An automatic gear-shifting strategy for manual transmissions

2007 ◽  
Vol 221 (5) ◽  
pp. 757-768 ◽  
Author(s):  
B Mashadi ◽  
A Kazemkhani ◽  
R Baghaei Lakeh
Keyword(s):  
Fuzzy Inference ◽  
Control Method ◽  
Fuzzy Controller ◽  
Step Function ◽  
Maximum Speed ◽  
Vehicle Speed ◽  
Deceleration Phase ◽  
Shifting Strategy ◽  
Automated Manual Transmission ◽  
Driver’S Intention

Based on two different criteria, namely the engine working conditions and the driver's intention, the governing parameters in decision making for gear shifting of an automated manual transmission are discussed. The gear-shifting strategy was designed by taking into consideration the effects of these parameters, with the application of a fuzzy control method. The controller structure is formed in two layers. In the first layer, two fuzzy inference modules are used to determine the necessary outputs. In the second layer a fuzzy inference module makes the decision of shifting by upshift, downshift, or maintain commands. The behaviour of the fuzzy controller is examined by making use of ADVISOR software. It is shown that at different driving conditions the controllers make correct decisions for gear shifting accounting for the dynamic requirements of the vehicle. It is also shown that the controller based on both the engine state and the driver's intention eliminates unnecessary shiftings that are present when the intention is overlooked. A microtrip is designed in which a required speed in the form of a step function is demanded for the vehicle on level or sloping roads. Both strategies for the vehicle to reach the maximum speed starting from rest allow the gear shift to be made consecutively. Considerable differences are observed between the two strategies in the deceleration phase. The engine-state strategy is less sensitive to downshift, taking even unnecessary upshift decisions. The state intention strategy, however, interprets the driver's intention correctly for decreasing speed and utilizes engine brake torque to reduce the vehicle speed in a shorter time.

Engagement Control of Automated Clutch for Vehicle Launching Considering the Instantaneous Changes of Driver's Intention

2016 ◽  
Vol 139 (2) ◽  
Author(s):  
Liang Li ◽  
Zaobei Zhu ◽  
Yong Chen ◽  
Kai He ◽  
Xujian Li ◽  
...  
Keyword(s):  
Control Strategy ◽  
Control Method ◽  
Sliding Mode ◽  
Linear Quadratic Regulator ◽  
Vehicle Speed ◽  
Linear Quadratic ◽  
Clutch Engagement ◽  
Automated Manual Transmission ◽  
Driver’S Intention ◽  
Artificial Neural Network Ann

Engagement control of automated clutch is essential during launching process for a vehicle equipped with an automated manual transmission (AMT), and instantaneous changes in the driver's launching intention make it more complicated to control the clutch. This paper studies the identification of the driver's launching intentions, which may change anytime, and proposes a clutch engagement control method for vehicle launching. First, a launching-intention-aware machine (LIAM) based on artificial neural network (ANN) is designed for real-time tracking and identifying the driver's launching intentions. Second, the optimal engagement strategy for different launching intentions is deduced based on the linear quadratic regulator (LQR), which figures out a compromise between friction loss, vehicle shock, engine speed, clutch speed, and desired vehicle speed. Third, the relationship between transmitted torque and clutch position is obtained by experiments, and a sliding-mode controller (SMC) is designed for clutch engagement. Finally, the clutch engagement control strategy is validated by a joint simulation model and an experiment bench. The results show that the control strategy reflects the driver's launching intentions correctly and improves the performance of vehicle launching.

scholarly journals Study of the Intelligent Behavior of a Maximum Photovoltaic Energy Tracking Fuzzy Controller

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3263 ◽  
Author(s):  
Gul Tchoketch Kebir ◽  
Cherif Larbes ◽  
Adrian Ilinca ◽  
Thameur Obeidi ◽  
Selma Tchoketch Kebir
Keyword(s):  
Fuzzy Logic ◽  
Control Method ◽  
Fuzzy Controller ◽  
Power Converter ◽  
Climatic Conditions ◽  
Maximum Power ◽  
Maximum Speed ◽  
Special Choice ◽  
Photovoltaic Generators ◽  
Inputs And Outputs

