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Journal articles on the topic 'Adaptive PID'

Author: Grafiati

Published: 23 June 2021

Last updated: 21 June 2025

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1

Aulia, Zahwa Dinda. "BLDC Motor Stability Management Using Adaptive PID (MRAC-PID)." Journal of Engineering and Scientific Research 5, no. 2 (2023): 98–104. http://dx.doi.org/10.23960/jesr.v5i2.147.

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BLDC motors have become popular in various industries such as automotive, consumer, healthcare, industrial automation, and instrumentation due to their optimal performance. To keep the BLDC motor in optimal condition, a control engineering system is required that serves as a controller. A single Proportional Integral Derivative (PID) control system is only suitable for linear conditions, so it cannot produce satisfactory output when there is a change in set point. To overcome this obstacle, an adaptive PID control system known as MRAC-PID control system is applied, which is able to control the stability of the BLDC motor as desired. Testing of this system is done under 4 different conditions using MATLAB software. After testing, the parameter values for the MRAC control system were obtained, namely ???????? = 0.4; ???????? = 50.75; ???????? = 0.0000867. Based on the test, the MRAC control system produces a system success rate of 81.2% to 98.9%.

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2

Sun, Yan Xia, and Zeng Hui Wang. "Adaptive Optimal Digital PID Controller." Applied Mechanics and Materials 789-790 (September 2015): 1021–26. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.1021.

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It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.

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3

Taeib, Adel, and Abdelkader Chaari. "PID Controller Based Adaptive PSO." International Review of Automatic Control (IREACO) 7, no. 1 (2014): 31. http://dx.doi.org/10.15866/ireaco.v7i1.1288.

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4

Bányász, Cs, and L. Keviczky. "A Simple Adaptive PID Tuner." IFAC Proceedings Volumes 31, no. 18 (1998): 471–77. http://dx.doi.org/10.1016/s1474-6670(17)42036-2.

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5

Amaral, W., J. C. Batista, G. Favier, L. Gimeno, A. Llamosas, and R. Machado. "PID Controller for Adaptive Applications." IFAC Proceedings Volumes 19, no. 13 (1986): 161–66. http://dx.doi.org/10.1016/s1474-6670(17)59534-8.

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6

Keviczky, L., and C. S. Bányász. "A Completely Adaptive PID Regulator." IFAC Proceedings Volumes 21, no. 9 (1988): 89–95. http://dx.doi.org/10.1016/s1474-6670(17)54708-4.

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7

Bartlomiej, B., and Ł. Andrzej. "The Adaptive Robust PID Controller." IFAC Proceedings Volumes 21, no. 9 (1988): 227–32. http://dx.doi.org/10.1016/s1474-6670(17)54730-8.

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8

Hägglund, T., and K. J. Åström. "An Industrial Adaptive PID Controller." IFAC Proceedings Volumes 23, no. 1 (1990): 251–56. http://dx.doi.org/10.1016/s1474-6670(17)52729-9.

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9

Thompson, S., and E. G. McCluskey. "An Expert Adaptive PID Controller." IFAC Proceedings Volumes 23, no. 1 (1990): 257–62. http://dx.doi.org/10.1016/s1474-6670(17)52730-5.

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10

Benaskeur, A. R., and A. Desbiens. "Backstepping-based adaptive PID control." IEE Proceedings - Control Theory and Applications 149, no. 1 (2002): 54–59. http://dx.doi.org/10.1049/ip-cta:20020100.

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11

Burakov, M. V., V. F. Shishlakov, and A. S. Konovalov. "ADAPTIVE NEURAL NETWORK PID CONTROLLER." Issues of radio electronics, no. 10 (October 20, 2018): 86–92. http://dx.doi.org/10.21778/2218-5453-2018-10-86-92.

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The problem of constructing an adaptive PID controller based on the Hopfield neural network for a linear dynamic plant of the second order is considered. A description of the plant in the form of a discrete transfer function is used, the coefficients of which are determined with the help of a neural network that minimizes the discrepancy between the outputs of the plant and the model. The neural network processes the current and delayed input and output signals of the plant, forming an output for estimating the coefficients of the model. Another neural network determines the PID regulator coefficients at which the dynamics of the system approach the dynamics of the reference process. The calculation of weights and displacements of neurons in Hopfield networks used for identification and control is based on the construction of Lyapunov functions. The proposed methodology can be used to organize adaptive control of a wide class of linear dynamic systems with variable parameters. The results of the simulation in the article show the effectiveness of the proposed method.

