Modeling and Analyses of the N-link PenduBot
As the foundation of the multi-link PenduBot control, the mathematical model should be established first. Based on the method of kinematics and dynamics, the N-link PenduBot mathematical models are established in this paper, including the nonlinear model, the linear model and the generalized model which considers the motor characteristic. The natural characteristic of the system is analyzed. By using the condition number of the controllability matrix, the control difficulty for the higher order systems is compared. From the comparison result, the control law of the different links PenduBot is obtained. Increasing with the count of PenduBot links, the control difficulty is growing dramatically. Only a strong control measure can make it stable. So, further study on multi-link PenduBot can be carried on.
Research on Control Problem of PenduBot Based on PSO Algorithm
PenduBot is a new experiment object in the control theory and a typical representation in the underactuated robot, so it is the research focus of control and robot domain. It is known for its strongly nonlinear and naturally unstable properties. To stabilize the PenduBot and verify the control abilities of the algorithm on strongly nonlinear and naturally unstable properties, the thesis presents the purpose that the state-feedback matrixes can be optimized by new bionics algorithm PSO. Based on the introduction of standard PSO algorithm, how to select the position and velocity evolution equations parameters and fitness function became a great emphasis. Next, the simulations were done on the linearized PenduBot model in MATLAB environment by PSO and LQR algorithm separately, and the results were compared. Finally, the comparison results proved the PSO advantages. The expected goal was achieved.
Modelling of the rotary inverted pendulum system
This paper presents an approach on the bond graph modelling applied to a Quanser rotary inverted pendulum experiment followed by the computation of the mathematical model. The system was decomposed into subsystems that were modelled separately. The obtained subsystems generated submodels and the overall model was then built up by combining these separate structures. The model was built up and simulated using BondSim environment.