simulink controller algorithm Search Results


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MathWorks Inc simulink controller algorithm
Fig.17. shows the comparison of shaft torque with proposed <t>controller,</t> as we know that the generated torque in the SBSRM is proportional to the phase winding currents and hence the generated torque is at starting is about 0.8 N.m, which is same as that of the conventional PID controller but at the steady state condition the torque generated by the proposed controller generates optimized torque for the same load. Fig.18 shows that shaft displacements at both suspending and rotating conditions applied simultaneously. As we know that both suspension and torque controlling are completely individual to each other hence the injection of torque winding currents in case (ii) has not been showing any affect on the suspension winding currents, even though little fluctuations can be observed, the shaft is still in center position. These fluctuations are now reduced with the proposed controller as shown in the fig.19.
Simulink Controller Algorithm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc smith predictive controller
Fig.17. shows the comparison of shaft torque with proposed <t>controller,</t> as we know that the generated torque in the SBSRM is proportional to the phase winding currents and hence the generated torque is at starting is about 0.8 N.m, which is same as that of the conventional PID controller but at the steady state condition the torque generated by the proposed controller generates optimized torque for the same load. Fig.18 shows that shaft displacements at both suspending and rotating conditions applied simultaneously. As we know that both suspension and torque controlling are completely individual to each other hence the injection of torque winding currents in case (ii) has not been showing any affect on the suspension winding currents, even though little fluctuations can be observed, the shaft is still in center position. These fluctuations are now reduced with the proposed controller as shown in the fig.19.
Smith Predictive Controller, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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MathWorks Inc test microgrid
Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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MathWorks Inc motor control algorithm
Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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MathWorks Inc matlab/simulink based controller algorithms
Fig. 1 <t>Microgrid</t> control structure based on distributed algorithms
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Image Search Results


Fig.17. shows the comparison of shaft torque with proposed controller, as we know that the generated torque in the SBSRM is proportional to the phase winding currents and hence the generated torque is at starting is about 0.8 N.m, which is same as that of the conventional PID controller but at the steady state condition the torque generated by the proposed controller generates optimized torque for the same load. Fig.18 shows that shaft displacements at both suspending and rotating conditions applied simultaneously. As we know that both suspension and torque controlling are completely individual to each other hence the injection of torque winding currents in case (ii) has not been showing any affect on the suspension winding currents, even though little fluctuations can be observed, the shaft is still in center position. These fluctuations are now reduced with the proposed controller as shown in the fig.19.

Journal: International Journal of Engineering & Technology

Article Title: Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor

doi: 10.14419/ijet.v7i2.21.11847

Figure Lengend Snippet: Fig.17. shows the comparison of shaft torque with proposed controller, as we know that the generated torque in the SBSRM is proportional to the phase winding currents and hence the generated torque is at starting is about 0.8 N.m, which is same as that of the conventional PID controller but at the steady state condition the torque generated by the proposed controller generates optimized torque for the same load. Fig.18 shows that shaft displacements at both suspending and rotating conditions applied simultaneously. As we know that both suspension and torque controlling are completely individual to each other hence the injection of torque winding currents in case (ii) has not been showing any affect on the suspension winding currents, even though little fluctuations can be observed, the shaft is still in center position. These fluctuations are now reduced with the proposed controller as shown in the fig.19.

Article Snippet: Using this RTIO, one can skip the process of generating the C-code form the Simulink building blocks, hence every change in Simulink controller algorithm shows the instantaneous effect on real-time hardware.

Techniques: Comparison, Generated, Suspension, Injection

Fig.16: Comparison of speeds with proposed controller

Journal: International Journal of Engineering & Technology

Article Title: Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor

doi: 10.14419/ijet.v7i2.21.11847

Figure Lengend Snippet: Fig.16: Comparison of speeds with proposed controller

Article Snippet: Using this RTIO, one can skip the process of generating the C-code form the Simulink building blocks, hence every change in Simulink controller algorithm shows the instantaneous effect on real-time hardware.

Techniques: Comparison

Fig.18: Comparison of shaft torque with proposed controller

Journal: International Journal of Engineering & Technology

Article Title: Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor

doi: 10.14419/ijet.v7i2.21.11847

Figure Lengend Snippet: Fig.18: Comparison of shaft torque with proposed controller

Article Snippet: Using this RTIO, one can skip the process of generating the C-code form the Simulink building blocks, hence every change in Simulink controller algorithm shows the instantaneous effect on real-time hardware.

Techniques: Comparison

Fig.21. Shows that X-directional and Y-directional displacements for suspension algorithm alone, it can be observed that these displacements have been settled at zero positions. Fig. 22(a) shows that displacements with PID controller when suspension load is decreased suddenly form 2N to 1N, it can be observed that conventional PID controller is showing unstable performance. Fig. 22(b) shows displacements with proposed parallel fuzzy controller, it can be observed that proposed controller exhibits stable operation when comparing with the conventional PID controller when the suspension load is suddenly decreased from 2N to 1N. Fig. 24 (a) shows that the speed of the SBSRM with the conventional PID controller, it can be seen that it has been settled to the desired speed with more oscillations and settled after four time divisions. Fig. 24(b) shows the A-Phase and B-Phase torque winding currents with PID controller, it can observed that initially torque winding

Journal: International Journal of Engineering & Technology

Article Title: Real- time implementation of parallel type fuzzy- PID controller for effective control of hybrid Pole self bearing Switched reluctance motor

doi: 10.14419/ijet.v7i2.21.11847

Figure Lengend Snippet: Fig.21. Shows that X-directional and Y-directional displacements for suspension algorithm alone, it can be observed that these displacements have been settled at zero positions. Fig. 22(a) shows that displacements with PID controller when suspension load is decreased suddenly form 2N to 1N, it can be observed that conventional PID controller is showing unstable performance. Fig. 22(b) shows displacements with proposed parallel fuzzy controller, it can be observed that proposed controller exhibits stable operation when comparing with the conventional PID controller when the suspension load is suddenly decreased from 2N to 1N. Fig. 24 (a) shows that the speed of the SBSRM with the conventional PID controller, it can be seen that it has been settled to the desired speed with more oscillations and settled after four time divisions. Fig. 24(b) shows the A-Phase and B-Phase torque winding currents with PID controller, it can observed that initially torque winding

Article Snippet: Using this RTIO, one can skip the process of generating the C-code form the Simulink building blocks, hence every change in Simulink controller algorithm shows the instantaneous effect on real-time hardware.

Techniques: Suspension

Fig. 1 Microgrid control structure based on distributed algorithms

Journal: IET Renewable Power Generation

Article Title: Resilient cooperative control of AC microgrids considering relative state‐dependent noises and communication time‐delays

doi: 10.1049/iet-rpg.2019.1180

Figure Lengend Snippet: Fig. 1 Microgrid control structure based on distributed algorithms

Article Snippet: The performance assessment of the presented control algorithm is evaluated through simulations on a test microgrid in MATLAB/Simulink software.

Techniques: Control

Fig. 3 Single-line diagram of the test microgrid system Table 1 Parameters of the test microgrid system

Journal: IET Renewable Power Generation

Article Title: Resilient cooperative control of AC microgrids considering relative state‐dependent noises and communication time‐delays

doi: 10.1049/iet-rpg.2019.1180

Figure Lengend Snippet: Fig. 3 Single-line diagram of the test microgrid system Table 1 Parameters of the test microgrid system

Article Snippet: The performance assessment of the presented control algorithm is evaluated through simulations on a test microgrid in MATLAB/Simulink software.

Techniques: