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Decision and planning are key technologies for achieving autonomous driving. In response to the issue of considering the safety of the target lane and planning the optimal obstacle avoidance trajectory for autonomous trucks during emergency turning and obstacle avoidance, a fuzzy relationship was established based on the difference between the relative distance between vehicles on the left and right lanes during lane changing and the minimum safe distance between the vehicles during lane changing. The safety values designed by fuzzy rule reasoning were compared, a safer lane for turning was selected to avoid obstacles. In order to quickly plan the optimal obstacle avoidance trajectory, a third-order Bézier curve was used to form an obstacle avoidance curve by designing the coordinates of four control points. In order to prevent the truck from rollover and collision with the vehicle in front due to excessive lateral acceleration during turning, the stability boundary and collision boundary of the vehicle were designed to constrain the control points, the functions in MATLAB was used to solve the optimal lane changing trajectory at different speeds, and finally simulation software was used for simulation verification. The results show that the designed obstacle avoidance decision and trajectory planning can safely and effectively avoid obstacles. [ABSTRACT FROM AUTHOR]

ObjectiveIn order to solve the problem of an underactuated ship responding slowly to heading changes during rudder roll stabilization (RRS) caused by fixed weight values in model predictive control (MPC), a RRS control method based on the finite time extended states observer (FTESO), fuzzy rules and robust predictive control is proposed. MethodsA fixed speed linear underactuated ship model is established for controller design. The FTESO is used to estimate the ship's motion states and external disturbances. By analyzing the conditions of the ship's course-keeping and heading change, the objective function weights under the two conditions and the fuzzy rules between the states and weights are designed respectively. Robust predictive control is used to solve the multi-objective cooperative control problem with constraints. The closed-loop stability of the proposed control method is then proven theoretically. ResultsAccording to a numerical simulation of a multi-purpose naval vessel, the proposed control method is compared with a disturbance compensation MPC and disturbance observer enhanced MPC, and is shown to have a higher roll stabilization rate by 5.74% and 0.898 3%, respectively. The response time of the proposed method for a 30° heading change is also reduced by 1.8 s and 7.3 s respectively. ConclusionThe effectiveness of the proposed method in underactuated ship rolling reduction is proven.

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The traditional PID algorithm used in the currently existing automatic train operation system is limited due to fixed parameters, making it difficult to achieve excellent control effects in actual operation scenes featuring strong coupling and high nonlinearity, which is mainly attributed to difficulties in overcoming nonlinear disturbances.In light of this, this paper proposes an automatic train operation approach that relies on a fuzzy adaptive PID algorithm, which can adjust the PID parameters in real time according to the preset fuzzy rules, thus improving the PID controller's performance in speed tracking and leading to an improved train control effect. Based on the data from Changsha Rail Transit Line 4, the semi-physical simulation results show that the proposed algorithm resulted in the average root-mean-square error (RMSE) of 18.876 cm/s between the actual train speed and the recommended speed, which is less than the 35.200 cm/s of the traditional PID controller and the value of fitting degree is 1.69 m/s between travel speed and the recommended speed, which is less than the 2.25 m/s of the traditional PID controller, suggesting that the proposed algorithm offers a more effective solution for tracking the recommended speed curves and improving system operation efficiency.

Copyright of Journal of Beijing University of Technology is the property of Journal of Beijing University of Technology, Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to alleviate the environmental pollution caused by ship transportation, an energy management strategy of parallel ship power system based on improved fuzzy logic control is proposed for the parallel hybrid power system with diesel engines as the main propulsion device. Based on the fuzzy logic control strategy, the error between required power and output power is used as a modification item and the sum of modification item, and demand power is taken as the input of the fuzzy logic control. By changing the fuzzy rules of excitation, the output power of the diesel engine and the output power of the battery pack are redistributed, so that the error between the total output power and the actual demand power is reduced. Energy management decisions can be made easily and quickly under different conditions by using this method which provides a new solution for energy management of high-power ship power systems.