The Maximum Power Point Tracking (MPPT) strategy is commonly used to maximize the produced power from photovoltaic generators. In this paper, we proposed a control method with a fuzzy logic approach that offers significantly high performance to get a maximum power output tracking, which entails a maximum speed of power achievement, a good stability, and a high robustness. We use a fuzzy controller, which is based on a special choice of a combination of inputs and outputs. The choice of inputs and outputs, as well as fuzzy rules, was based on the principles of mathematical analysis of the derived functions (slope) for the purpose of finding the optimum. Also, we have proved that we can achieve the best results and answers from the system photovoltaic (PV) with the simplest fuzzy model possible by using only 3 sets of linguistic variables to decompose the membership functions of the inputs and outputs of the fuzzy controller. We compare this powerful controller with conventional perturb and observe (P&O) controllers. Then, we make use of a Matlab-Simulink® model to simulate the behavior of the PV generator and power converter, voltage, and current, using both the P&O and our fuzzy logic-based controller. Relative performances are analyzed and compared under different scenarios for fixed or varied climatic conditions.

Heavy-duty truck platooning on hilly terrain highways: Methods for assessment and improvement

2021 ◽  
pp. 095440702110676
Author(s):  
Miles J Droege ◽  
Brady Black ◽  
Shubham Ashta ◽  
John Foster ◽  
Gregory M Shaver ◽  
...  
Keyword(s):  
Control Method ◽  
Control Strategies ◽  
Speed Control ◽  
Vehicle Speed ◽  
Heavy Duty ◽  
Cruise Control ◽  
Hilly Terrain ◽  
Optimal Speed ◽  
Look Ahead ◽  
Shifting Strategy

Platooning heavy-duty trucks is a proven method to reduce fuel consumption on flat ground, but a significant portion of the U.S. highway system covers hilly terrain. The effort described in this paper uses experimentally gathered single truck data from a route with hilly terrain and an experimentally-validated two-truck platoon simulation framework to analyze control methods for effective platooning on hilly terrain. Specifically, this effort investigates two platoon control aspects: (1) the lead truck’s vehicle speed control and (2) the platoon’s transmission shifting algorithm. Three different types of lead truck speed control strategies are analyzed using the validated platoon model. Two are commercially available cruise control strategies – conventional constant set speed cruise control (CCC) and flexible set speed cruise control (FCC). The third lead truck speed control strategy was developed by the authors in this paper. It uses look-ahead grade information for an entire route to create an energy-optimal speed profile for the lead truck which is called long-horizon predictive cruise control (LHPCC). Then, a two-truck platoon transmission shifting strategy that coordinates the shift events – Simultaneous Shifting (SS) – is introduced and compared to a commercially available shifting strategy using the validated platoon model. This shifting strategy demonstrates further improvements in the platoon performance by improving the platoon gap control. A summary of these simulations demonstrates that the performance of the platoon can be improved by three methods: adding speed flexibility to the lead truck speed control method, using look-ahead road grade information to generate energy-optimal speed targets for the lead truck, and coordinating the timing of the transmission shifts for each truck in the platoon.

scholarly journals Integrated Longitudinal and Lateral Networked Control System Design for Vehicle Platooning

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3085 ◽  
Author(s):  
Chedia Latrech ◽  
Ahmed Chaibet ◽  
Moussa Boukhnifer ◽  
Sébastien Glaser
Keyword(s):  
Control Method ◽  
Fuzzy Controller ◽  
Actuator Saturation ◽  
Networked Control System ◽  
Vehicle Speed ◽  
Lateral Control ◽  
String Stability ◽  
The Road ◽  
Wireless Communication Network ◽  
Vehicle Platooning

This paper investigates platoon control of vehicles via the wireless communication network. An integrated longitudinal and lateral control approaches for vehicle platooning within a designated lane is proposed. Firstly, the longitudinal control aims to regulate the speed of the follower vehicle on the leading vehicle while maintaining the inter-distance to the desired value which may be chosen proportional to the vehicle speed. Thus, based on Lyapunov candidate function, sufficient stability conditions formulated in BMIs terms are proposed. For the general objective of string stability and robust platoon control to be achieved simultaneously, the obtained controller is complemented by additional conditions established for guaranteeing string stability. Furthermore, constraints such as actuator saturation, and controller constrained information are also considered in control design. Secondly, a multi-model fuzzy controller is developed to handle the vehicle lateral control. Its objective is to maintain the vehicle within the road through steering. The design conditions are strictly expressed in terms of LMIs which can be efficiently solved with available numerical solvers. The effectiveness of the proposed control method is validated under the CarSim software package.