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12

Zhang, Yan Hong, De An Zhao, and Jian Sheng Zhang. "Research on Single Neuron Adaptive PID Control." Applied Mechanics and Materials 150 (January 2012): 174–77. http://dx.doi.org/10.4028/www.scientific.net/amm.150.174.

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As a branch of the intelligent control, neural networks is applied in control more and more widely, the single neuron adaptive PID control algorithm is studied in this paper, and the program is written by MATLAB, the common object of single neuron adaptive PID is simulated, and the effect of single neuron adaptive PID control parameters on control effect is analyzed, experimental results show that the single neuron PID control has more obvious advantages than general PID control.

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13

Padron, Joel Perez, Jose Perez, Jose Javier Perez Diaz, and Carlos Astengo-Noguez. "Time-Delay Fractional Variable Order Adaptive Synchronization and Anti-Synchronization between Chen and Lorenz Chaotic Systems Using Fractional Order PID Control." Fractal and Fractional 7, no. 1 (2022): 4. http://dx.doi.org/10.3390/fractalfract7010004.

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In this research work, time-delay adaptive synchronization and adaptive anti-synchronization of chaotic fractional order systems are analyzed via the Caputo fractional derivative, and the prob-lem of synchronization and anti-synchronization of chaotic systems of variable fractional order is solved by using the fractional order PID control law, the adaptive laws of variable-order frac-tional calculus, and a control law deduced from Lyapunov’s theory extended to systems of time-delay variable-order fractional calculus. In this research work, two important problems are solved in the control area: The first problem is described in which deals with syn-chro-nization of chaotic systems of adaptive fractional order with time delay, this problem is solved by using the fractional order PID control law and adaptative laws. The second problem is de-scribed in which deals with anti-synchronization of chaotic systems of adaptive frac-tional order with time delay, and this problem is solved by using the fractional order PID con-trol law and adaptative laws.

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14

Wang, Ya Gang, and Zhen Ping Tian. "Adaptive PID controllers with robustness specifications." International Journal of Modelling, Identification and Control 20, no. 2 (2013): 148. http://dx.doi.org/10.1504/ijmic.2013.056187.

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15

Joshi, Avinash S., and R. B. Ghatikar. "Self Tuning and Adaptive PID Controller." IETE Journal of Education 30, no. 2 (1989): 63–68. http://dx.doi.org/10.1080/09747338.1989.11436235.

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16

He, Xiao Song. "Adaptive Fuzzy PID Control for Cutter." Applied Mechanics and Materials 310 (February 2013): 506–9. http://dx.doi.org/10.4028/www.scientific.net/amm.310.506.

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It is very difficult to accurately control the speed of paper roller and sword roller of the paper cutting machine, and the proportions of two paces control have greater randomness. Alterable fuzzy-PID (proportion integral derivative) control arithmetic is put forward based on PLC. The alterable fuzzy controller and the PI adjustor are realized by PLC (Programmable Logic Controller). The satisfied results are achieved with PID control and adaptive fuzzy control. The simulation results show that the two schemes are satisfying. And the two schemes have their own characteristics.

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17

Badr, Ahmed Z. "Neural Network Based Adaptive PID Controller." IFAC Proceedings Volumes 30, no. 6 (1997): 251–57. http://dx.doi.org/10.1016/s1474-6670(17)43373-8.

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18

Tsai, Ching-Chih, Chien-Cheng Yu, and Chia-Ta Tsai. "Adaptive ORFWNN-Based Predictive PID Control." International Journal of Fuzzy Systems 21, no. 5 (2019): 1544–59. http://dx.doi.org/10.1007/s40815-019-00650-w.

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19

de Larminat, Ph. "Adaptive PID Regulators. Ambitions and Limitations." IFAC Proceedings Volumes 21, no. 10 (1988): 105–10. http://dx.doi.org/10.1016/b978-0-08-036620-3.50023-5.

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20

Esfandyari, Morteza, Mohammad Ali Fanaei, and Hadi Zohreie. "Adaptive fuzzy tuning of PID controllers." Neural Computing and Applications 23, S1 (2012): 19–28. http://dx.doi.org/10.1007/s00521-012-1215-8.

