An intelligent control method based on artificial neural network for numerical flight simulation of the basic finner projectile with pitching maneuver

In this paper,an intelligent control method applying on numerical virtual flight is proposed.The proposed algorithm is verified and evaluated by combining with the case of the basic finner projectile model and shows a good application prospect.Firstly,a numerical virtual flight simulation model based on overlapping dynamic mesh technology is constructed.In order to verify the accuracy of the dynamic grid technology and the calculation of unsteady flow,a numerical simulation of the basic finner projectile without control is carried out.The simulation results are in good agreement with the experiment data which shows that the algorithm used in this paper can also be used in the design and evaluation of the intelligent controller in the numerical virtual flight simulation.Secondly,combined with the real-time control requirements of aerodynamic,attitude and displacement parameters of the projectile during the flight process,the numerical simulations of the basic finner projectile’s pitch channel are carried out under the traditional PID(Proportional-Integral-Derivative)control strategy and the intelligent PID control strategy respectively.The intelligent PID controller based on BP(Back Propagation)neural network can realize online learning and self-optimization of control parameters according to the acquired real-time flight parameters.Compared with the traditional PID controller,the concerned control variable overshoot,rise time,transition time and steady state error and other performance indicators have been greatly improved,and the higher the learning efficiency or the inertia coefficient,the faster the system,the larger the overshoot,and the smaller the stability error.The intelligent control method applying on numerical virtual flight is capable of solving the complicated unsteady motion and flow with the intelligent PID control strategy and has a strong promotion to engineering application.

Intelligent PID Control Method for Quadrotor UAV with Serial Humanoid Intelligence

Quadrotor unmanned aerial vehicles(UAVs)are widely used in inspection,agriculture,express delivery,and other fields owing to their low cost and high flexibility.However,the current UAV control system has shortcomings such as poor control accuracy and weak anti-interference ability to a certain extent.To address the control problem of a four-rotor UAV,we propose a method to enhance the controller’s accuracy by considering underactuated dynamics,nonlinearities,and external disturbances.A mathematical model is constructed based on the flight principles of the quadrotor UAV.We develop a control algorithm that combines humanoid intelligence with a cascade Proportional-Integral-Derivative(PID)approach.This algorithm incorporates the rate of change of the error into the inputs of the cascade PID controller,uses both the error and its rate of change as characteristic variables of the UAV’s control system,and employs a hyperbolic tangent function to improve the outer-loop control.The result is a double closed-loop intelligent PID(DCLIPID)control algorithm.Through MATLAB numerical simulation tests,it is found that the DCLIPID algorithm reduces the rise time by 0.5 s and the number of oscillations by 2 times compared to the string PID algorithm when a unit step signal is used as input.A UAV flight test was designed for comparison with the serial PID algorithm,and it was found that when the UAV planned the trajectory autonomously,the errors in the X-,Y-,and Z-directions were reduced by 0.22,0.21,and 0.31 m,respectively.Under the interference environment of artificial wind about 3.6 m·s-1,the UAV hovering error in X-,Y-,and Z-directions are 0.24,0.42,and 0.27 m,respectively.The simulation and experimental results show that the control method of humanoid intelligence and cascade PID can improve the real-time,control accuracy and anti-interference ability of the UAV,and the method has a certain reference value for the research in the field of UAV control.

Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

An autonomous microgrid that runs on renewable energy sources is presented in this article.It has a supercon-ducting magnetic energy storage(SMES)device,wind energy-producing devices,and an energy storage battery.However,because such microgrids are nonlinear and the energy they create varies with time,controlling and managing the energy inside them is a difficult issue.Fractional-order proportional integral(FOPI)controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance.The suggested dedicated control for the SMES comprises two loops:the outer loop,which uses the FOPI to regulate the DC-link voltage,and the inner loop,responsible for regulating the SMES current,is constructed using the intelligent FOPI(iFOPI).The FOPI+iFOPI parameters are best developed using the dandelion optimizer(DO)approach to achieve the optimum performance.The suggested FOPI+iFOPI controller’s performance is contrasted with a conventional PI controller for variations in wind speed and microgrid load.The optimal FOPI+iFOPI controller manages the voltage and frequency of the load.The behavior of the microgrid as a reaction to step changes in load and wind speed was measured using the proposed controller.MATLAB simulations were used to evaluate the recommended system’s performance.The results of the simulations showed that throughout all interruptions,the recommended microgrid provided the load with AC power with a constant amplitude and frequency.In addition,the required load demand was accurately reduced.Furthermore,the microgrid functioned incredibly well despite SMES and varying wind speeds.Results obtained under identical conditions were compared with and without the best FOPI+iFOPI controller.When utilizing the optimal FOPI+iFOPI controller with SMES,it was found that the microgrid performed better than the microgrid without SMES.