At present, the local ventilator control mainly uses traditional PID controllers. The parameters of the controller are fixed and selected based on experience. It is difficult to achieve the optimal combination of membership functions and fuzzy rules, which makes it difficult for the controller to meet the adaptive control requirements of local ventilator. In order to solve this problem, a fuzzy PID controller for mine local ventilator optimized by improved genetic algorithm is designed. In the improved genetic algorithm, the Euclidean distance is introduced to increase the diversity of the population, and the adaptive crossover and mutation probability are introduced to improve the convergence of the algorithm. In the encoding process, the proportional fraction is used to indirectly optimize the membership functions, which enables the improved genetic algorithm to optimize both the membership functions and fuzzy rules. The optimized membership functions and fuzzy rules are imported into the fuzzy PID controller. The controller can adaptively adjust the air volume of the local ventilator through the inverter according to the different working status of the local ventilator so as to realize the dynamic adjustment of the local ventilator. The simulation results show that compared with the traditional fuzzy PID controller, the improved fuzzy PID controller can basically achieve no overshoot, the rise time is shortened by 56.25%, and the stabilization time is shortened by 47.06%, which can better meet the control requirements of mine local ventilator.

Aiming at the problems of the high frequency chattering and poor robustness in the speed estimation strategy of the traditional super twisting sliding mode observer(STSMO), an improved fuzzy super twisting sliding mode observer for speed observation of permanent magnet synchronous motor was proposed. Firstly, a normalized feedforward phase-locked loop was used to extract the rotor position and speed to avoid the accumulation of observation errors when the given speed is ramp. Secondly, the super twisting sliding mode observer and fuzzy controller were combined, and the fuzzy rules were introduced to adjust the sliding mode gain online, so as to improve the observation accuracy and robustness of the system and suppress the high frequency chattering. Finally, MATLAB/ Simulink simulation software was used to verify the proposed strategy, and the simulation results show that compared with the traditional super twisting sliding mode observer, the improved fuzzy super twisting sliding mode observer has higher observation accuracy and chattering phenomenon is suppressed. When subjected to external load disturbance or internal parameter perturbation, the system has strong robustness. [ABSTRACT FROM AUTHOR]

The problem of strictly asynchronous dissipative control for a class of continuous time nonlinear generalized Markov jump systems under Takagi-Sugeno fuzzy rules is studied. Firstly, by constructing a less conservative mode independent Lyapunov function, the sufficient conditions for stochastic stability and strict dissipation of generalized system are discussed. Then, the hidden Markov model that is widely used in practice is introduced. By combining the state transition probability with the conditional probability of the hidden Markov process, and through Schur transformation, a fuzzy state feedback controller is designed, which can operate asynchronously with the original system, so as to ensure the stochastic stability and strict dissipation of the closed-loop system. Finally, numerical simulation using Matlab linear matrix inequality(LMI) toolbox is applied to verify the effectiveness of the conclusion. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Acta Automatica Sinica is the property of Chinese Academy of Sciences, Institute of Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many diagnosis expert systems for equine had been developed in the past, but there are no in-depth studies for the equine disease diagnosis expert system. And also most of the traditional expert systems are used by the conventional inference approach. The domain expert confidence factor (CF) for each rule of diseases is kept unchanged with its original value in conventional inference approach. So most of the traditional expert systems have a static knowledge base with static inference, and the decision power of these systems remains same through the life cycle of the system. In fact, the progress of equine knowledge requires modification of the knowledge base and rule base in the system. In this paper, we suggested a new approach for providing the intelligence in the system for diagnosis of the equine diseases. This experiment was conducted to develop a remote auxiliary equine diseases diagnosis expert system. By collecting and analyzing the experiences of diagnosis and treatment from experts on equine disease, the numerical expression of the equine diseases diagnosis knowledge was developed. The knowledge of equine diseases was represented with the method called object-attribute-value triples act (referred as O-A-V act) that combined with the generative formula. As such, it was easy to extract knowledge rules and these rules were used for inference mechanism. Using the confidence factor, multi-valued logic was used to represent the rules of confidence level. In this paper, we suggested a new inference method which was based on use of a fuzzy rule promotion theory. This approach can enhance the intelligence of the disease diagnosis system. If a rule was repeatedly used in corrective diagnostic results, it was then promoted to a higher confidence factor by the rule promotion factor (PCF), and the PCF was the original confidence factor in the next diagnosis session. In short, the dynamic PCF which was generated in the past dialogue was used instead of static CF in the final decision making process. The dynamically promoted rules were derived from those diagnosis sessions, which resulted in successful decisions. This enabled more efficient decision making in the future sessions. With this approach, it was not only decreasing the number of interactive between the system and the users, but also leading to acceptable diagnostic results. Based on the research of knowledge representation and inference mechanism, an auxiliary diagnostic expert system of equine diseases based on Microsoft.Net and SQL Server 2008 was designed and developed. It provided online help to equine farmers and extension workers in China. For the inference engine of system, we used the fuzzy rule promotion methodology that matched facts against conditions and determined which rules were applicable. Also, it automatically revised the confidence factor of each rule. The system performance test was conducted by 804 disease test cases. The successful and unsuccessful diagnosis consultation sessions were noted each time. By thirteen iteration tests, it showed that diagnostic accuracy of the system was closed to 92.28%. It proved that the method was a new way to enhance the diagnostic intelligence. The system met basic requirement of users. Suggestion for future improvement is needed to modify the rule promotion theory by minimizing the errors in the estimation of confidence factor and then estimation of the promoted confidence factor of the rule. By constantly maintaining and updating the knowledge base, and enriching the knowledge base, the system can definitely have a wide application in the countrywide. [ABSTRACT FROM AUTHOR]

