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The angles generated by the actuators in comparison with the desired angles can be seen in Fig. 11 and Fig. 12 where the controller yields a smooth transition from the initial angles to the final angles by the torques T, and T, shown in Fig.13 and Fig. 14.

Figure 11 The angles generated by the actuators in comparison with the desired angles can be seen in Fig. 11 and Fig. 12 where the controller yields a smooth transition from the initial angles to the final angles by the torques T, and T, shown in Fig.13 and Fig. 14.

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Fig. 1. Diagram of the two links robotic manipulator There are some important observations about the dynamics equations that are useful in the controller design. The matrices D and C are not independent, from this property a special condition is derived, resulting that D is symmetric. The state- space representation of the dynamics equations (1) is necessary due to neural network identification model needs the equation written in this form because the system could be decoupled into parts. They are given by :
Fig. 1. Diagram of the two links robotic manipulator There are some important observations about the dynamics equations that are useful in the controller design. The matrices D and C are not independent, from this property a special condition is derived, resulting that D is symmetric. The state- space representation of the dynamics equations (1) is necessary due to neural network identification model needs the equation written in this form because the system could be decoupled into parts. They are given by :
Fig. 2. Diagram of the neural network controller
Fig. 2. Diagram of the neural network controller
A neural network controller based in the model proposed by Patino and Liu [10] is implemented for the output tracking of the robotic manipulator. Considering a first order MIMO robotic plant the MRAC controller shown in Fig. 4 can be implemented.
A neural network controller based in the model proposed by Patino and Liu [10] is implemented for the output tracking of the robotic manipulator. Considering a first order MIMO robotic plant the MRAC controller shown in Fig. 4 can be implemented.
The purpose is to generate the necessary control input to the plant to move the links in the desired angle. Several simulations were performed in MATLAB and SIMULINK with the two neural network controllers to test the performance and compare the output response of each controller scheme. The parameters of the robot manipulator used in all the simulations are shown in table 1.
The purpose is to generate the necessary control input to the plant to move the links in the desired angle. Several simulations were performed in MATLAB and SIMULINK with the two neural network controllers to test the performance and compare the output response of each controller scheme. The parameters of the robot manipulator used in all the simulations are shown in table 1.
Fig. 7. Torque input for the two links robotic manipulator In Fig. 7 the input torque generated by the controller is shown, driving the output to a good approximated value according to the desired position. The output responses in radians are shown in Fig. 8 and Fig 9 where good approximations where obtained by the neural network controller and identification model. Better approximations can be obtained modifying the neural network architecture, but one of the advantages of this model is that a small number of nodes give good approximations.
Fig. 7. Torque input for the two links robotic manipulator In Fig. 7 the input torque generated by the controller is shown, driving the output to a good approximated value according to the desired position. The output responses in radians are shown in Fig. 8 and Fig 9 where good approximations where obtained by the neural network controller and identification model. Better approximations can be obtained modifying the neural network architecture, but one of the advantages of this model is that a small number of nodes give good approximations.
To test the controller and identification model sinusoidal input signals were used qq, = 0.9sin(4mt) + 0.3sin (147t) and Gaz = 1.7 sin(4nt) + 0.9 sin(14mt) as used in many robotic and mechanical systems.
To test the controller and identification model sinusoidal input signals were used qq, = 0.9sin(4mt) + 0.3sin (147t) and Gaz = 1.7 sin(4nt) + 0.9 sin(14mt) as used in many robotic and mechanical systems.
Fig. 6. Test output of the identification model with random inputs ‘ig. 5. Random inputs training signals for the robotic systems
Fig. 6. Test output of the identification model with random inputs ‘ig. 5. Random inputs training signals for the robotic systems
Fig. 10. Output q, of the robotic manipulator in radians A point to point simulation was done where the end effector is moved from the initial position (0.75,0) m_ to a desired point defined by the reference angles used by the neural network controller. Therefore, the performance of the controller can be test with smoother trajectories.
Fig. 10. Output q, of the robotic manipulator in radians A point to point simulation was done where the end effector is moved from the initial position (0.75,0) m_ to a desired point defined by the reference angles used by the neural network controller. Therefore, the performance of the controller can be test with smoother trajectories.
Fig. 8. Output q, of the robotic manipulator in radians
Fig. 8. Output q, of the robotic manipulator in radians
Fig. 9. Output q2 of the robotic manipulator in radians
Fig. 9. Output q2 of the robotic manipulator in radians
Fig. 12. Output qz of the robotic manipulator in radians
Fig. 12. Output qz of the robotic manipulator in radians
Fig. 13. Torque T, yield by the neural network controller
Fig. 13. Torque T, yield by the neural network controller
Fig. 17. Final position of the two link manipulator after the simulation
Fig. 17. Final position of the two link manipulator after the simulation
Fig. 15. Trajectory of the end effector in the x axis
Fig. 15. Trajectory of the end effector in the x axis
Fig. 18. Torque generated by the adaptive NN and PD controllers respectively The torque generated by the output of the adaptive neural network controller is shown in Fig.18, where the torque generated by the PD controller is shown either.
Fig. 18. Torque generated by the adaptive NN and PD controllers respectively The torque generated by the output of the adaptive neural network controller is shown in Fig.18, where the torque generated by the PD controller is shown either.
Fig. 11. Output q, of the robotic manipulator in radians
Fig. 11. Output q, of the robotic manipulator in radians
Fig. 12. Output qz of the robotic manipulator in radians
Fig. 12. Output qz of the robotic manipulator in radians
Fig. 14. Norm of the weights of the adaptive NN controller V. Conclusions
Fig. 14. Norm of the weights of the adaptive NN controller V. Conclusions
Related topics:
RoboticsArtificial IntelligenceEnvironmental InformaticsMulti Agent Systems and Technologies
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