Expert
936 Order Completed 98 % Response Time 36 Reviews Since 2017

Matlab Assignment Helper

Sydney, Australia

Craig D


Master’s Degree, Mechanical Engineering, The University of Queensland

Profession

Matlab tutor and assignment helper

Skills

I have worked as an online Matlab tutor and assignment helper for more than ten years. Before I became a full-time employee of Matlab Assignment Experts, I used to render my services part-time as a freelancer but my passion for working with students pushed me to work as a full-time academic assistant. My area of expertise is Simulink, digital signal processing, classification algorithms simulation, implementation of image processing algorithms, statistical pattern recognition, and machine learning. It would be my pleasure to collaborate with you on your projects.

Get Free Quote


 Your Order has been sent successfully. We will contact you as soon as possible.
Error: Please try again

Cross-Coupling Effect in Simulink 

Cross-coupling is the influence between the two axes. The cross-coupling effect arises due to internal rotating parts of the system that have a net angular momentum about a direction. The cross-coupling effect, in this case, produces moment proportional to the angular yawing velocity and yawing moment proportional angular rolling velocity. Cross-coupling can be minimized by using gimbal momentum wheels. Wheels reduce the cross-coupling effect by filtering big changes in momentum or by control moment gyros and reaction wheels. On Im11. Are shown plots of the angular velocity.

Cross-Coupling Effect in Simulink                

Im11. Angular velocity 

On the image Im12. We can see the Simulink model with linear PD control. Derivative control improves reaction time, derivative jump instantly, and suppress the disturbance at the static working regime. Linear Derivative control should suppress linear disturbance around the working point of a nonlinear system. Cross-Coupling Effect in Simulink            

Im12. Simulink model of the object and linear PD control system 

Plots are shown on images Im13, Im14, and Im15.

Cross-Coupling Effect in Simulink   Cross-Coupling Effect in Simulink   Cross-Coupling Effect in Simulink                                              

    The system is destabilized. We get out of the working point of the linear control system (caused by object non-linearity). We can try to tune control gains again but it is not guaranteed that we can succeed. If we have to tune control gains again, we have to check system properties (equilibrium states, stability and reality of those states, etc…). In this case, performance can not be improved.

Cross-Coupling Effect in Simulink  

Cross-Coupling Effect in Simulink

Cross-Coupling Effect in Simulink  

 After implementing the non-linear control, the system was stable. After changing the desired points, we can easily re-tune control gains. The Nonlinear Simulink model is shown on image Im19. Below the model image, we can see output comparation.


Cross-Coupling Effect in Simulink          

Im19. System model

Cross-Coupling Effect in Simulink  

Simulink model of the closed-loop control for UAV is shown on the image Im22 and on Im23 is the position control system.

Cross-Coupling Effect in Simulink              

Im22. UAV linear control Simulink model

Cross-Coupling Effect in Simulink              

Im23. UAV position control system On images below are shown plots from the task.

Cross-Coupling Effect in Simulink Cross-Coupling Effect in Simulink   Cross-Coupling Effect in Simulink   Cross-Coupling Effect in Simulink                                                          

I did not have success in tuning control parameters for this purpose. UAV is trying to follow the eight-shape but it is not good enough. What is important, the system is stable but it is not giving a nominal working regime. The Simulink model is shown on image 24, on the images below are shown plots from the task.

Cross-Coupling Effect in Simulink

   I'm. 24

Cross-Coupling Effect in Simulink   Cross-Coupling Effect in Simulink   Cross-Coupling Effect in Simulink

Robotics: Analysis and Control

The car subsystem of the Simulink model is shown on Im1.

Analysis and Control            

Im1. Car kinematic subsystem 

Closed-loop control of the car Simulink model is shown on the Im2. We have to tune control gains to avoid gain destabilization. Gains have to be < 1, in different proportions we can balance between static error and reaction time.

Analysis and Control          

Im2. Closed-loop control system for car

Results of the simulation are shown on images Im3 and Im4 (states and inputs respectively).


Analysis and Control                       Analysis and Control                    

in (b). Include plots of the states and inputs, and explain any differences in performance. 


Plots of the states and inputs are shown on images Im5 and Im6. After adding the non- linearity saturation type, the system is unstable and can not reach the reference position. In reality, every system has saturation, and its part of his physical description. Saturation can affect system stability, control winding up, etc. To calculate proper control, we have to include all saturation of the object and control system.

Analysis and Control                     Analysis and Control                    

b)Using the model from (c) and the initial conditions from (b), set up a control to follow a line

specified as aX + bY + c = 0 with a = 2, b = 1 and c = −25. Provide a plot of the states and input γ and list the control gains you used. Show that (x, y) converges to the specified line. The Simulink model is shown on Im7. Analysis and Control                

Plots are shown on Im8, Im9, and Im10.

Analysis and Control                   Analysis and Control                     Analysis and Control