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21

Silveira, Antonio S., Antonio A. R. Coelho, and Francisco J. Gomes. "Model-Free Adaptive PID Controllers Applied to the Benchmark PID'12." IFAC Proceedings Volumes 45, no. 3 (2012): 370–75. http://dx.doi.org/10.3182/20120328-3-it-3014.00063.

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22

Feng, Jiangtao, Qinhe Gao, and Wenliang Guan. "Mathematical Modeling and Fuzzy Adaptive PID Control of Erection Mechanism." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 254–63. https://doi.org/10.12928/TELKOMNIKA.v15i2.3568.

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This paper describes an application of fuzzy adaptive PID controller to erection mechanism. Mathematical model of erection mechanism was derived. Erection mechanism is driven by electrohydraulic actuator system which is difficult to control due to its nonlinearity and complexity. Therefore fuzzy adaptive PID controller was applied to control the system. Simulation was performed in Simulink software and experiment was accomplished on laboratory equipment. Simulation and experiment results of erection angle controlled by fuzzy logic, PID and fuzzy adaptive PID controllers were respectively obtained. The results show that fuzzy adaptive PID controller can effectively achieve the best performance for erection mechanism in comparison with fuzzy logic and PID controllers.

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23

Bangzhen Huang, Bangzhen Huang, Yani Cui Bangzhen Huang, Jia Ren Yani Cui, and Jiafu Yi Jia Ren. "Miniature Detection Buoy Based on Fuzzy Adaptive PID Algorithm." 電腦學刊 33, no. 6 (2022): 119–29. http://dx.doi.org/10.53106/199115992022123306010.

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<p>The ocean buoy is an important technical means to obtain ocean environmental parameters as a newly developed detection technology. Traditional ocean buoys use hydraulic devices to change the volume of the oil bladder to achieve fixed-depth control, with slow response speed and long detection cycle. Therefore, it can only be applied to deep-sea environment and lacks the ability of rapid and fine detection in shallow waters (<200m). Considering the above problems, the paper designs a miniature detection buoy, which adopts an innovative inner and outer sleeves design. The miniature detection buoy uses a geared motor to drive the internal screw rotation to quickly stretch and shrink the volume of the shell to achieve the functions of floating, diving and fixed-depth. Moreover, the control performance research is carried out by establishing the dynamics model of the miniature detection buoy. According to the model, the fuzzy adaptive PID control algorithm is used to accurately control the speed and steering of the geared motor. The simulation results show that the buoy system with the fuzzy adaptive PID control algorithm has better fixed-depth accuracy and response speed than the classic PID control algorithm.</p> <p>&nbsp;</p>

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24

Liu, Bin, Gang Yao, Xiao Bing Xiao, and Xu Sheng Yin. "The Research on Self-Adaptive Fuzzy PID Controller." Applied Mechanics and Materials 373-375 (August 2013): 1462–65. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1462.

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The parameters of the traditional proportional-integral-derivative (PID) controller are hard to automatically adjust when the controlled object changes, which controls ineffective for time-varying, nonlinear system. Combine the fuzzy control and PID control, and use self-adaptive fuzzy control to achieve self-tuning PID parameters online. Using matlab simulation system, the results show that the self-adaptive fuzzy PID control effects have been improved than the conventional PID control.

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25

Pham, Le-Anh-Dung, Vu-Quan Tran, Thanh-Tay Nguyen, et al. "Adaptive-PID Experimental STM32F4 Controller for Rotary Inverted Pendulum." Robotica & Management 28, no. 2 (2023): 44–47. http://dx.doi.org/10.24193/rm.2023.2.8.

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Rotary inverted pendulum (RIP) is a classical model of control engineering. Paper deals with a PID-adaptive structure which is based on structure of neuron to train Kp, Ki, Kd through operation. In simulation, our adaptive controller is proven to work in larger range than classical PID controller. Through experimental model using STM32F4, we prove vibration of system under adaptive-PID is smaller than under classical PID structure. Then, combination of neuron network (NN) and PID control can be used as simple structure for single-input multi output (SIMO) systems which are similar to RIP.

