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.
Design of control system with limited feedback gain based on CPSO algorithm
To solve the problem that the feedback gain is too high during high-order control system design, we propose the design idea of limited feedback gain. Using the chaos particle swarm algorithm (CPSO), we realized the process of limited feedback gain control system design. Taking the three-link PenduBot controller as an example, the design process is illustrated in detail. Simulation and physical control results indicate that the design method is effective, the gain we get is relatively low, and could be easily implemented. The design idea provides a new and effective method for high-order control system design.
Particle Swarn Optimized Adaptive Dynamic Programming
Particle swarm optimization is used for the training of the action network and critic network of the adaptive dynamic programming approach. The typical structures of the adaptive dynamic programming and particle swarm optimization are adopted for comparison to other learning algorithms such as gradient descent method. Besides simulation on the balancing of a cart pole plant, a more complex plant pendulum robot (pendubot) is tested for the learning performance. Compared to traditional adaptive dynamic programming approaches, the proposed evolutionary learning strategy is verified as faster convergence and higher efficiency. Furthermore, the structure becomes simple because the plant model does not need to be identified beforehand