Research on a Fuzzy Adaptive PID Control Method for Four Rotor UAV Control System

This paper investigates the fuzzy control method for the linear switched reluctance motor (LSRM) drive system. Modeling of the LSRM is constructed based on the finite element method (FEM) environment Maxwell and the control system is analyzed by the co-simulation environment with Simplorer. Both the performance of the fuzzy proportional integral differential (PID) controller and the traditional PID controller are compared. The simulation results prove that the fuzzy PID controller yields faster rising time and smaller overshoot, compared with the traditional PID controller.

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

This paper investigates the fuzzy Proportion Integration Differentiation (PID) controller for uncertain networked control systems (UNCSs). By combining T-S fuzzy model with the fuzzy PID controller, the uncertain networked control systems under transmission delays and external disturbances are proposed in this letter. Moreover, the asymptotically stability condition about the uncertain networked control systems is presented in the form of Lyapunov function and linear matrix inequality. The applicability of the proposed PID controller gain for uncertain networked control system is numerically tasted for various system parameters.

The turret system is a precision mechanical system capable of carrying mission equipment to move in both azimuth and elevation degrees of freedom. The servo control of the turret usually adopts proportion-integration-differentiation (PID) control, However, when the turret perform target tracking or shooting tasks, it faces various impact responses such as vibration impact, center of mass change, elastic deformation, etc. The PID control method with fixed parameters cannot handle these unknown or unpredictable effects well. So, the combination of PID control and fuzzy reasoning decision is proposed to establish the dynamic fuzzy adaptive adjustment rules of PID parameters and build an adaptive fuzzy PID (FPID) controller to suppress external interference. The turret servo control models are given out first, then the fuzzy adjustment rules for PID parameters based on tracking error and error change rate are provided. Finally, to verify the superiority of the FPID algorithm over the PID algorithm, a comparative analysis was conducted on the target single maneuver with and without considering external influences. The simulation results show that the FPID can suppress external interference and FPID has higher accuracy than PID, especially in the case of target maneuvering and external response, its tracking error can be reduced by about 80%. This method can be used for the servo control of the turret system to improve the servo accuracy of the turret during the target tracking process.

Humidity sensors are prone to condensation in high humidity and low temperature environments, reducing the sensor's measurement performance. Aiming at the current humidity sensor heating system that may not reach the target temperature due to open-loop control and high power consumption, a closed-loop control heating system for the humidity sensor is designed to perform real-time heating and temperature detection of the humidity-sensitive structure. The finite element analysis method is used to conduct thermal analysis on the heating structure, which obtains the temperature distribution of the sensor heating area, guides the heater structure design and gains the location distribution of temperature sensors. A variable parameter fuzzy PID (Proportion Integration Differentiation) control method with variable target temperature is proposed to perform closed-loop control on the temperature of the sensor’s sensitive structure. The heating target temperature is set as a function of the ambient temperature. Also the coefficient of PID changes with error. Thus, the power consumption of the system has been effectively reduced and the heating speed is apparently increased. Simulation experiments show that the heating system designed for controllable heating humidity sensor has uniform temperature distribution. When the ambient temperature is −70°C, the difference between upper and lower temperatures is only 0. 0878°C, with a low power consumption (5 × 106W/m3). At the same time, the overregulation of the temperature control system is low, which is 7. 227%. The system has high environmental adaptability and temperature stability.

To achieve a better robustness and get a static response of a certain target missiles in longitudinal control, a mathematical model of target missiles is established. The uncontrolled trajectory, the predetermined trajectory and the requirements of the predetermined trajectory are analyzed next. Then the trajectory instruction angle is designed to ensure the stable flight of the target missiles system. To solve the problem that the PID controller cannot adjust parameters online and the output is easy to be disturbed by external noise, the fuzzy adaptive PID control is designed. It enhanced the robustness of the target missiles by fuzzy control, and also has a good response by using PID control. The simulation experiments show the fuzzy adaptive control system can track the predetermined trajectory rapidly and smoothly, and the static error and overshoot were significantly reduced. When the system are affected by noise, the fuzzy adaptive PID control can achieve a good robustness than traditional PID controller.