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26

Cheng, Zheng Mei, Xiao Ding Guo, Xiao Ke Chen, and Jing Xin Deng. "The Simulation of Adaptive Fuzzy PID Control System on the New Test Power Source System of Small Locomotive." Applied Mechanics and Materials 103 (September 2011): 240–45. http://dx.doi.org/10.4028/www.scientific.net/amm.103.240.

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The voltage stability of test power source system about small locomotive is poor, and the response speed is slow. The theory of adaptive fuzzy PID control used in the small locomotive test power source system was proposed. The design of adaptive fuzzy PID controller and the effect of simulation were detail described. The control effect of adaptive fuzzy PID control and the traditional PID control is compared. The application value of adaptive fuzzy PID control on small locomotive test power source system has been proved.

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27

shehata, Ahmed M., Mohamed K. khalil, and Mahmoud M. Ashry. "Adaptive Fuzzy PID Controller applied to micro turbojet engine." Journal of Physics: Conference Series 2128, no. 1 (2021): 012030. http://dx.doi.org/10.1088/1742-6596/2128/1/012030.

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Abstract Digital controllers are utilized for controlling modern gas turbine engines. Firstly, an identified discrete model is built for a micro turbojet engine (MTE) jet cat P200sx. Two controllers are designed, gain scheduling PID and adaptive fuzzy tuned PID controllers are presented in this paper. Analysis of traditional PID and Adaptive fuzzy tuned PID controller applied for micro turbojet engine is presented. According to the fuzzy rules, a fuzzy logic controller (FLC) is developed to modify the gains of the PID controller automatically. MATLAB/Simulink is utilized to simulate the complete device consisting of an adaptive fuzzy PID controller, and the micro turbojet engine model. The two controllers’ responses are compared. A comparison of the robustness of each controller against the effect of noise and rejection of disturbance is illustrated. The results showed that, through small rise time, small set time, minimal overshoot, and minimal SteadyState speed error, the PID controller adaptive fuzzy tuned provides better dynamic MTE action and thus superior performance.

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28

Mao, Zheng Yu, Jing Sheng Zhang, and Lu Xu Zhang. "Research on Adaptive Fuzzy PID Dust Collection System." Advanced Materials Research 722 (July 2013): 437–40. http://dx.doi.org/10.4028/www.scientific.net/amr.722.437.

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Aimed at time lag, time variant, serious inertia, nonlinearity problems and the difficulty of PID controller parameters adjusting, the combination of PID control and fuzzy control was put forward, this paper introduces a kind of design of adaptive fuzzy PID dust collection system of constant pressure with regarding the pressure of the deviation and the rate of change of deviation as the input variables in the ventilation pipe. The three parameters of PID controller can be adjusted by establishing the fuzzy control rules and practicing the fuzzy generalize. The experiment simulation and the practical test show that the response of the system with adaptive fuzzy PID control is much faster than the tradition PID control but the overshoot is lower and quickly achieve the steady-state.

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29

Zhou, Jun. "Intelligent Information Control for Air System." Applied Mechanics and Materials 310 (February 2013): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.310.502.

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PID control has been widely applied in the industrial process control because of its robust and easy realization, but it is difficult to tune the parameters of PID controller, which often leads to oscillation and overshoot. Due to no repetition and random of adaptive fuzzy PID control, the authors propose a method to search for normalization PID controller parameters based on adaptive fuzzy PID control, which can be expected to have higher ability of searching for global optimal PID parameters according to the performance index of control system . The MATLAB simulation of the Adaptive fuzzy PID controller and a PID controller were carried out on the Air Conditioning System temperature. Results showed that: Response time of Adaptive fuzzy PID was 0.46 s., and maximum overshoot didn’t exceed 3.29%.The stability accuracy and rapidity of the system were able to satisfy the Air Conditioning System technical requirements.

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30

Zhang, Jing Dong, Rui Tang, and Yong Qiao Wei. "Fuzzy Adaptive PID Control for Parallel Machine Tool." Applied Mechanics and Materials 273 (January 2013): 660–64. http://dx.doi.org/10.4028/www.scientific.net/amm.273.660.

