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.
Multiobjective control of power plants using particle swarm optimization techniques
Multiobjective optimal power plant operation requires an optimal mapping between unit load demand and pressure set point in a fossil fuel power unit (FFPU). In general, the optimization problem with varying unit load demand cannot be solved using a fixed nonlinear mapping. This paper presents a modern heuristic method, particle swarm optimization (PSO), to realize the optimal mapping by searching for the best solution to the multiobjective optimization problem, where the objective functions are given with preferences. This optimization procedure is used to design the reference governor for the control system. This approach provides the means to specify optimal set points for controllers under a diversity of operating scenarios. Variations of the PSO technique, hybrid PSO, evolutionary PSO, and constriction factor approach are applied to the FFPU, and the comparison is made among the PSO techniques and genetic algorithm.