managed, under which one of the highway lanes is a dedicated lane for truck traffic and trucks are forming platoons using adaptive cruise control technology. We provide the numerical results of the experiment at the end of the paper. We also present the impact of vehicle hybridisation combined with automation on the energy consumption.

Vibration Isolation for Active Suspensions With Performance Constraints and Actuator Saturation

This paper investigates the problem of vibration isolation for vehicle active suspension systems, where parameter uncertainties, external disturbances, actuator saturation, and performance constraints are considered in an unified framework. A constrained adaptive robust control technology is developed to not only stabilize the attitude of vehicle in the context of parameter uncertainties and external disturbances, but also cover the problems of actuator saturation and performance constraints. Furthermore, the performance analysis of the closed-loop systems is given, by means of rigorous mathematical derivations. Extensive comparative experimental results are obtained to illustrate the effectiveness of the proposed control law.

民用飞机导光板自适应调光控制技术研究

民用飞机导光板自适应调光控制技术研究

Learning Drivers’ Behavior to Improve Adaptive Cruise Control

Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers. In this article, we focus on the adaptive cruise control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver’s preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce an approach to combine machine learning algorithms with demographic information and behavioral driver models into existing automated assistive systems. This approach can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This approach sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers’ behavior exclusively based on the ACC’s sensors, we found that improved learning models could be developed by adding information on drivers’ demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

Nonlinear adaptive trajectory tracking control for a quad-rotor with parametric uncertainty

A novel adaptive control algorithm to trajectory tracking control problem for a quad-rotor with uncertain mass and inertial parameters is presented based on the noncertainty equivalence principle. The estimation laws for quad-rotor mass and inertial parameters are developed with the introduction of stable attracting manifolds into the position-loop and attitude-loop adaptation process, respectively. The position and attitude trajectory tracking control algorithms are constructed through a unified immersion and invariance adaptive control technology. With extracting the desired attitude trajectory from the position-loop, the desired attitude trajectory for the attitude-loop is calculated from a command filter. The ultimately uniformly boundedness of the closed-loop system signals is derived for the quad-rotor under the proposed control algorithm. Simulation results demonstrate the performance of the proposed approach.

Material removal with constant depth in CNC laser milling based on adaptive control of laser fluence

Computer numerical control CNC laser milling is an innovative technology to produce precision parts in micron and even nanometer scale. However, due to the restriction of dynamic performance of the machine tool, the preset feed rate cannot be reached immediately, and the actual feed rate is unpredictable when the part with rapidly varied geometric feature is machined. At this moment, the incident laser energy is still the default value, so the feed rate below the target value will cause excessive accumulation of laser fluence into the workpiece surface, eventually leading to an inhomogeneous milling depth and developing the excessive heat affect zone (HAZ). Aiming at the problem in CNC laser milling of the parts with rapidly varied geometric feature in this study, a method of real-time adaptive control of laser fluence according to actual feed rate was proposed. Firstly, a prediction model of laser milling depth was established. Then, based on the model and small amount of preliminary trials, laser fluence required at arbitrary feed rate was obtained to realize the material removal with constant depth. Furthermore, the adaptive control technology of laser fluence on the basis of actual feed rate was investigated. By monitoring the actual feed rate of the machine tool and regulating the laser fluence with it, the machining depth of the parts with rapidly varied geometric feature by CNC laser milling can remain constant. CNC laser milling experiments conducted in this paper demonstrate the feasibility and veracity of the adaptive control strategy.

Advanced In-Process Control System for Sheet Stamping and Tube Hydroforming Processes

A sophisticated servo press with the digital control has been developed and attracted attention in recent years. By utilizing its high function in-process, servo presses have a potential to enhance the forming limit and to improve quality and accuracy of product not only in sheet stamping but also in tube hydroforming processes. On the other hand, in-process control and adaptive process control technologies in metal forming processes using intelligent technique and soft computing have been investigated and developed previously. Nowadays we are in a good environment to realize further advanced adaptive in-process control in metal forming process. To further advance this technology, sensing system is essential element and it should be applied to feedback control optimally in their forming operation. This paper describes the current situation on advanced intelligent process control technology for sheet stamping and tube hydroforming processes on the basis of the research results by the author.