Design and Application of Intelligent Control System for Molten Iron Transportation Based on 5G Technology

Molten transport is an important link in the iron and steel enterprise production,involves many complex factors,artificial management is difficult.Therefore,puts forward a kind of molten iron transport wisdom control system based on 5G technology,which mainly contains the intelligent identification tracking system,equipment status collection information acquisition system,locomotive vehicle terminal system,etc.Combined with the analysis of the actual application situation,the system could integrate all the processes and elements of molten iron produc-tion and transportation,realize the integration of operation and management,and also promote the improvement of the turnover efficiency of molten iron tank,reduce the demand for personnel,and reduce the labor cost.

INTELLIGENT PRECISION CONTROL IN CNC GRINDING

The development of intelligent control techniques provides powerful means for the control of machine tools. In this paper, a intelligent control technique and an algorithm for precision control of CNC grinding of ceramic chips are introduced. In the process of ceramic chip CNC grinding, the dimension of the chips tends to get larger and the dimensional error to exceed the tolerance as the number of the chips increases which are machined on the same part program. There are many factors leading to the occurrence of the error and the law of error variation is very complicated. With the introduced intelligent self learning error compensation technique, the CNC system can improve the control strategy to compensate the error automatically. The simulational result is also given.

Open multilevel hierarchic intelligent control system for eco-industrial system

In allusion to the characteristics of the open complex giant system, an open multilevel hierarchic intelligent control system is established for the eco-industrial system. With the idea of the open engineering system, using the hall for workshop of metasynthetic engineering （HWME）, intelligent control techniques, the expert system and the design of experiments are integrated within the framework of the nonlinear multiobjective decision support system to develop a robust, top-level design specification so as to make the system have the quality of adaptive control, self-organizing, self-learning and robustness. Finally, an illustrative example is given to clarify the effectiveness of the method.

Ensuring Secure Platooning of Constrained Intelligent and Connected Vehicles Against Byzantine Attacks:A Distributed MPC Framework

This study investigates resilient platoon control for constrained intelligent and connected vehicles(ICVs)against F-local Byzantine attacks.We introduce a resilient distributed model-predictive platooning control framework for such ICVs.This framework seamlessly integrates the predesigned optimal control with distributed model predictive control(DMPC)optimization and introduces a unique distributed attack detector to ensure the reliability of the transmitted information among vehicles.Notably,our strategy uses previously broadcasted information and a specialized convex set,termed the“resilience set”,to identify unreliable data.This approach significantly eases graph robustness prerequisites,requiring only an(F+1)-robust graph,in contrast to the established mean sequence reduced algorithms,which require a minimum(2F+1)-robust graph.Additionally,we introduce a verification algorithm to restore trust in vehicles under minor attacks,further reducing communication network robustness.Our analysis demonstrates the recursive feasibility of the DMPC optimization.Furthermore,the proposed method achieves exceptional control performance by minimizing the discrepancies between the DMPC control inputs and predesigned platoon control inputs,while ensuring constraint compliance and cybersecurity.Simulation results verify the effectiveness of our theoretical findings.

INTELLIGENT VIRTUAL CONTROL:MEASURING INSTRUMENT FROM WHOLE TO PART

A new concept called intelligent virtual control (IVC), which can be drivenby measuring functions, is put forward. This small 'intelligent measurement instrument unit (IMIU)',carrying with functions of instrument, consists of different types of intelligent virtualinstrument (IVI) through individual components together as building blocks and can be displayeddirectly on the computer screen. This is a new concept of measuring instrument, and also animportant breakthrough after virtual instrument (VI). Virtual control makes instrument resourcesobtain further exploitation. It brings about a fundamental change to the design and manufacturingmode. The instrument therefore, can not only be produced directly inside a PC, but the product isinvolved in the 'green product' system. So far, all the present digital instruments will grow to bereplaced by intelligent control with green characteristics.