According to the analysis of such uncertain disturbance, the time for the material shortage to occur, the period for the material shortage to last and the tardiness penalty casued by material shortage were expressed by fuzzy variables. This paper set up a mathematic model of predictive-reactive scheduling based on fuzzy rules and proposed a mechanism to make scheduling decisions based on fuzzy rules. It proposed a predictive-reactive scheduling approach consisting of the offline robustness optimization and the online scheduling revision, in which both the efficiency measure and stability measure were considered. It developed a genetic algorithm in both the processes of predictive scheduling and reactive scheduling. It validates the feasibility of the proposed method by simulation experiments with 3 different sizes of Job-Shop scheduling problems. [ABSTRACT FROM AUTHOR]

In order to avoid local optimum of Mamdani model parameter optimization, a novel algorithm for Mamdani neural network was proposed. The initial parameters of Mamdani Fuzzy Neural Network ( FNN) were generated by Fuzzy C-means clustering, based on PSO, and then optimized by using PSO. Finally, Gradient descent method was adopted for further optimizing the parameters so that the fuzzy rules could be automaticaUy adjusted, modified and improved. Numerical experiments show that the presented algorithm improves the approximation ability of Mamdani FNN. [ABSTRACT FROM AUTHOR]

Aimed at the serious problem of phenomenon that there are so many out-of-vocabulary words and translation disorder in Uyghur-Chinese machine translation, and combined the features of Uyghur and the maximum entropy classification algorithm, this paper presented a max entropy reordering model in Uyghur-Chinese machine translation based on tackiness fuzzy rules. The model was based on max entropy classification algorithm, and created the constraints of tackiness fuzzy rules for Uyghur in the word level, then extracted better effective reordering rules from training corpus to guide the translation decoding procedure. The experiments show that compared with the current reordering methods, such as MSD (mono, swap, discontinuous) and so on, this reordering method is better retained the tackiness feature of Uyghur, and the quality of the translation is also improved. [ABSTRACT FROM AUTHOR]

Complex context of emergencies, involving natural, cultural, organizational, resource and other factors, is fundamental to identify the activities and scenarios. Based on analysis of relations between event context and scenario changing, elements of context are defined in perspective of structure and transformation. Research ideas are described from cognition of context elements to the description of context, and to building fuzzy rules of scenario reasoning. For context elements, a fuzzy rule for scenario changing in multi-context is designed, and a method based on fuzzy rules reasoning for emergency scenario planning under situation of incomplete information is proposed. [ABSTRACT FROM AUTHOR]

Aiming at the premature convergence problem in traditional intelligent optimization algorithm, this paper proposed a fuzzy artificial bees colony algorithm, it based on the principles of fuzzy processing and bees colony behavior. It introduced fuzzy inputs and fuzzy outputs into the algorithm to maintain dynamic updates of the nectar access probability. According to effective adjustment on nectar access probability during the different stages of algorithm calculation, the algorithm avoided local optima. Simulation tests of traveling salesman problem and comparisons with other algorithms show the performance of proposed algorithm. The computational results prove the algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]