A Novel Adaptive Fuzzy MPPT Algorithm under Changing Atmospheric Conditions

As a kind of mobile robot, the intelligent trolley is also called a wheeled robot, and this field is the fastest growing in the current technological development. Smart trolleys have good automatic guidance functions, often used in modern logistics conveying systems. A fuzzy control algorithm has a good control effect for nonlinear systems and has a strong anti-interference ability. In order to optimize the multi-objective longitudinal following system of smart carts, therefore, this paper develops the study of fuzzy PID control algorithm for multi-objective longitudinal following of smart carts. Based on the incremental PID control, an improved PID algorithm is constructed by introducing incomplete differentiation, differential prior, and “optimal curvature” algorithm. Based on the actual control experience, the fuzzy control table is designed, and the adaptive control algorithm is obtained. Finally, the simulation results of the fuzzy PID control algorithm are tested by MATLAB/Simulink, and the most suitable fuzzy PID control algorithm for multi-objective optimal longitudinal following of intelligent carts is derived.

Solving the path-tracking problem of vehicles is a key part of realizing intelligent transportation. The four-wheel- independent-steering (4WIS) electric vehicle (EV) with steer-by-wire (SBW) has the advantages of good controllability, low speed maneuverability and high speed stability, which is beneficial to improving the path tracking performance. In this paper, a novel 4WIS electric vehicle is proposed and the path tracking control research was carried out on it by using the adaptive fuzzy PID algorithm. Firstly, the four-wheel steering strategy was analyzed, and the geometric relationship between the front and rear steering angle when the vehicle's centroid angle is zero was derived. Furthermore, based on the principle of adaptive fuzzy PID control and the single-step heading prediction algorithm, a path-tracking controller was designed for the self-built 4WIS EV. A Simulink/Carsim co-simulation platform was built, on which the simulation was carried out at different condition, and the simulation results show that the designed adaptive fuzzy PID controller has good path-tracking performance and strong robustness. Finally, the serpentine trajectory and the double lane change (DLC) trajectory are taken as the reference trajectory, and the reliability and robustness of the designed adaptive fuzzy PID control algorithm were verified on the open road. The results show that the adaptive fuzzy PID control algorithm has good tracking performance and is far superior to pure PID control.

With the development of wind power technology in recent years, wind power technology has made rapid development. In this paper, the wind farm is the main object of study, the energy storage elements is vanadium redox flow battery (VRB), the use of decoupling control of fuzzy PID control algorithm for real-time changes in PID parameters of the active and reactive power. The outer loop of motor side rectifier is power loop, the inner is current loop. The outer loop of grid side inverter is the voltage loop, the inner is current loop, and based on vector control, the rectifier and inverter achieve decoupling process.The traditional PID is poor in anti-interference ability and robustness,so this paper uses the fuzzy PID control algorithm. The MATLAB/simulation results show that the fuzzy control algorithm can achieve power tracking control,and has strong robustness.

An efficient fuzzy logic control algorithm for photovoltaic maximum power point tracking under partial shading condition

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

When flexible risers in the ocean are subjected to various environmental loads in the ocean, vibration will inevitably occur. Due to the nonlinearity and uncertainty of the actual riser system, it is impossible to obtain an accurate mathematical model of the riser system. The existing control algorithms designed based on the precise model of the riser system cannot meet the actual control requirements. A flexible riser boundary control method based on fuzzy control algorithm is proposed to suppress the vibration of marine flexible riser systems with uncertain models. Firstly, the dynamic models of existing marine flexible risers were studied. In response to the uncertainty of the riser system model, the control experience was transformed into an automatic control strategy in the form of fuzzy language control rules to improve the adaptability of the controller. Then, combining boundary control technology with fuzzy control, a boundary controller is constructed to stabilize the riser in a small neighborhood of its original position and reduce fatigue damage to the riser. Finally, the effectiveness of the designed fuzzy boundary control algorithm was verified through MATLAB simulation results, and the vibration suppression effect of the algorithm on the vertical tube was better than that of traditional PD control.

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

In this paper, a nonlinear integrated multi-body dynamic (MBD) model of a deep-sea mining (DSM) system was developed. A particular nonlinear interaction model between the mining vehicle and the seabed sediment was derived. The fluid resistance and the resistance torque acting on the vehicle were obtained by the computational fluid dynamics (CFD) numerical simulations. In addition, a rigid-flexible coupled discrete element method (DEM) was adopted to model an extremely long mining riser efficiently and accurately. Developed by the C# program, a user-defined subprogram was intended to establish the DEM model of the riser. A motion control system model for trajectory tracking of the vehicle was developed in which the fuzzy control algorithm and the fuzzy adaptive PID control algorithm were used to control the speed and speed ratio of the tracks, respectively. Furthermore, the collaborative simulation between the dynamic model and the control model was achieved, and a coordinated motion mode for the total mining system was proposed and simulated. Accordingly, the trajectory tracking accuracy and slip of the vehicle, the spatial state change of the riser, and the interaction forces among subsystems were analyzed. This paper can provide a valuable theoretical basis and technical reference for the integrated design, performance prediction, and operation control of the practical DSM system.