A novel multi-parameter coordinated shift control strategy for an automated manual transmission based on fuzzy inference

2016 ◽  
Vol 231 (5) ◽  
pp. 684-699 ◽  
Author(s):  
Lipeng Zhang ◽  
Xiaohong Zhang ◽  
Zongqi Han ◽  
Junyun Chen ◽  
Jingchao Liu
Keyword(s):  
Control Strategy ◽  
Dynamic Performance ◽  
Automatic Transmission ◽  
Rate Of Change ◽  
Manual Transmission ◽  
Vehicle Speed ◽  
Shift Control ◽  
Automated Manual Transmission ◽  
Shift Strategy ◽  
Driver’S Intention

For a vehicle equipped with an automatic transmission, the shift control strategy should reflect the driver’s intention in the dynamic performance and the economy performance of the vehicle. However, the driver’s intention is difficult to identify and involve in the shift strategy because of the complexity of driving environments, the diversity of powertrain parameters and the randomness of the driver’s behaviour. Therefore, in this paper, by considering a vehicle equipped with an automated manual transmission as the study object, a novel multi-parameter coordinated shift control strategy is proposed on the basis of identification of the driver’s intention. First, in order to predict the intention of the driver more effectively, the relative opening degree of the accelerator is defined on the basis of the dynamic analysis. Then, the characteristics of the driver’s expected acceleration, which involve the influence of the driving environment, are proposed. They can be classified into five categories, namely stop, deceleration, keep, acceleration and urgent acceleration. Next, a fuzzy control system is designed to identify the driver’s acceleration characteristics in real time. This considers the vehicle speed, the rate of change in the opening degree of the accelerator and the relative opening degree of the accelerator as the inputs and the quantitative intention of the driver as the output. Finally, the novel multi-parameter coordinated shift control strategy is formulated on the basis of the vehicle speed, the opening degree of the accelerator and the quantitative intention of the driver. The designed shift strategy is compared with conventional methods using simulations and is verified by road tests. The results show that the shift control strategy can make the vehicle shift much more effective.

scholarly journals A Heating Controller Designing Based on Living Space Heating Dynamic’s Model Approach in a Smart Building

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 998
Author(s):  
Roozbeh Sadeghian Broujeny ◽  
Kurosh Madani ◽  
Abdennasser Chebira ◽  
Veronique Amarger ◽  
Laurent Hurtard
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Control Strategies ◽  
Time Series Prediction ◽  
Adaptive Controller ◽  
Inference System ◽  
Diverse Range ◽  
Smart Building ◽  
Living Space ◽  
Soft Computing Techniques

Most already advanced developed heating control systems remain either in a prototype state (because of their relatively complex implementation requirements) or require very specific technologies not implementable in most existing buildings. On the other hand, the above-mentioned analysis has also pointed out that most smart building energy management systems deploy quite very basic heating control strategies limited to quite simplistic predesigned use-case scenarios. In the present paper, we propose a heating control strategy taking advantage of the overall identification of the living space by taking advantage of the consideration of the living space users’ presence as additional thermal sources. To handle this, an adaptive controller for the operation of heating transmitters on the basis of soft computing techniques by taking into account the diverse range of occupants in the heating chain is introduced. The strategy of the controller is constructed on a basis of the modeling heating dynamics of living spaces by considering occupants as an additional heating source. The proposed approach for modeling the heating dynamics of living spaces is on the basis of time series prediction by a multilayer perceptron neural network, and the controlling strategy regarding the heating controller takes advantage of a Fuzzy Inference System with the Takagi-Sugeno model. The proposed approach has been implemented for facing the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil, taking into account the occupants of spaces in the control chain. The obtained results assessing the efficiency and adaptive functionality of the investigated fuzzy controller designed model-based approach are reported and discussed.

scholarly journals An Adaptive Neuro-Fuzzy Control of Pneumatic Mechanical Ventilator

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 51
Author(s):  
Jozef Živčák ◽  
Michal Kelemen ◽  
Ivan Virgala ◽  
Peter Marcinko ◽  
Peter Tuleja ◽  
...  
Keyword(s):  
Control System ◽  
Control Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Mechanical Design ◽  
Mechanical Ventilator ◽  
Mechanical Ventilators ◽  
Inference System ◽  
Control Approach ◽  
Neuro Fuzzy