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The hydraulic control system, which is important composition of parallel machine tool, is a high order, nonlinear, parameter uncertain system, which seriously affect dynamic performance of machine tool, So it is very difficult to gain good performance with traditional control. By making a single-channel control of parallel machine tool as the research object, a fuzzy adaptive PID controller is proposed based on the traditional PID control and fuzzy control theory. Using the Fuzzy PID controller, PID parameters can real-time change and the system has good adaptive ability and robustness. System dynamic simulation is made by the MATLAB. Simulation results reveal that the performance of this adaptive fuzzy PID controller is much better than the conventional PID controller, even in the external disturbance and system conditions changing, this controller has the the characteristics of fast response and good robustness.

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31

Chen, Xiu Jia, and Hong Di Qiu. "Research on Single Neuron Adaptive PID Controller." Applied Mechanics and Materials 651-653 (September 2014): 826–30. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.826.

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The paper focuses on single neuron adaptive PID controller based on unsupervised Hebb algorithm, and simulation research on the controller is carried out for a second-order pure lag process system. Simulation results show that through learning and adjusting weights of single neuron adaptive PID controller, its online self-tuning ability can make timely adjustment of PID controller parameters according to controlled object changes and external disturbances in order to ensure that the stability and robustness of the system and, ultimately, more satisfactory actual control effect is obtained. At last, the control characteristics and parameter design rules are concluded.

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32

Zhao, Keqin, Diming Lou, Yunhua Zhang, and Liang Fang. "Optimization and Realization of the Coordination Control Strategy for Extended Range Electric Vehicle." Machines 10, no. 5 (2022): 297. http://dx.doi.org/10.3390/machines10050297.

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This paper designed a fuzzy adaptive proportional integral differential (PID) control algorithm to optimize the overshoot of speed and torque, fuel consumption and exhaust emissions of the traditional PID control strategy in the process of working condition switching of an extended range electric vehicle. The simulation was carried out in Matlab/Simulink, and the optimization of the control strategy was verified by a bench test. The results show that the fuzzy adaptive PID control strategy effectively reduced the speed overshoot in the process of working condition switching compared with the traditional PID control strategy. The bench test proved that the fuzzy adaptive PID control strategy could effectively optimize the switching process, especially in the speed and torque reduction switching process, and the speed overshoot rate of the fuzzy PID control was greatly reduced to 0.7%, far less than that of the traditional PID control with 6.6%, while the torque overshoot rate was within 0.8%. Additionally, the fuzzy adaptive PID control could effectively reduce the fuel consumption, especially in the switching process of increasing the speed and torque, where the fuel consumption of the fuzzy adaptive PID control was 2.1% and 0.5% lower than that of the traditional PID control, respectively. Additionally, the fuzzy adaptive PID control could also reduce the particulate emissions, especially in the process of increasing the speed and torque, where the number of particles of the fuzzy PID control was 11% and 19% less than that of the traditional PID control, respectively. However, the NOx emissions based on the fuzzy PID control were slightly higher than those of the traditional PID control due to the smooth operation and improved combustion.

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33

Irmawan, Erwhin, and Erwan Eko Prasetiyo. "Kendali Adaptif Neuro Fuzzy PID untuk Kestabilan Terbang Fixed Wing UAV (Adaptive Control of Neuro Fuzzy PID for Fixed Wing UAV Flight Stability)." Jurnal Nasional Teknik Elektro dan Teknologi Informasi 9, no. 1 (2020): 73–78. http://dx.doi.org/10.22146/jnteti.v9i1.142.

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Unmanned Aerial Vehicle (UAV), especially fixed wing, are widely used to carry out various missions, namely civil and military missions. To support the implementation of this mission, it is necessary to develop an intelligent automatic control system (autopilot). In this paper, an autopilot system with adaptive neuro fuzzy PID control is developed to control lateral (pitch) and longitudinal (roll) motion, by taking advantage of PID, fuzzy, and neural network control. Therefore, robust controls which can handle non-linear conditions can be formed. This paper aims to determine the performance of adaptive control of neuro fuzzy PID controllers for longitudinal and lateral motion on UAV. The result shows that adaptive control of neuro fuzzy PID are able to control the lateral and longitudinal motion of the aircraft and able to compensate for interferences from environmental disturbances in flying condition, such as changes in direction and wind speed that causes changes in aircraft attitude. The control characteristics of neuro fuzzy PID adaptive control in lateral and longitudinal motion are relatively similar. Adaptive control of neuro fuzzy PID has better performance than fuzzy PID control, i.e., faster settling time and lower percentage of maximum overshoot.

