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    MathWorks Inc control se implementó mediante un esquema pid sintonizado con simulink control design
    Control Se Implementó Mediante Un Esquema Pid Sintonizado Con Simulink Control Design, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 885 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/pid+controller+design/Simulink+Control+Design/10__1109_slash_tla__2026__11369469-178-1-10
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    Article Snippet: .. Retrieved December 1, 2023, from https://www.analyticssteps.com/blogs/future-autonomouscars-edge-computing Control Tutorials for MATLAB and Simulink—Introduction: PID Controller Design. (n.d.). ..

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    Article Title: A Hybrid Interleaved DC-DC Converter Based on Buck-Boost topologies for Medium Voltage Applications
    Article Snippet: .. Based on the state space averaged model derived in section 5, the PI-controller’s parameters Kp and Ki are tuned using the "PID tuner" feature inherent in the (PID) controller design of Matlab-Simulink [32]. ..



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    Fig. <t>1.</t> <t>Simulink</t> simulation of <t>PID</t> controller 2.4 PID Parameter Tuning In PID control, it is important to achieve the best performance of the control system by properly adjusting the values of these three parameters. The three parameters are proportional coefficient (Kp), integral coefficient (Ki) and differential coefficient (Kd), which correspond to the three basic control behaviors of the controller, reflecting the response speed, stability and anti- interference of the controller. The specific role is as follows: The magnification of the proportional coefficient Kp control error is the most basic proportional controller in the control loop. The larger the proportional coefficient Kp, the faster the response speed of the controller to the error, but at the same time, it will also increase the stability of the system, oscillation and even instability such as oscillation. The role of the integral coefficient Ki is to eliminate the steady-state error of the control system and to compensate for slower changes. In general, a larger integral coefficient can reduce the steady-state error of the controller, but if the integral coefficient is too large, the system may have integral saturation, resulting in system failure (Zhang et al., 2024). The differential coefficient Kd is used to weaken the sensitivity of the controller output to interference and suppress the fast response of the system. The larger the differential coefficient
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    Fig. 1. Simulink simulation of PID controller 2.4 PID Parameter Tuning In PID control, it is important to achieve the best performance of the control system by properly adjusting the values of these three parameters. The three parameters are proportional coefficient (Kp), integral coefficient (Ki) and differential coefficient (Kd), which correspond to the three basic control behaviors of the controller, reflecting the response speed, stability and anti- interference of the controller. The specific role is as follows: The magnification of the proportional coefficient Kp control error is the most basic proportional controller in the control loop. The larger the proportional coefficient Kp, the faster the response speed of the controller to the error, but at the same time, it will also increase the stability of the system, oscillation and even instability such as oscillation. The role of the integral coefficient Ki is to eliminate the steady-state error of the control system and to compensate for slower changes. In general, a larger integral coefficient can reduce the steady-state error of the controller, but if the integral coefficient is too large, the system may have integral saturation, resulting in system failure (Zhang et al., 2024). The differential coefficient Kd is used to weaken the sensitivity of the controller output to interference and suppress the fast response of the system. The larger the differential coefficient

    Journal: Journal of Engineering Research and Reports

    Article Title: Design and Application of Fuzzy PID Controller Based on MATLAB

    doi: 10.9734/jerr/2024/v26i111323

    Figure Lengend Snippet: Fig. 1. Simulink simulation of PID controller 2.4 PID Parameter Tuning In PID control, it is important to achieve the best performance of the control system by properly adjusting the values of these three parameters. The three parameters are proportional coefficient (Kp), integral coefficient (Ki) and differential coefficient (Kd), which correspond to the three basic control behaviors of the controller, reflecting the response speed, stability and anti- interference of the controller. The specific role is as follows: The magnification of the proportional coefficient Kp control error is the most basic proportional controller in the control loop. The larger the proportional coefficient Kp, the faster the response speed of the controller to the error, but at the same time, it will also increase the stability of the system, oscillation and even instability such as oscillation. The role of the integral coefficient Ki is to eliminate the steady-state error of the control system and to compensate for slower changes. In general, a larger integral coefficient can reduce the steady-state error of the controller, but if the integral coefficient is too large, the system may have integral saturation, resulting in system failure (Zhang et al., 2024). The differential coefficient Kd is used to weaken the sensitivity of the controller output to interference and suppress the fast response of the system. The larger the differential coefficient

    Article Snippet: In terms of motor control, Simulink 's PID control structure diagram (shown in Fig. 1) facilitates controller design and simulation.

