In this paper a new controller parameter adaptation rule is proposed. The proposed adaptation rule is derived via unfalsification control theory and gradient method without imposing many assumptions on the plant. Convergence of parameter is discussed. Numerical examples and simulation results are also provided.

This paper is concerned with adaptive stabilization of an undamped non-linear beam with disturbed outputs by boundary feedback control. The adaptive controller is constructed by the concept of high-gain adaptive feedback and the estimation mechanism for the unknown parameters of the measurement noise. The global existence and uniqueness of the solution of the closed-loop system is justified. Th...

Penalty methods have been commonly used to improve the generalization performance of feedforward neural networks and to control the magnitude of the network weights. Weight boundedness and convergence results are presented for the batch BP algorithm with penalty for training feedforward neural networks with a hidden layer. A key point of the proofs is the monotonicity of the error function with...

In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a balance between convergence speed and robustness. Results show that the algorithm is capable of learning the required trajectory in very few iterations. However at this convergence...

In system identification and adaptive control, the problem of designing strictly positive real (SPR) transfer functions in the presence of uncertain adaptation parameters is essential for stability and convergence in a group of parameter adaptation algorithms. This paper proposes a convex optimization approach to address the robust SPR problem. Besides achieving the robust SPR condition, the pr...

A penalty/least-squares method for optimal control problems for ®rst-order elliptic systems is considered wherein the constraint equations are enforced via penalization. The convergence, as the penalty parameter tends to zero, of the solution to the penalized optimal control problem to that of the unpenalized one is demonstrated as is the convergence of a gradient method for determining solutio...

Model Predictive Control and Identification is an adaptive control technique which solves an online optimization problem to find process inputs for dual control problem. Its main goal is to bring robustness, to Model Predictive Control, increase the capability to handle uncertainties and time varying parameters in the processes. Theoretical properties, such as feasibility of the optimization pr...

2 Statement of the Problem A large class of problems in parameter estimation concerns systems where parameters occur nonlinearly. In [1]-[5], a stability framework for identification and control of such systems has been established. We address the issue of parameter convergence in such systems in this paper. Sufficient conditions under which parameter estimates converge to their true values are...

An algorithm for adaptively controlling genetic algorithm parameter (GAP) coding using fuzzy rules is presented. The fuzzy GAP coding algorithm is compared to the dynamic parameter encoding scheme proposed by Schraudolph and Belew. The performance of the algorithm on a hydraulic brake emulator parameter identification problem is investigated. Fuzzy GAP coding control is shown to dramatically in...