This paper presents an optimal tuning of a proportional integral differential (PID) controller used to maintain glucose concentration at a desired set point. The PID controller synthesizes an appropriate feed rate profile for an E. coli fed-batch cultivation process. Mathematical models are developed based on dynamic mass balance equations for biomass, substrate, and product concentration of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation for bacterial phytase extracellular production. For model parameter identification and PID tuning, a hybrid metaheuristic technique—chaotic electromagnetic field optimization (CEFO)—is proposed. In the hybridization, a chaotic map is used for the generation of a new electromagnetic particle instead of the electromagnetic field optimization (EFO) search strategy. The CEFO combines the exploitation capability of the EFO algorithm and the exploration power of ten different chaotic maps. The comparison of the results with classical EFO shows the superior behaviour of the designed CEFO. An improvement of 30% of the objective function is achieved by applying CEFO. Based on the obtained mathematical models, 10 PID controllers are tuned. The simulation experiments show that the designed controllers are robust, resulting in a good control system performance. The closed-loop transient responses for the corresponding controllers are similar to the estimated models. The settling time of the control system based on the third PID controller for all estimated models is approximately 9 min and the overshoot is approximately 15%. The proposed CEFO algorithm can be considered an effective methodology for mathematical modelling and achievement of high quality and better performance of the designed closed-loop system for cultivation processes.

For the trajectory tracking problem of a single-particle train dynamics model under the condition of partial loss of power in the train power unit due to actuator failure during the operation of the EMU, considering the situation where the system exists with uncertain dynamics from the model and unknown external disturbers, a control method based on fuzzy adaptive fault-tolerant control method for EMU is proposed. Under the framework of the backstepping method, the fuzzy system is used to approach the system function, and a fuzzy adaptive controller is designed. It is proved that the designed control law can enable the train system to track the given speed and displacement trajectory under partial power loss. Using the CRH3 train as the research object, the results show that the designed controller can complete the trajectory tracking control task.

This paper presents a method for fuzzy model reference adaptive control system design for a partially known multi-input multi-output (MIMO) linear dynamic plant. Differential or convolutional equations can describe the plant to be controlled, and the fuzzy controller is assumed to be from the MIMO sector-bounded nonstationary nonlinearities class. The task is to obtain robust adaptive stabilization. Some absolute stability theorems and integral evaluations of the state and control vectors are applied in the MIMO fuzzy adaptive controller design procedure for the first time. A method for automatically obtaining a near-optimal nonlinear dynamic reference model is described. A practical step-by-step procedure of the MIMO adaptive fuzzy controller design is proposed, resulting in highly interpretable MIMO fuzzy control rules based on triangular fuzzy sets and strong triangular fuzzy partition. An example of the fuzzy controller design according to the proposed procedure is given.

In this article, considering uncertainty and nonlinearity factors, the resonance phenomenon of conveyance is studied and a fuzzy adaptive back-stepping controller is proposed for rope lifting system with time-varying length and distributed parameters. Firstly, the dynamic model is constructed and derived by continuum mechanics, and the dynamical behavior is obtained by time-frequency analysis method. Secondly, the fuzzy adaptive back-stepping control method is proposed to suppress nonstationary vibration. Thirdly, based on Lyapunov function, the asymptotic stability of the system under the proposed control algorithm is verified. Finally, compared with proportion–integration–differentiation (PID) back-stepping controller, the efficiency of the proposed controller is proven by applying excitation with different frequency and uncertain parameters, and the adaptability and robustness are illustrated to validate under mixed frequency disturbance by simulation compared with PID back-stepping controller. Based on the dynamical behavior analysis and control method proposed in this article, the robustness of vibration controller of time-varying lifting system under uncertain nonlinear time-varying disturbance will be greatly improved.

Brushless direct current (BLDC) motor has been widely used in industrial production. Based on the method of model design, BLDC motor speed control is studied. In Matlab/Simulink, a fuzzy PID speed control model of BLDC motor is established, and C Code is generated by Real-Time Workshop (RTW) technology. After that, Code Warrior is compiled into machine Code and written into ECU. The results show that the fuzzy Proportion Integration Differentiation (PID) controller can improve the speed performance of the BLDC.