The disturbance, multi-parameters, and non-linearities are the major difficulties in the collecting main pressures control system. This paper develops a fuzzy control algorithm for the collecting main pressure control based on expert control, in order to solve the collecting main pressure disturbance problem. The fuzzy algorithm adopts two phase control strategies. One is the fuzzy feed-forward control algorithm for decoupling control. Another is the expert control algorithm for the cooler actuating power control. The experience results show the fuzzy control algorithm using expert control can make collecting main pressure and the cooler actuating power in the requirement range and obtain a well control performance.

In the RNA virus laboratory, to realize the characteristics of small temperature fluctuation and high precision in the process of RNA virus detection, a high-precision temperature control system based on a fuzzy PID temperature control algorithm is designed. The system takes MSP430 as the main controller and controls the temperature through the NTC acquisition circuit and TEC-driven circuit. The system first adopts the incremental PID algorithm for preliminary design, second determines the incremental PID parameters, and then obscures the algorithm on this basis to make it an adaptive fuzzy PID algorithm, which further improves the stability and response speed of the system. Finally, through the physical test, the incremental PID algorithm is compared with the adaptive fuzzy PID algorithm, and the design of a fuzzy PID temperature control system for RNA virus detection is realized. The test results show that the system error of the improved fuzzy PID is less than $0.2^{\circ}\mathrm{C}$, the working time is shortened by nearly 70 seconds, and has the characteristics of high precision, fast response speed, and small temperature fluctuation. Through the PID temperature control system for RNA virus detection designed, by comparison, the system has high precision and low-temperature fluctuation and can be well used in RNA virus laboratories.

The aim of this research is to control the temperature of roasting process to be stable. The temperature controller is needed in process of coffee roasting because of high and low temperature is very influential on the process. This paper presents the development of the coffee roaster machine where the temperature is controlled using fuzzy logic control algorithm. The temperature is sensed using PT100. The fuzzy control algorithm computes the controller output based on error and the rate of error. The fuzzy control algorithm is implemented using MATLAB. A PLC Omron is used and interfaced with PC Desktop, so that a real-time control can be conducted. A HMI is used so that a roaster machine operator can easily operates the machine. A complete PLC system and mechanical design are discussed in this paper. Experiments were conducted to verify the performance of the controller. From the experiment results, the desired temperature can be controlled with relatively small error, 0.52%. It can cause the temperature of the process relatively stable.

In this work, a Fuzzy Logic Control (FLC) based MPPT technique is proposed to improve the performance of a stand-alone solar energy system. The Fuzzy logic controller is used as an intelligent way of tracking the maximum power point (MPP). The Taguchi method is adopted in this study to analyze multiple operating conditions of solar PV array. Solar PV output changes with Atmospheric conditions. The change in PV Current and Power are measured and fed to the Fuzzy logic controller as input. The Fuzzy controller is designed with 25 fuzzy rules and the Mamdani fuzzy inference is performed to obtain the aggregation which will be defuzzified by Center of gravity method. Based on the change in PV Current and Power, the Fuzzy logic controller generate the duty cycle for the boost converter (DC-DC converter). The variation of the duty cycle is from 0 to 1. The signal of change in duty ratio from the Fuzzy logic MPPT algorithm is fed to the PWM for switching the IGBT to dynamically update the duty cycle of the boost converter for extracting the maximum power from the solar PV array. A stand-alone Photovoltaic system with a boost converter is simulated in MATLAB Simulink to demonstrate the results and applicability of the proposed method.

In hatching process, temperature, relative humidity and oxygen concentration are three main parameters to the success of the incubator. Incubation system is a multi-variable multi-interference and time delay complex dynamic system, a hybrid intelligent control method is proposed. Based on characteristics of complexity and uncertainty for its mathematical models in the hatching process, this paper will divide the system into two structures, considering of the effect of throttle opening which causes temperature and relative humidity big interference and strong coupling, feed-forward compensation decoupling control algorithm is employed for before-class structure to realize the decoupling of main parameters. After decupling, according to different conditions fuzzy immune PID control algorithm or fuzzy control algorithm is selected for back-class structure to realize incubator intelligent control. Finally, the run of system verifies the effectiveness of the control algorithm.

Development of fuzzy control algorithm for shape control in cold rolling