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signal u(t) from dataset DO5 on the computed transfer function. PID Control Law for Estimated Transfer Function

Figure 52 signal u(t) from dataset DO5 on the computed transfer function. PID Control Law for Estimated Transfer Function

Related Figures (95)

Figure 1.2: Hydrofoil Configurations: surface piercing and fully submerged. (IHS, 1998) piercing and fully submerged, as shown in Figure 1.2.
Figure 1.2: Hydrofoil Configurations: surface piercing and fully submerged. (IHS, 1998) piercing and fully submerged, as shown in Figure 1.2.
Table 2.1: 6 DOF standard motion notation used for marine vehicles. (SNAME, 1950) To determine the equations of motion, two reference frames are considered:
Table 2.1: 6 DOF standard motion notation used for marine vehicles. (SNAME, 1950) To determine the equations of motion, two reference frames are considered:
Let the position-orientation vector 7 with respect to the earth-fixed frame be defined and the linear-angular velocity vector 1 with respect to the body-fixed frame be
Let the position-orientation vector 7 with respect to the earth-fixed frame be defined and the linear-angular velocity vector 1 with respect to the body-fixed frame be
dynamics of a marine vehicle can be derived (Fossen, 1994): matrix and 7 is a generalized vector of forces and moments, defined as which can be be linearly superposed by different components, based on the gen-
dynamics of a marine vehicle can be derived (Fossen, 1994): matrix and 7 is a generalized vector of forces and moments, defined as which can be be linearly superposed by different components, based on the gen-
adding a reference precompensator NV, as shown in Figure 2.4.
adding a reference precompensator NV, as shown in Figure 2.4.
shown on Figure 3.1. Some definitions are made about the parts of Figure 3.1, as follows: 3.1. General System
shown on Figure 3.1. Some definitions are made about the parts of Figure 3.1, as follows: 3.1. General System
equires cooling fan for temperature contro
equires cooling fan for temperature contro
present work.
present work.
for experimental tests is shown in Figure 3.6. Figure 3.6: Computer Architecture for Experimental Tests
for experimental tests is shown in Figure 3.6. Figure 3.6: Computer Architecture for Experimental Tests
Raspberry Pi logging pseudo-algorithm
Raspberry Pi logging pseudo-algorithm
for physical connection. The variable to be controled is hydrofoil angle, shown in drofoil system, which consisted of hydrofoil, linear actuator, and mechanical parts
for physical connection. The variable to be controled is hydrofoil angle, shown in drofoil system, which consisted of hydrofoil, linear actuator, and mechanical parts
The second part consisted in identifying the black-box transfer function. An
The second part consisted in identifying the black-box transfer function. An
Thus a driver motor is usually required. Figure 4.3 shows how it works.
Thus a driver motor is usually required. Figure 4.3 shows how it works.
Table 4.1: Driver-actuator characterization Since there is a non-linearity (range) resulting in zero speed to the linear
Table 4.1: Driver-actuator characterization Since there is a non-linearity (range) resulting in zero speed to the linear
Table 4.2: Foil Angle Calibration
Table 4.2: Foil Angle Calibration
to a value of zero (See Figure 4.4).
to a value of zero (See Figure 4.4).
Table 4.3: Foil Angle Calibration Statistics table lists some useful statistics: Both MSE and standard deviation values are sufficiently small. Maximum
Table 4.3: Foil Angle Calibration Statistics table lists some useful statistics: Both MSE and standard deviation values are sufficiently small. Maximum
part that the Foil Angle potentiometer was mounted on (See Figure 4.11).
part that the Foil Angle potentiometer was mounted on (See Figure 4.11).
Arduino’s pseudo-algorithm for filter analysis experiment are listed below.
Arduino’s pseudo-algorithm for filter analysis experiment are listed below.
Figure 4.18: Responsiveness of 10 and 20 Hz one pole low-pass filters
Figure 4.18: Responsiveness of 10 and 20 Hz one pole low-pass filters
Table 4.4: Filter Statistics
Table 4.4: Filter Statistics
Figure 4.21: Noise rejection for 10 and 20 Hz one pole low-pass filters
Figure 4.21: Noise rejection for 10 and 20 Hz one pole low-pass filters
Table 4.5: Preliminar datasets
Table 4.5: Preliminar datasets
Table 4.6: First-Order Estimated Models
Table 4.6: First-Order Estimated Models
Table 4.7: Second-Order Estimated Models
Table 4.7: Second-Order Estimated Models
Figure 4.29: Comparison between actual and simulated response
Figure 4.29: Comparison between actual and simulated response
Figure 4.32: Comparison between actual and simulated response
Figure 4.32: Comparison between actual and simulated response
Figure 4.33: Comparison between actual and simulated response
Figure 4.33: Comparison between actual and simulated response
outcome of this experiment.
outcome of this experiment.
trollers. Figure 4.38: Step response for P controlled plant
trollers. Figure 4.38: Step response for P controlled plant
Table 4.8: PID Control Parameters Figures 4.38 to 4.40 show the unit step response for each of these con-
Table 4.8: PID Control Parameters Figures 4.38 to 4.40 show the unit step response for each of these con-
be obtained with smaller control gains for the estimated transfer function.
be obtained with smaller control gains for the estimated transfer function.
For K, = 34.68, Kk; = 4.25.
For K, = 34.68, Kk; = 4.25.
For K, = 45.82, K; = 41.07, Ka = 0.95.
For K, = 45.82, K; = 41.07, Ka = 0.95.
4.44 to 4.46.
4.44 to 4.46.
State-space controller
State-space controller
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