In this research, a fuzzy adaptive PD control approach is introduced for managing the coupled indoor temperature and humidity system. Initially, the mathematical framework of indoor temperature and humidity is analyzed. For the coupled system, a decoupling control strategy is introduced, which eliminates the mutual influence between temperature and humidity by incorporating decoupling components, thereby enabling the system to be equivalently controlled as two independent single-input single-output systems. Secondly, a fuzzy adaptive PD control method is employed to achieve parameter self-tuning. Finally, to assess the efficacy of the introduced control approach, the present study undertakes a simulation analysis, utilizing a medium-sized office as a case study. The outcomes of the simulation suggest that upon implementing the fuzzy adaptive PD control methodology, the modulation of indoor temperature and humidity achieves enhanced precision and swiftness, notably demonstrating greater robustness in the presence of disturbance signals.

사람이 생활하는 환경에서 일반적인 휠베이스 이동(Mobility) 방식의 로봇은 장애물에 둘러싸여 로봇의 움직임에 있어 자유로운 주행 제약을 받게 된다. 장애물을 신속하게 회피하려면 회전과정 없이 단순히 좌우 이동만 하면 되는 홀로노믹(Holonomic) 시스템 특성의 이동로봇이 필요하다. 본 논문에서는 세 개의 옴니휠(Omni-Wheels)을 사용한 홀로노믹 이동로봇의 추적제어기를 개발한다. 옴니휠을 이용한 이동로봇은 시스템 파라미터의 불확실성(uncertainty)으로 인하여 선형 제어기로는 추적제어가 매우 어려운 상황이다. 그러므로 강인성이 탁월한 퍼지 제어기를 이용한 퍼지 적응 제어 기법을 설계하여 옴니휠 이동 로봇의 추적제어(tracking control) 성능을 높인다. 본 논문에서 제어 대상 시스템의 매개 변수의 불확실성에 강인한 퍼지 제어기를 병렬로 설계하고 시스템 인식(system identification)을 이용하여 대상 시스템이 특성 변화에 적절히 대처할 수 있는 적합한 퍼지 제어기를 선택한 후 피드백 제어를 실행하는 퍼지 다층 제어기(Fuzzy Multi-Layered Controller) 시스템을 이용한 적응 제어기법을 제시한다. 고전 적응 제어기와 기존 퍼지 적응 제어기의 문제점을 극복한 퍼지 적응 제어기를 도입하여 강인 제어기를 병렬로 설계하고 시스템 인식을 이용하여 대상 시스템의 특성 변화에 적절히 대처할 수 있는 적합한 퍼지 제어기를 선택한 후 피드백 제어를 실행하는 퍼지 다층 제어기(FMLC)를 제시한다. The trajectory control for omni-directional mobile robot is not easy. Especially, the tracking control which system uncertainty problem is included is much more difficult. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy multi-layered algorithm. The fuzzy control method is able to solve the problems of classical adaptive controller and conventional fuzzy adaptive controllers. It explains the architecture of a fuzzy adaptive controller using the robust property of a fuzzy controller. The basic idea of new adaptive control scheme is that an adaptive controller can be constructed with parallel combination of robust controllers. This new adaptive controller uses a fuzzy multi-layered architecture which has several independent fuzzy controllers in parallel, each with different robust stability area. Out of several independent fuzzy controllers, the most suited one is selected by a system identifier which observes variations in the controlled system parameter. This paper proposes a design procedure which can be carried out mathematically and systematically from the model of a controlled system; related mathematical theorems and their proofs are also given. Finally, the good performance of the developed mobile robot is confirmed through live tests of path control task.

The paper aims to expand a way of applying the fuzzy adaptive control strategy by analyzing the advantages and disadvantages of traditional PID control strategies. In view of the high requirements of the variable-pitch servo system for megawatt-level wind turbines, combined with the control characteristics of series-excited DC servo motors, a wind power variable-pitch DC servo system based on fuzzy adaptation was designed. The superiority and reliability of the fuzzy adaptive PI control method are verified by using simulation tools to carry out simulation experiments, and then the experiments are carried out by DC series servo motor and servo driver. In summary, the performance of the wind power variable pitch DC servo system is excellent.