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34

Zhang, Xuelin, Xiaobin Xu, Xiaojian Xu, Pingzhi Hou, Haibo Gao, and Feng Ma. "Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule." Mathematics 11, no. 5 (2023): 1145. http://dx.doi.org/10.3390/math11051145.

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To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of KP, KI, and KD. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

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35

Qiu, Xin Yun, and Yuan Gao. "Adaptive PID Controller Based on Single Neuron." Advanced Materials Research 466-467 (February 2012): 981–85. http://dx.doi.org/10.4028/www.scientific.net/amr.466-467.981.

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An adaptive PID controller based on single neuron is proposed. The properties, control algorithm, parameters tuning, the control law and the application condition of the controller are studied in the paper. To satisfy the properties of the requirements of the control system in an electromotor group, such as a broad dynamic changing range, a fast response, a little overshoot and time-variable parameter, a new-type self-optimizing PID controller based on artificial neural networks is proposed and studied. It is verified that the controller has few adjustable parameters and excellent robust performance. The results of simulation and experiment prove that the controller is superior to the traditional PID controller.

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36

Xia, Chang Gao, and Chong Cao. "Tuning of PID Parameters and Fuzzy Adaptive PID Control of the Hydrostatic Driving System." Advanced Materials Research 403-408 (November 2011): 5112–16. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.5112.

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Composed of a variable displacement pump and a constant displacement motor, the hydrostatic driving system is a kind of closed speed control system with adjustable displacement. It is widely used in the field of engineering vehicle and other fields. Based on an analysis of the constitution and mathematical model of the hydrostatic driving system, the present study tuned PID parameters by using the critical proportioning method and the optimization method of NCD respectively. Then a kind of fuzzy adaptive PID controller was designed on the basis of the traditional PID control and the fuzzy control theory. In the controller, fuzzy logic was used to realize online self-tuning of PID parameters according to the motor speed error and its derivative, so that the system could have better adaptive ability and strong disturbance resisting performance. The dynamic simulation was made in MATLAB/SIMULINK. The simulation results show that the optimization method of NCD has better tuning effect and the response performance of the fuzzy adaptive PID controller is better than that of the classic one. Besides, it should be noted that a drawback was found about the fuzzy adaptive PID control. On the basis of fixed scale factors, a group of quantification factors is appropriate for a specific input signal, but for other signals, the response of the system is not so ideal. A method of adjusting quantification factors according to input signal was adopted to solve the above problem. Automatic adjusting of quantification factors was realized, and this could ensure ideal response to all input signals.

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37

Wu, Siqi, and Xihao Zhao. "Current status and prospects of adaptive PID control in spacecraft attitude control." Applied and Computational Engineering 11, no. 1 (2023): 203–9. http://dx.doi.org/10.54254/2755-2721/11/20230234.

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Spacecraft attitude control is a precise aspect that requires accurate operation. Adaptive PID control, in this case, could be a promising approach for improving the performance. In order to provide a better comprehension about the adaptive PID control in spacecraft attitude control, This paper will mainly discuss the aspect in current solutions, challenges, and future directions through providing and interpreting the scientific and reliable essays in this area. Several kinds of classic adaptive PID control will be introduced in the paper for their feasibility and applicability. Then, paper will discuss several kinds of limitations faced by traditional adaptive PID control in reality such as the complexity of spacecraft dynamics. To overcome these limitations, this paper will also discuss a couple of machine learning based adaptive PID control proposed by researchers such as fuzzy logic systems. These adaptive schemes can adjust the controller parameters through advanced algorithms to ameliorate the control performance of the spacecraft.

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38

Gulhane, Sanjay M., Abhay R. Kasetwar, Dr Vicky Butram, and Dr Milind Narlawar. "Improved PID based Adaptive Controllers for Denoising Biomedical Signals." International Journal of Electrical and Electronics Research 12, no. 3 (2024): 1051–59. http://dx.doi.org/10.37391/ijeer.120340.