Study on Intelligent Control and 3D Real-time Distributed Animation Simulation for Super-maneuver Attack of the New Generation Fighter

The super-maneuver flight performance has a very high tactical value, and the development of this tactical value has great significance. A discussion is devoted to the study of intelligent control methods and technologies of real-time distributed 3-dimensional animation simulation for the super-maneuverable attack of new generational fighter in this paper. A flight control system of super-maneuver is reconstructed by adopting three layers BP neural networks of number 3, and the fire/flight coupler is designed by introducing a fuzzy control rule whose universe of discourse and gain are regulated adaptively on the line. Furthermore, a new method of real-time distributed 3-dimensional animation simulation is put forward, and a real-time distributed 3-dimensional animation simulation tool platform is constructed in this paper. The simulation result is lifelike, perceivable directly and useful.

Nonlinear Intelligent Flight Control for Quadrotor Unmanned Helicopter

Quadrotor unmanned helicopter is a new popular research platform for unmanned aerial vehicle(UAV),thanks to its simple construction,vertical take-off and landing(VTOL)capability.Here a nonlinear intelligent flight control system is developed for quadrotor unmanned helicopter,including trajectory control loop composed of co-controller and state estimator,and attitude control loop composed of brain emotional learning(BEL)intelligent controller.BEL intelligent controller based on mammalian middle brain is characterized as self-learning capability,model-free and robustness.Simulation results of a small quadrotor unmanned helicopter show that the BEL intelligent controller-based flight control system has faster dynamical responses with higher precision than the traditional controller-based system.

Mathematical Model and Its Hybrid Dynamic Mechanism in Intelligent Control of Ironmaking

A hybrid dynamic model was proposed, which considered both the hydrokinetic and the chaotic properties of the blast furnace ironmaking process; and great emphasis was put on its mechanism. The new model took the high complexity of the blast furnace as well as the effects of main parameters of the model into account, and the predicted results were in very good agreement with actual data.

RESEARCH ON MECHANICAL MEASUREMENT-ORIENTED INTELLIGENT VIRTUAL CONTROLS

Intelligent virtual control (IVC) is an intelligent measurement instrumentunit with the function of actual measurement instruments, and the unit can be used as basic buildingblock for a variety of more complex virtual measurement instruments on a PC. IVC is a furtheradvancement from virtual instrument (VI), and it fuses the function modules and the controls modulesso that the relationship between the functions and controls of an instrument is imbedded in one ormore units. The design, implementation and optimization methods of IVCs are introduced. The computersoftware representation of IVCs is discussed. An example of an actual VI constructed with thebuilding blocks of IVCs is given.

Cloud Control System Architectures,Technologies and Applications on Intelligent and Connected Vehicles:a Review

The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.

HUMAN-IMITATING INTELLIGENT CONTROL AND TRIPLE INVERTED-PENDULUM

A 'Human-Imitating Intelligent Control Theory' with 'generalized reduction' and 'Human Imitating' concepts as its kernel is proposed. And a world puzzlein the control circles is solved successfully based on this theory. The puzzle is thewell-known 'triple inverted pendulum control' using a SINGLE motor. A human-imitating intelligent technique to control inverted pendulum is here described. The success. ful experimental results show that our control objective can be achieved without a precise mathematical model of the plant. Finally, general principles of designing complexautomatic control systems based on the human-imitating intelligent control theory areconcluded.

Intelligent PID guidance control for AUV path tracking

Based on rational behavior model of three layers, a tracking control system is designed for straight line tracking which is commonly used in underwater survey missions. An intelligent PID control law implemented as planning level during the control system using transverse deviation is came up with. Continuous tracking of path expressed by a point sequence can be realized by the law. Firstly, a path tracking control system based on rational behavior model of three layers is designed, mainly satisfying the needs of underactuated AUV. Since there is no need to perform spatially coupled maneuvers, the 3D path tracking control is decoupled into planar 2D path tracking and depth or height tracking separately. Secondly, planar path tracking controller is introduced. For the reason that more attention is paid to comparing with vertical position control, transverse deviation in analytical form is derived. According to the Lyapunov direct theory, control law is designed using discrete PID algorithm whose parameters obey adaptive fuzzy adjustment. Reference heading angle is given as an output of the guidance controller conducted by lateral deviation together with its derivative. For the purpose of improving control quality and facilitating parameter modifying, data normalize modules based on Sigmoid function are applied to input-output data manipulation. Lastly, a sequence of experiments was carried out successfully, including tests in Longfeng lake and at the Yellow sea. In most challenging sea conditions, tracking errors of straight line are below 2 m in general. The results show that AUV is able to compensate the disturbance brought by sea current. The provided test results demonstrate that the designed guidance controller guarantees stably and accurately straight route tracking. Besides, the proposed control system is accessible for continuous comb-shaped path tracking in region searching.