    Techniques: Control

    Fig. 2. Step response of PID controller After repeated parameter tuning, the final parameters of the PID are: Kp = 50, Ki = 0.8, Kd = 0.23, and the output waveform is the most stable. It can be seen from Fig. 2 that the system can respond quickly. For incremental PID control, such performance indicators have been able to achieve the purpose of stable control of smart cars. 2.5 Establish Fuzzy Control Rules In PID control, the system deviation refers to the difference between the actual output and the expected output, which is the so-called error. In general, the PID controller measures the error between the system output and the desired output, and calculates and adjusts the output signal of the controller according to the size of the error, so that the system output is as close as possible to the desired output. If the system deviation is large, it is necessary to use the proportional coefficient Kp to adjust the response speed of the controller to the error. If the system deviation is small, it is necessary to use the integral coefficient Ki to reduce the error, or use the differential coefficient Kd to suppress the oscillation of the system. The controller needs to use these three coefficients flexibly according to the actual situation and system characteristics to achieve the control objectives. Therefore, the key to the success of the fuzzy PID control system is how to quickly and accurately realize the self-adjustment of the three coefficients of the PID, and fuzzy adaptive control of the speed of the intelligent vehicle (Chen et al., 2024).

    Journal: Journal of Engineering Research and Reports

    Article Title: Design and Application of Fuzzy PID Controller Based on MATLAB

    doi: 10.9734/jerr/2024/v26i111323

    Figure Lengend Snippet: Fig. 2. Step response of PID controller After repeated parameter tuning, the final parameters of the PID are: Kp = 50, Ki = 0.8, Kd = 0.23, and the output waveform is the most stable. It can be seen from Fig. 2 that the system can respond quickly. For incremental PID control, such performance indicators have been able to achieve the purpose of stable control of smart cars. 2.5 Establish Fuzzy Control Rules In PID control, the system deviation refers to the difference between the actual output and the expected output, which is the so-called error. In general, the PID controller measures the error between the system output and the desired output, and calculates and adjusts the output signal of the controller according to the size of the error, so that the system output is as close as possible to the desired output. If the system deviation is large, it is necessary to use the proportional coefficient Kp to adjust the response speed of the controller to the error. If the system deviation is small, it is necessary to use the integral coefficient Ki to reduce the error, or use the differential coefficient Kd to suppress the oscillation of the system. The controller needs to use these three coefficients flexibly according to the actual situation and system characteristics to achieve the control objectives. Therefore, the key to the success of the fuzzy PID control system is how to quickly and accurately realize the self-adjustment of the three coefficients of the PID, and fuzzy adaptive control of the speed of the intelligent vehicle (Chen et al., 2024).

    Article Snippet: In terms of motor control, Simulink 's PID control structure diagram (shown in Fig. 1) facilitates controller design and simulation.

    Techniques: Control

    Fig. 6. Fuzzy control Kd output surface By constructing a fuzzy inference system structure file, the designed membership function and a fuzzy controller containing 49 fuzzy rules are embedded in the fuzzy logic control simulation module of MATLAB. We completed the construction of a fuzzy adaptive PID controller. In the controller, the Kp, Ki, Kd coefficients are output after the fuzzy controller is processed, and the corresponding surfaces are shown in Figs. 4,5,6.

    Journal: Journal of Engineering Research and Reports

    Article Title: Design and Application of Fuzzy PID Controller Based on MATLAB

    doi: 10.9734/jerr/2024/v26i111323

    Figure Lengend Snippet: Fig. 6. Fuzzy control Kd output surface By constructing a fuzzy inference system structure file, the designed membership function and a fuzzy controller containing 49 fuzzy rules are embedded in the fuzzy logic control simulation module of MATLAB. We completed the construction of a fuzzy adaptive PID controller. In the controller, the Kp, Ki, Kd coefficients are output after the fuzzy controller is processed, and the corresponding surfaces are shown in Figs. 4,5,6.