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Biomedical signal processing is one of the most popular research domains. Very fine features in biomedical signals carry important information regarding patient’s health. So, it is necessary to have noise free biomedical signals for the correct diagnosis. The major trouble for biomedical equipment is Power Line Interference (PLI) which impairs the signals. An adaptive filter can be one of the possible solutions for the removal of non-stationary noise, but maintaining the system stability along with a high convergence rate is a critical issue. The adaptive algorithm works on the principle of minimization of error for optimized coefficients updating while PID controller attempts to minimize the error over time by adjusting the control variables. In this paper, these two different approaches are combined to get an efficient solution for adaptive PLI cancellation and two new algorithms namely PID-based Response Adjustment for Reducing Error (PID-RARE) and PID-based Coefficient Adjustment for Reducing Error (PID-CARE) are proposed. The integration of NSLMS adaptive algorithm with PID controller in the proposed algorithms are found to be an effective solution to adaptive PLI cancellation and have shown quite better performance in terms of SNRout, correlation coefficient, mean square error thereby providing more cleaner signal at lesser convergence rate.

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Chang, Kai, Xiao Jian Han, and Yong Yang. "Self-Adaptive PID Control of Hydraulic Quadruped Robot." Applied Mechanics and Materials 496-500 (January 2014): 1407–12. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1407.

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Servo hydraulic system is important one of driving patterns of quadruped robot. While servo hydraulic system has characteristics of parameters time-varying and strong nonlinearity, so traditional PID control pattern cannot meet all the control demands well. To solve these problems, a set of control hardware is designed for a quadruped robot and a new control algorithm which can adjust PID parameters in real-time condition based on fuzzy control theory is proposed. By deducing transfer function, establishing system model and using MATLAB simulation tools to simulate the two algorithms, we compared the two algorithms and the simulation result turns out that compared with traditional PID control algorithm, the fuzzy PID control algorithm has features such as low overshoot, high stability precision, strong robustness. At the same time, the result turns out that the fuzzy PID control algorithm is much fitter for the joint control of quadruped robot.

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Uçak, Kemal, and Beyza Nur Arslantürk. "Adaptive MIMO fuzzy PID controller based on peak observer." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 13, no. 2 (2023): 139–50. http://dx.doi.org/10.11121/ijocta.2023.1247.

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In this paper, a novel peak observer based adaptive multi-input multi-output (MIMO) fuzzy proportional-integral-derivative (PID) controller has been introduced for MIMO time delay systems. The adaptation mechanism proposed by Qiao and Mizumoto [1] for single-input single-output (SISO) systems has been enhanced for MIMO system adaptive control. The tracking, stabilization and disturbance rejection performances of the proposed adaptation mechanism have been evaluated for MIMO systems by comparing with non-adaptive fuzzy PID and classical PID controllers. The obtained results indicate that the introduced adjustment mechanism for MIMO fuzzy PID controller can be successfully deployed for MIMO time delay systems.

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Xu, Guo Sheng. "The Design and Simulation of Control System for Electrical Heating." Applied Mechanics and Materials 325-326 (June 2013): 1193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.325-326.1193.

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In view of the fact that the performance of any conventional PID control can t meet the requirement an electric boiler temperature control system, this paper puts forward a kind of improved algorithm for tuning the PID parameters. an adaptive fuzzy controller with adjusting factor is proposed in this paper. Experimental results illustrate that the adaptive fuzzy PID controller achieved the system performance index. The method of adaptive fuzzy PID control is a ideal method.

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Hernandez-Barragan, Jesus, Jorge D. Rios, Alma Y. Alanis, Carlos Lopez-Franco, Javier Gomez-Avila, and Nancy Arana-Daniel. "Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm." Electronics 9, no. 4 (2020): 636. http://dx.doi.org/10.3390/electronics9040636.

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In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

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Yang, Ke, Jianhua Li, Jiajie Yang, and Lixin Xu. "Research on Adaptive Closed-Loop Control of Microelectromechanical System Gyroscopes under Temperature Disturbance." Micromachines 15, no. 9 (2024): 1102. http://dx.doi.org/10.3390/mi15091102.