MPPT Strategy of PV System Based on Adaptive Fuzzy Control

In order to use photovoltaic cell effectively and improve its photoelectric conversion efficiency, the maximum power point of photovoltaic generation system should be tracked rapidly and stably [1]. Taking into account the solar PV systems are often affected by external factors,it is difficult to determine system parameters, and has a strong non-linear,so this paper,a adaptive fuzzy logic control technology for STP0950S-36 type of independent photovoltaic systems to a adaptive fuzzy controller design method, and using MATLAB/SIMULINK,fuzzy logic toolbox for simulation tools such as maximum power point control,adaptive fuzzy control simulation results of MPPT with fixed step method compared to fixed-step method was found to reach steady there is a certain state after the fluctuation,The results show that the method can quickly and correctly track change of MPP in different light intensity and the system has excellent stability performance.

Learning from adaptive neural dynamic surface control of strict-feedback systems.

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Research on a New Type of Self-Propelled Artillery Control System Test Equipment

This paper regard the self-propelled artillery control system as the research object, by using the adaptive control, data fusion and dual-mode modulation technology, construction of the hardware platform of the system is made, the fault diagnosis model and repair information management system is established, finally a corresponding test equipment is developed. the device has strong Integrity , good versatility, high stability, realize offline detection, parts of the joint debugging and commissioning and quality evaluation, the comprehensive fault diagnosis, repair decision-making and information management, greatly improve the force protection equipment repair capacity.

Adaptive Control System of Coated Seed Dryer

In order to improve the efficiency of the coated seed dryers, improve the drying quality and reduce energy consumption, the adaptive control technology of coated seed dryer was studied. Detection system for temperature measuring, material level measuring, and wind speed and direction measuring were fixed on the drying equipment. According to the detected data, the adaptive control system controlled the dryer blade to adjust the temperature and intensity of air intake. This technology accurately controlled the drying air temperature, moisture and moisture content in the seeds. The test results show that the drying efficiency is 1.38 times of the original dryer, while the power consumption reduces to 40.05%, and intake air temperature reduced 19.84°C, which can improve the seed activity.

Adaptive Robust Vibration Control of Full-Car Active Suspensions With Electrohydraulic Actuators

This paper investigates the problem of vibration suppression in vehicular active suspension systems, whose aim is to stabilize the attitude of the vehicle and improve the riding comfort. A full-car model is adopted, and electrohydraulic actuators with highly nonlinear characteristics are considered to form the basis of accurate control. In this paper, the H∞ performance is introduced to realize the disturbance suppression by selecting the actuator forces as virtual inputs, and an adaptive robust control technology is further used to design controllers which help real force inputs track virtual ones. The resulting controllers are robust against both actuator parametric uncertainties and uncertain actuator nonlinearities. The stability analysis for the closed-loop system is given within the Lyapunov framework. Finally, a numerical example is given to illustrate the effectiveness of the proposed control law, where different road conditions are considered in order to reveal the closed-loop system performance in detail.

A new touchscreen application to retrieve speech information efficiently

In this paper, we describe an adaptive speech rate control technology for ultrafast listening that is equivalent to skimming. Nowadays, listening to audio books on mobile devices is quite common. Therefore, in the future we will obtain ever-increasing amounts of information through speech instead of conventional printed materials. People read books at various levels of detail from close reading to skimming. Although a similar feature to skimming is required to efficiently obtain information from audio sources, there is no tool equivalent to skimming for audio playback. Thus, we have developed a new speech rate conversion method to efficiently obtain information from audio sources with very fast replay. This algorithm will help not only sighted people to enjoy audio books but also visually impaired people because almost all of their information is obtained from speech. Thus, the implementation of this technology on special audio players for visually impaired people as a new replay function is expected to be useful. Moreover, this technology should be useful for all audio book listeners, not only people with limited sight.1

Adaptive Current Control with PI-Fuzzy Compound Controller for Shunt Active Power Filter

An adaptive control technology and PI-fuzzy compound control technology are proposed to control an active power filter (APF). AC side current compensation and DC capacitor voltage tracking control strategy are discussed and analyzed. Model reference adaptive controller for the AC side current compensation is derived and established based on Lyapunov stability theory; proportional and integral (PI) fuzzy compound controller is designed for the DC side capacitor voltage control. The adaptive current controller based on PI-fuzzy compound system is compared with the conventional PI controller for active power filter. Simulation results demonstrate the feasibility and satisfactory performance of the proposed control strategies. It is shown that the proposed control method has an excellent dynamic performance such as small current tracking error, reduced total harmonic distortion (THD), and strong robustness in the presence of parameters variation and nonlinear load.