COVID-19 was first identified in December 2019 in Wuhan, China. It mainly affects the respiratory system and can lead to the death of the patient. The motivation for this study was the current pandemic situation and general deficiency of emergency mechanical ventilators. The paper presents the development of a mechanical ventilator and its control algorithm. The main feature of the developed mechanical ventilator is AmbuBag compressed by a pneumatic actuator. The control algorithm is based on an adaptive neuro-fuzzy inference system (ANFIS), which integrates both neural networks and fuzzy logic principles. Mechanical design and hardware design are presented in the paper. Subsequently, there is a description of the process of data collecting and training of the fuzzy controller. The paper also presents a simulation model for verification of the designed control approach. The experimental results provide the verification of the designed control system. The novelty of the paper is, on the one hand, an implementation of the ANFIS controller for AmbuBag pressure control, with a description of training process. On other hand, the paper presents a novel design of a mechanical ventilator, with a detailed description of the hardware and control system. The last contribution of the paper lies in the mathematical and experimental description of AmbuBag for ventilation purposes.

A new meta-heuristic optimization algorithm based on a paradigm from physics: string theory

2021 ◽  
Vol 41 (1) ◽  
pp. 1657-1675
Author(s):  
Luis Rodriguez ◽  
Oscar Castillo ◽  
Mario Garcia ◽  
Jose Soria
Keyword(s):  
String Theory ◽  
Optimization Algorithm ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Optimization Problems ◽  
Flower Pollination Algorithm ◽  
Grey Wolf Optimizer ◽  
Mathematical Functions ◽  
Inference System ◽  
First Case

The main goal of this paper is to outline a new optimization algorithm based on String Theory, which is a relative new area of physics. The String Theory Algorithm (STA) is a nature-inspired meta-heuristic, which is based on studies about a theory stating that all the elemental particles that exist in the universe are strings, and the vibrations of these strings create all particles existing today. The newly proposed algorithm uses equations based on the laws of physics that are stated in String Theory. The main contribution in this proposed method is the new techniques that are devised in order to generate potential solutions in optimization problems, and we are presenting a detailed explanation and the equations involved in the new algorithm in order to solve optimization problems. In this case, we evaluate this new proposed meta-heuristic with three cases. The first case is of 13 traditional benchmark mathematical functions and a comparison with three different meta-heuristics is presented. The three algorithms are: Flower Pollination Algorithm (FPA), Firefly Algorithm (FA) and Grey Wolf Optimizer (GWO). The second case is the optimization of benchmark functions of the CEC 2015 Competition and we are also presenting a statistical comparison of these results with respect to FA and GWO. In addition, we are presenting a third case, which is the optimization of a fuzzy inference system (FIS), specifically finding the optimal design of a fuzzy controller, where the main goal is to optimize the membership functions of the FIS. It is important to mention that we used these study cases in order to analyze the proposed meta-heuristic with: basic problems, complex problems and control problems. Finally, we present the performance, results and conclusions of the new proposed meta-heuristic.

scholarly journals Robust control method for bionic gait of machine legs based on time delay feedback

2018 ◽  
Vol 173 ◽  
pp. 02009
Author(s):  
Lu Xing-Hua ◽  
Huang Peng-Fen ◽  
Huang Wei-Peng
Keyword(s):  
Time Delay ◽  
Robust Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Delayed Feedback Control ◽  
Accurate Estimation ◽  
Gait Control ◽  
Robot Leg ◽  
Correlation Parameters ◽  
Robotic Leg

The bionic machine leg is disturbed by the joint during the walking process, which is easy to produce time delay, which causes the robustness of the control of the machine leg is not good. In order to improve the robustness of the bionic gait control of the machine leg, a robust control method for the bionic gait of the machine leg based on time - delay feedback is proposed. The gait correlation parameters of robot leg are collected by sensor array, and the dynamic model of bionic gait is constructed. The fuzzy controller of bionic gait of robot leg is constructed by using time-delay coupling control method. The delayed feedback control error compensation method of machine leg correction is taken to improve the steady control performance of the robotic leg, reduce the steady-state error, improve the robustness of the control machine leg. The simulation results show that this method is robust to the bionic gait control of the machine leg. The output error of the gait parameter can quickly converge to zero, and the accurate estimation of the attitude parameter is stronger.

scholarly journals Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4181 ◽  
Author(s):  
Chun-Hui Lin ◽  
Shyh-Hau Wang ◽  
Cheng-Jian Lin
Keyword(s):  
Mobile Robots ◽  
Control Method ◽  
Fuzzy Controller ◽  
Bee Colony ◽  
Load Carrying ◽  
Navigation Control ◽  
Interval Type ◽  
Navigation Method ◽  
Neural Fuzzy

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

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