    Article Snippet: In terms of motor control, Simulink 's PID control structure diagram (shown in Fig. 1) facilitates controller design and simulation.

    Techniques: Control

    Fig. 7. System block diagram of fuzzy PID controller

    Journal: Journal of Engineering Research and Reports

    Article Title: Design and Application of Fuzzy PID Controller Based on MATLAB

    doi: 10.9734/jerr/2024/v26i111323

    Figure Lengend Snippet: Fig. 7. System block diagram of fuzzy PID controller

    Article Snippet: In terms of motor control, Simulink 's PID control structure diagram (shown in Fig. 1) facilitates controller design and simulation.

    Techniques: Blocking Assay

    Fig. 8. Adaptive fuzzy PID control response diagram

    Journal: Journal of Engineering Research and Reports

    Article Title: Design and Application of Fuzzy PID Controller Based on MATLAB

    doi: 10.9734/jerr/2024/v26i111323

    Figure Lengend Snippet: Fig. 8. Adaptive fuzzy PID control response diagram

    Article Snippet: In terms of motor control, Simulink 's PID control structure diagram (shown in Fig. 1) facilitates controller design and simulation.

    Techniques: Control

    Fig. 9. Adaptive fuzzy PID control response diagram It can be seen from the simulation result Fig. 8 that compared with traditional PID control and incremental PID control, adaptive fuzzy PID control has better control effect and higher stability. Under different working environments and operating conditions, the adaptive fuzzy PID controller can automatically adjust the control parameters to achieve more accurate and stable control. In comparison with incremental PID control, the overshoot of adaptive fuzzy PID control is almost 0, indicating that adaptive fuzzy PID control greatly improves the rapid response and robustness of the control system. This also further proves the superiority and applicability of adaptive fuzzy PID control in automatic control systems. (2) Anti-interference test of fuzzy PID control In the stable state of the system, if a large speed or sharp deceleration is suddenly given, this instantaneous change will cause great interference to the control system and cause further instability of the system. However, with the support of adaptive fuzzy PID control, the system can adapt to different load and working conditions by automatically adjusting the control parameters, so as to achieve fast response and strong robustness. For example, after giving the system a large speed signal, the control system Fig. 9 shows that the gain is generated, but the system is quickly stabilized by the support of

    Journal: Journal of Engineering Research and Reports

    Article Title: Design and Application of Fuzzy PID Controller Based on MATLAB

    doi: 10.9734/jerr/2024/v26i111323

    Figure Lengend Snippet: Fig. 9. Adaptive fuzzy PID control response diagram It can be seen from the simulation result Fig. 8 that compared with traditional PID control and incremental PID control, adaptive fuzzy PID control has better control effect and higher stability. Under different working environments and operating conditions, the adaptive fuzzy PID controller can automatically adjust the control parameters to achieve more accurate and stable control. In comparison with incremental PID control, the overshoot of adaptive fuzzy PID control is almost 0, indicating that adaptive fuzzy PID control greatly improves the rapid response and robustness of the control system. This also further proves the superiority and applicability of adaptive fuzzy PID control in automatic control systems. (2) Anti-interference test of fuzzy PID control In the stable state of the system, if a large speed or sharp deceleration is suddenly given, this instantaneous change will cause great interference to the control system and cause further instability of the system. However, with the support of adaptive fuzzy PID control, the system can adapt to different load and working conditions by automatically adjusting the control parameters, so as to achieve fast response and strong robustness. For example, after giving the system a large speed signal, the control system Fig. 9 shows that the gain is generated, but the system is quickly stabilized by the support of

    Article Snippet: In terms of motor control, Simulink 's PID control structure diagram (shown in Fig. 1) facilitates controller design and simulation.

    Techniques: Control, Comparison, Generated