FLS-Based Nonuniform Trajectory Tracking AILC for Uncertain Nonlinear Systems with Nonsymmetric Dead-Zone Input and Initial State Error

This paper proposes an AILC method for uncertain nonlinear system to solve different target tracking problems. The method uses fuzzy logic systems (FLS) to approximate every uncertain term in systems. All closed-loop signals are bounded on 0 , T according to the Lyapunov theory. A time-varying boundary layer and a typical convergent series are introduced to handle initial state error, unknown bounds of errors, and nonuniform target tracking, respectively. The result is that the tracking error’s norm can converge to a small neighborhood along iteration increasing asymptotically. Finally, the simulation results of mass-spring mechanical system show the correctness of the theory and validity of the method.

RBFNN-Based Nonuniform Trajectory Tracking Adaptive Iterative Learning Control for Uncertain Nonlinear System with Continuous Nonlinearly Input

This paper proposes an adaptive iterative learning control (AILC) method for uncertain nonlinear system with continuous nonlinearly input to solve different target tracking problem. The method uses the radial basis function neural network (RBFNN) to approximate every uncertain term in systems. A time-varying boundary layer, a typical convergent series are introduced to deal with initial state error and unknown bounds of errors, respectively. The conclusion is that the tracking error can converge to a very small area with the number of iterations increasing. All closed-loop signals are bounded on finite-time interval 0 , T . Finally, the simulation result of mass-spring mechanical system shows the correctness of the theory and validity of the method.

Modeling of Uncertain Nonlinear System With Z-Numbers

In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.