Research on Adaptive Power Control System of Working Machinery

The load changes of working machinery in the running process will certainly result in poor matches in the characteristics of the transmission system. For the issue, this paper studies the adaptive power control model and algorithm. The adaptive power control system is developed using the adaptive power control technology based on the load identification, and is applied in the full AC electric forklift and the CNC band sawing machine. The variable frequency system and intelligent control method based on the integration of the load characteristic recognition and control proposed in this paper, adaptively adjust the parameters of the PID controller according to the load characteristic parameters identified real-timely for the transmission system of the working machinery, so as to achieve the best performance matches, the highest effectiveness and lowest energy consumption of the entire transmission system.

Intelligent Control of Aluminium Rolling Mills Using Two Dimensional Adaptive Filters

The thickness of the aluminium sheets emerging from a rolling mill usually contains several unwanted frequency components. These tend to come from the cyclic and eccentric effects of the rotating machinery. In addition to these effects, the measurement of sheet thickness is made using radiation sources - either nucleonic or X-Ray which, by their very nature, introduce a random noise component into the measurement. The control technology currently used in the aluminium rolling mills is not able to successfully isolate and compensate for the periodic components present in the thickness profile of the rolled sheets. Due to this problem, there are difficulties in accurate production and the related losses for aluminium industry are significant. This paper discusses novel idea of applying adaptive filtering algorithm to reduce periodic variations in the produced thickness profiles in a typical aluminium rolling mill. As the modifications and testing of new control algorithms on the existing rolling mills systems are neither simple nor a cost effective exercises, proposed algorithm is simulated and tested using some typical signals and system parameters measured on a number of rolling mills in the industrial situation. The obtained results prove the effectiveness of the proposed approach and encourage further investigations and field trials of adaptive filtering and active control technology in the rolling mills industry.

Toward Adapting Cars to Their Drivers

Traditionally, vehicles have been considered as machines that are controlled by humans for the purpose of transportation. A more modern view is to envision drivers and passengers as actively interacting with a complex automated system. Such interactive activity leads us to consider intelligent and advanced ways of interaction leading to cars that can adapt to their drivers.In this article, we focus on the adaptive cruise control (ACC) technology that allows a vehicle to automatically adjust its speed to maintain a preset distance from the vehicle in front of it based on the driver's preferences. Although individual drivers have different driving styles and preferences, current systems do not distinguish among users. We introduce a method to combine machine‐learning algorithms with demographic information and expert advice into existing automated assistive systems. This method can reduce the interactions between drivers and automated systems by adjusting parameters relevant to the operation of these systems based on their specific drivers and context of drive. We also learn when users tend to engage and disengage the automated system. This method sheds light on the kinds of dynamics that users develop while interacting with automation and can teach us how to improve these systems for the benefit of their users. While generic packages such as Weka were successful in learning drivers' behavior, we found that improved learning models could be developed by adding information on drivers' demographics and a previously developed model about different driver types. We present the general methodology of our learning procedure and suggest applications of our approach to other domains as well.

Development of Adaptive Acoustic Impedance Control Technologies of Acoustic Duct Liner

This paper describes the development of adaptive acoustic impedance control (AAC) technologies to achieve a larger fan noise reduction, by adaptively adjusting reactance and resistance of the acoustic liner impedance. For the actual proof of the AAC technology III performance, the advanced fan noise absorption control duct liner II was made on trial basis, with the simple control system and the plain device. And, then, the duct liner II was examined for the AAC technology I, II, and III models, using the high speed fan test facility. The test results made clear that the duct liner II of the AAC technology III model could achieve the fan noise reduction higher than O.A. SPL 10 dB (A) at the maximum fan speed 6000 rpm, containing the reduction of fundamental BPF tone of 18 dB and 2nd BPF tone of 10 dB in response to the fan peed change from 3000 to 6000 rpm.

Application of Adaptive Neural Network Control Technology in Active Vibration Isolation Control

Application of Adaptive Neural Network Control Technology in Active Vibration Isolation Control

COLA: COmmon Layer Architecture for Adaptive Power Control and Access Technology Assignment in New Generation Networks

Cognitive radio in network core devices, such as basestations, is being considered as a spectrum management solution for future society's communication demands. Aside from new resource allocation algorithms, efficient inter- and intra-protocol processing should be considered. In this paper, we propose an opportunistic cross layer architecture called COmmon Layer Architecture (COLA) for information exchange between arbitrary layers in New Generation Networks with network-oriented cognitive radio. COLA provides a means for faster information exchange between OSI layers by introducing abstraction at each layer and designing primitives for communication between each abstraction layer. Emulation and simulation results showed improvements, compared to conventional networks, in disrupted service (∼42% reduction), average delay (∼47% reduced) and packet drop ratio (∼22% reduced) in the scenario analyzed.