In existing neural network (NN) learning control methods, the trajectory of NN inputs must be recurrent to satisfy a stringent condition termed persistent excitation (PE) so that NN parameter convergence is obtainable. This paper focuses on command-filtered backstepping adaptive control for a class of strict-feedback nonlinear systems with functional uncertainties, where an NN composite learnin...

The problem of sampled-data (SD) based adaptive linear quadratic (LQ) optimal control is considered for linear stochastic continuous-time systems with unknown parameters and disturbances. To overcome the difficulties caused by the unknown parameters and incompleteness of the state information, and to probe into the influence of sample size on system performance, a cost-biased parameter estimato...

In this study, a new mechanism that adapts the mutation rate for each locus on the chromosomes, based on feedback obtained from the current population is proposed. Through tests using the one-max problem, it is shown that the proposed scheme improves convergence rate. Further tests are performed using the 4-Peaks and multiple knapsack test problems to compare the performance of the proposed app...

Using geometric concepts from observability theory for nonlinear systems, we propose an approach for parameter estimation for linearly and nonlinearly parameterized systems. The proposed approach relies on extending a parameter estimation problem to a state estimation problem by introducing the parameters as auxiliary state variables. Applying tools from geometric nonlinear control theory we es...

Combined control variates and importance sampling variance reduction and its two-fold optimality are investigated. Two-time-scale stochastic approximation algorithm is applied in parameter search for the combination and almost sure convergence of the algorithm to the unique optimum is proved. The parameter search procedure is further incorporated into adaptive Monte Carlo simulation, and its la...

Abstract: The aim of the present paper is to integrate a recurrent neural network in two schemes of real-time soft computing neural control. There are applied the following control schemes: an indirect and a direct trajectory tracking control, using the state and parameter information, given by an identification recurrent neural network. The applicability of the proposed control schemes is conf...

A method for control of mechanical systems under phase constraints, applicable to energy control of Hamiltonian systems is proposed. The constrained energy control problem for two pendulums by a single control action is studied both analytically and numerically. It is shown that for a proper choice of penalty parameter of the algorithm any energy level for the one pendulum under any specified c...

A new approach to the planning and analysis of the successful convergence of assembly is presented. The convergence is refered to as p-convergence, since it is derived within a probability framework. The assembly is modeled as a hybrid dynamic system (HDS) accounting for the presence of the discrete change of contact and the continuous movement during the process. The continuous property is the...