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Microelectromechanical System (MEMS) gyroscopes are inertial sensors used to measure angular velocity. Due to their small size and low power consumption, MEMS devices are widely employed in consumer electronics and the automotive industry. MEMS gyroscopes typically use closed-loop control systems, which often use PID controllers with fixed parameters. These classical PID controllers require a trade-off between overshoot and rise time. However, temperature variations can cause changes in the gyroscope’s parameters, which in turn affect the PID controller’s performance. To address this issue, this paper proposes an adaptive PID controller that adjusts its parameters in response to temperature-induced changes in the gyroscope’s characteristics, based on the error value. A closed-loop control system using the adaptive PID was developed in Simulink and compared with a classical PID controller. The results demonstrate that the adaptive PID controller effectively tracked the changes in the gyroscope’s parameters, reducing overshoot by 96% while maintaining a similar rise time. During gyroscope startup, the adaptive PID controller achieves faster stabilization with a 0.036 s settling time, outperforming the 0.06 s of the conventional PID controller.

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Shi, Gong She, Lei Huang, and Wei Hu. "The Brushless DC Motor Adaptive Fuzzy PID Servo Controller Design." Applied Mechanics and Materials 246-247 (December 2012): 838–41. http://dx.doi.org/10.4028/www.scientific.net/amm.246-247.838.

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The brushless DC motor (BLDCM) non-linear and the complexity of the working conditions are likely to cause the conventional PID servo control performance is not satisfactory. In order to improve the performance of the BLDCM servo control system and PID parameter tuning efficiency, this paper designs an adaptive fuzzy PID controller. Fuzzy logic PID controller parameters Kp, Ki, Kd are adjusted online real time to achieve the effect of optimal control, the BLDCM speed is as to the control object, and in the Matlab of Simulink toolbox simulation is used to achieve speed closed loop of BLDCM. According to comparative analysis of the conventional PID and adaptive fuzzy PID of Dynamic response curve, adaptive Fuzzy PID quick start for brushless DC motors, anti-disturbance has better control effect.

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Murali, Dasari, Reddy Srinivasula, and Vijaya Kumar M. "GA-ANFIS PID compensated model reference adaptive control for BLDC motor." International Journal of Power Electronics and Drive System (IJPEDS) 10, no. 1 (2019): 265–76. https://doi.org/10.11591/ijpeds.v10.i1.pp265-276.

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Adaptive control is one of the widely used control strategies to design advanced control systems for better performance and accuracy. Model reference adaptive control (MRAC) is a direct adaptive strategy with some adjustable controller parameters and an adjusting mechanism to adjust them. In this work Model Reference Adaptive Control for BLDC motors has been designed with a PID controller tuned by GA-ANFIS. GA-Trained ANFIS framework for tuning the PID controller has been proposed. This is used along with the MRAC to deliver enhanced performance in the control of BLDC motor. The performance of the proposed approach is validated for motor control under conditions of change in speed, change in load, change in inertia and change in phase resistance. The performance is validated against convention PID and self tuning PID controllers. The result demonstrates a superior performance of the proposed approach.

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Hamada, Jun, Satoshi Suzuki, Tomoyasu Ichikawa, Hironori Kurihara, and Kazuya Sumida. "Adaptive PID Control of Multi-rotor Helicopter." Journal of the Robotics Society of Japan 36, no. 7 (2018): 508–15. http://dx.doi.org/10.7210/jrsj.36.508.

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Fraga-Gonzalez, Luis Fernando, Rita Q. Fuentes-Aguilar, Alejandro Garcia-Gonzalez, and Gildardo Sanchez-Ante. "Adaptive simulated annealing for tuning PID controllers." AI Communications 30, no. 5 (2017): 347–62. http://dx.doi.org/10.3233/aic-170741.

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Maia, Antônio A. T., Juan C. Horta-Gutierrez, Ricardo N. N. Koury, and Luiz Machado. "Superheating control using an adaptive PID controller." HVAC&R Research 20, no. 4 (2014): 424–34. http://dx.doi.org/10.1080/10789669.2013.874842.

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Macháček, Jiří, and Vladimír Bobál. "Adaptive PID Controller with On-Line Identification." IFAC Proceedings Volumes 33, no. 4 (2000): 437–42. http://dx.doi.org/10.1016/s1474-6670(17)38282-4.

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Wang, Wei, Lihong Xu, and Haigen Hu. "Neuron adaptive PID control for greenhouse environment." Journal of Industrial and Production Engineering 32, no. 5 (2015): 291–97. http://dx.doi.org/10.1080/21681015.2015.1048752.

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