A Reset‐Time Dependent Approach for Stability Analysis of Nonlinear Reset Control Systems

A reset mechanism in controller can affect the stability property of a closed loop control system. In a simple word, there are stable reset control systems with unstable base‐systems and also unstable reset systems with stable base‐systems. The Lyapunov stability theory is a strong tool to investigate the stability of a nonlinear system. In this paper, based on the well‐known Lyapunov stability concept, some stability conditions for nonlinear reset control systems are addressed. These conditions are dependent on the reset‐times and hence the reset‐time intervals are explicitly emerged in the stability conditions. Some applications of these results are used in numerical examples to show the effectiveness of the proposed approach.     

Immersion and invariance adaptive control of a class of nonlinear systems with unknown control direction

The goal of this article is to extend the adaptive control problem of parametric strict feedback form nonlinear systems, using immersion and invariance to the case of unknown, possibly, time-varying control direction. The idea is to immerse a target system in ? ^n?1, which is stabilised through the design of virtual controllers, into an extended system in ? ^n+p . The designed controller takes advantage of the well-known Nussbaum functions to deal with the unknown sign of input multiplier and is designed through the manifold dynamics belonging to ? ^p+1. The effectiveness of the proposed method is shown through a simulation example and is also compared to the classical adaptive backstepping approach with an unknown control direction.     

Improving GPS/INS Integration Using FIKF‐Filtered Innovation Kalman Filter

The Kalman Filter (KF) is the most popular approach in sensor fusion and navigation applications. Improving the accuracy of estimation may increase the navigation accuracy. KF is the best choice for minimum variance optimal estimation, but it is not the best approach to improve some of the main and classic features such as steady‐state error reduction. These features are more important than optimality for applications like accuracy improvement in navigation. Also, these features and their solutions have been addressed in classic control techniques as a widely known subject. Therefore, some similar improvements in the estimation problem are discussed in the literature. These studies try to conserve the optimality in the covariance of the error, but classic and optimal features cannot be achieved simultaneously. Hence, these methods are not as efficient as expected. This paper intends to improve the estimation of position and velocity in navigation problems by integrating classic and modern control techniques. Beside the classic features and advantages, it may be seen that the resulted covariance of error is not minimum anymore, but it is comparable with the optimal methods.     

A new method for integrating analog to digital conversion based on error reduction

This paper presents a method for eliminating errors of electronic components in integrating analog to digital converters. Offset error in integrator, comparator and amplifiers of dual-slope converter is a great limitation for increasing the resolution of this type of analog to digital converter. The paper proposes an idea that effectively eliminates offset error of integrator and reduces errors of other components. To validate the efficiency of the proposed method, simulation and experimental results are represented in this paper. As a result, this method can potentially allow a cost-effective design of high-resolution ADCs with low performance Op-Amps.