MATLAB Implementation of Robust Controller Design
Robust Controller Design Using RBF Networks - This approach leverages Radial Basis Function (RBF) networks to approximate arbitrary nonlinear relationships. The objective is to minimize the sum of squared errors, aligning with nonlinear Principal Component Analysis (PCA) goals. The nonlinear PCA model can be implemented using two separate RBF networks: one for nonlinear forward transformation and another for inverse transformation. Each RBF network is a three-layer feedforward architecture with radial basis functions as activation functions in the hidden layer. The first network maps high-dimensional data to a low-dimensional space (Figure 4), while the second network reconstructs the original high-dimensional data from the low-dimensional representation (Figure 5). Both networks require independent training to ensure optimal performance.