Name
Abstract | ||
|---|---|---|
The control of dish-like underwater robot motion is complex. It involves many kinds of influencing factors and it's also a nonlinear process. The model of attitude motion control is very important for the accuracy control and self adapting predictive control. For establishing the attitude motion model and predicting the attitude, SVM algorithm was used to construct a MIMO identifier in this paper. Moreover, in order to improve the effect of the identification and prediction, the grid search method was adopted to optimize the key parameter C and g in SVM. At last the effects were contrasted with GA and PSO optimized SVM algorithm by the data from the experiments in the pool, the results proved the superiority of grid search method in both calculating time and optimizing results. The results show the well performance of this GS-SVM on the identification and prediction for the attitude of dish-like underwater robot. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CSO.2012.189 | CSO |
Keywords | Field | DocType |
predictive control,grid search optimized svm,dish-like underwater robot attitude,mimo identifier,pso optimized svm algorithm,accuracy control,attitude motion model,dish-like underwater robot,svm algorithm,grid search method,attitude motion control,dish-like underwater robot motion,genetic algorithms,mobile robots,mathematical model,robot kinematics,optimization,support vector machines,attitude control,grid search,mimo,motion control,svm,prediction | Data mining,Motion control,Computer science,Control theory,Model predictive control,Attitude control,Artificial intelligence,Genetic algorithm,Hyperparameter optimization,Support vector machine,Robot kinematics,Machine learning,Mobile robot | Conference |
Citations | PageRank | References |
4 | 0.40 | 0 |
Authors | ||
4 | ||