In most adaptive control systems with unknown parameters, the adaptive laws are usually designed based on the predictor/observer error or control error. In this paper, we propose a novel adaptive control scheme for nonlinear robotic systems by incorporating the parameter error into the adaptive law, which leads to guaranteed estimation performance. By carrying out filter operations, the robotic...

The deconvolution of blurred and noisy satellite images is an ill-posed inverse problem, which can be regularized within a Bayesian context by using an a priori model of the reconstructed solution. Homogeneous regularization models do not provide sufficiently satisfactory results, since real satellite data show spatially variant characteristics. We propose here to use an inhomogeneous model, an...

The paper introduces novel techniques for achieving global convergence results and improving transient performance in stochastic adaptive control. For example, there is introduced switching between a least squares and an extended least squares parameter estimation algorithm according to an ill-conditioning measure, and there is appropriate selection of external persistently exciting signals, th...

This paper provides a solution to the convergence of the parameter estimates to their true values in an ideal adaptive regulation context. The key design feature consists in the use of an asymptotically vanishing internally generated exciting sequence. 1. Introduction IN THIS paper, the following question is addressed: given a known order linear time invariant system, how does one design an ada...

In a general Adaptive Robust Control (ARC) framework, the emphasis is always on the guaranteed transient performance and accurate trajectory tracking in the presence of uncertain nonlinearity and parametric uncertainties. However, when secondary purposes such as system health monitoring and prognosis are of equal importance, intelligent integration of output tracking performance oriented direct...

This paper develops an adaptive partial differential equation (PDE) observer for battery state-of-charge (SOC) and state-of-health (SOH) estimation. Real-time state and parameter information enables operation near physical limits without compromising durability, thereby unlocking the full potential of battery energy storage. SOC/SOH estimation is technically challenging because battery dynamics...

This paper develops an adaptive PDE observer for battery state-of-charge (SOC) and state-of-health (SOH) estimation. Real-time state and parameter information enables operation near physical limits without compromising durability, thereby unlocking the full potential of battery energy storage. SOC/SOH estimation is technically challenging because battery dynamics are governed by electrochemical...

The paper focuses on the synthesis of adaptive robust controllers that achieve not only excellent output tracking performance but also accurate parameter estimations for secondary purposes such as machine health monitoring and prognostics. Such an objective is accomplished through an intelligent integration of the output tracking performance oriented direct adaptive robust control (DARC) design...