Fuzzy-GA PID controller with incomplete derivation and its application to intelligent bionic artificial leg

An optimal PID controller with incomplete derivation is proposed based on fuzzy inference and the geneticalgorithm, which is called the fuzzy-GA PID controller with incomplete derivation. It consists of the off-line part andthe on-line part. In the off-line part, by taking the overshoot, rise time, and settling time of system unit step re-sponse as the performance indexes and by using the genetic algorithm, a group of optimal PID parameters K*p , Ti* ,and Tj are obtained, which are used as the initial values for the on-line tuning of PID parameters. In the on-linepart, based on K; , Ti* , and T*d and according to the current system error e and its time derivative, a dedicatedprogram is written, which is used to optimize and adjust the PID parameters on line through a fuzzy inference mech-anism to ensure that the system response has optimal dynamic and steady-state performance. The controller has beenused to control the D. C. motor of the intelligent bionic artificial leg designed by the authors. The result of computersimulation shows that this kind of optimal PID controller has excellent control performance and robust performance.

Control for Intelligent Manufacturing： A Multiscale Challenge

The Made in China 2025 initiative will require full automation in all sectors, from customers to production. This will result in great challenges to manufacturing systems in all sectors. In the future of manufacturing, all devices and systems should have sensing and basic intelligence capabilities for control and adaptation. In this study, after discussing multiscale dynamics of the modern manufacturing system, a five-layer functional structure is proposed for uncertainties processing. Multiscale dynamics include： multi-time scale, spacetime scale, and multi-level dynamics. Control action will differ at different scales, with more design being required at both fast and slow time scales. More quantitative action is required in low-level operations, while more qualitative action is needed regarding high-level supervision. Intelligent manufacturing systems should have the capabilities of flexibility, adaptability, and intelligence. These capabilities will require the control action to be distributed and integrated with different approaches, including smart sensing, optimal design, and intelligent learning. Finally, a typical jet dispensing system is taken as a real-world example for multiscale modeling and control.

Vision based intelligent traffic light management system using Faster R‐CNN

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

Intelligent casting:Empowering the future foundry industry

Emerging technological advances are reshaping the casting sector in latest decades.Casting technology is evolving towards intelligent casting paradigm that involves automation,greenization and intelligentization,which attracts more and more attention from the academic and industry communities.In this paper,the main features of casting technology were briefly summarized and forecasted,and the recent developments of key technologies and the innovative efforts made in promoting intelligent casting process were discussed.Moreover,the technical visions of intelligent casting process were also put forward.The key technologies for intelligent casting process comprise 3D printing technologies,intelligent mold technologies and intelligent process control technologies.In future,the intelligent mold that derived from mold with sensors,control devices and actuators will probably incorporate the Internet of Things,online inspection,embedded simulation,decision-making and control system,and other technologies to form intelligent cyber-physical casting system,which may pave the way to realize intelligent casting.It is promising that the intelligent casting process will eventually achieve the goal of real-time process optimization and full-scale control,with the defects,microstructure,performance,and service life of the fabricated castings can be accurately predicted and tailored.

Mechanoluminescent-Triboelectric Bimodal Sensors for Self-Powered Sensing and Intelligent Control

Self-powered flexible devices with skin-like multiple sensing ability have attracted great attentions due to their broad applications in the Internet of Things(IoT).Various methods have been proposed to enhance mechano-optic or electric performance of the flexible devices;however,it remains challenging to realize the display and accurate recognition of motion trajectories for intelligent control.Here,we present a fully self-powered mechanoluminescent-triboelectric bimodal sensor based on micronanostructured mechanoluminescent elastomer,which can patterned-display the force trajectories.The deformable liquid metals used as stretchable electrode make the stress transfer stable through overall device to achieve outstanding mechanoluminescence(with a gray value of 107 under a stimulus force as low as 0.3 N and more than 2000 cycles reproducibility).Moreover,a microstructured surface is constructed which endows the resulted composite with significantly improved triboelectric performances(voltage increases from 8 to 24 V).Based on the excellent bimodal sensing performances and durability of the obtained composite,a highly reliable intelligent control system by machine learning has been developed for controlling trolley,providing an approach for advanced visual interaction devices and smart wearable electronics in the future IoT era.