Thomas Gustafsson

Adaptive Kalman filtering based navigation: An IMU/GPS integration approach

This paper investigates on the development and implementation of a high integrity navigation system based on the combined use of the Global Positioning System (GPS) and an inertial measurement unit (IMU) for land vehicle applications. The complementary properties of the GPS and the INS have motivated several works dealing with their fusion by using a Kalman Filter. The conventional kalman filter has a fix error covariance matrix in all times of processing. Multi-sensor based navigation system that is implemented in this paper is called data synchronization. Also, multi-rate operations that are compared with conventional Kalman filtering has fix error covariance matrix. Therefore, when GPS outage occurred we have improper treat by kalman filter. In this paper we present an Adaptive method instead of conventional methods. It is shown that proposed method has a better performance rather than conventional method. Experimental results show the effectiveness of the GPS/INS integrated system.

Nonlinear observer design for a class of Lipschitz time-delay systems with unknown inputs: LMI approach

In this paper, we consider a method to design a full-order nonlinear observer for a class of nonlinear time-delay systems with unknown inputs. Based on Lyapunov-Krasovskii functional, we derive a sufficient condition for existence of the designed observer which requires solving a set of nonlinear matrix inequalities. Then, the achieved condition is formulated in terms of two linear matrix inequalities (LMIs). Finally, the proposed observer is illustrated with an example.

Robust state estimation and unknown inputs reconstruction for a class of nonlinear systems: Multiobjective approach

We consider a novel method to design H∞H∞ observers for a class of uncertain nonlinear systems subject to unknown inputs. First, the main system dynamics are rewritten as an augmented system with state vector including both the state vector of the main system and the unknown inputs. Then, we design a H∞H∞ reduced-order observer to estimate both state variables and unknown inputs simultaneously. Based on a Lyapunov functional, we derive a sufficient condition for existence of the designed observer which requires solving a nonlinear matrix inequality. To facilitate the observer design, the achieved condition is formulated in terms of a set of linear matrix inequalities (LMI). By extending the proposed method to a multiobjective optimization problem, the maximum bound of the uncertainty and the minimum value of the disturbance attenuation level are found. Finally, the proposed observer is illustrated with an example.

Automatic control of unmanned cranes at the Pasir Panjang terminal

The overhead bridge cranes (OHBC) at the Pasir Panjang terminal in the Port of Singapore are operated remotely. Each operator can handle up to six cranes. This is possible due to the highly automated system that controls the cranes. This paper describes the control system that is implemented in the terminal.

Regsim: a software tool for real time control and simulation

This paper presents a software tool, Regsim, that bridges the gap between simulation and real world experiments of control systems. The ideas behind Regsim are that it should be interactive, easy to use and that it should behave uniformly irrespective of the use, whether it is simulation or real-time control.

Extremum seeking control based on phasor estimation

Abstract We present an extremum seeking control algorithm based on the estimation of the phasor of the perturbation frequency in the output of the plant. The phasor estimator is based on a continuous time Kalman filter, which is reduced into a variable gain observer by explicitly solving the special case of the Riccati equation. Local stability of the proposed algorithm for general non-linear dynamic systems using averaging and singular perturbations is presented for the single input case. The advantage of the presented algorithm is that it can be used on plants with large and even variable phase lag.

A Fault diagnosis scheme for three phase induction motors based on uncertainty bounds

The aim of this article is to present a fault diagnosis scheme for the case of squirrel-cage Three Phase Induction Motors based on uncertainty bounds violation conditions. The suggested scheme has the capability to diagnose two types of faults: a) broken rotor bar and b) short circuit in stator winding. The fault diagnosis is being performed through a two steps procedure. In the first step the parameters of the healthy induction motor are being identified by utilizing a Set Membership Identification approach, where corresponding uncertainty bounds are also being provided. In the second step, specific proposed bound violation conditions for the fault detection and fault diagnosis are being on-line evaluated during a sliding time window. Multiple simulation results are being presented that prove the efficacy of the proposed scheme towards fault detection and fault diagnosis.

H ∞ observer design for uncertain discrete-time nonlinear delay systems: LMI optimization approach

In this paper, we present a robust H∞ observer for a class of nonlinear and uncertain time-delayed systems. To design the proposed observer, the time delay does not have to be exactly known. With knowledge about upper and lower bound of the delay term, we can design an H∞ observer that guarantees asymptotic stability of the estimation error dynamics and is robust against time-varying parametric uncertainties. We show that the described problem can be solved in terms of linear matrix inequalities (LMIs). In addition, the admissible Lipschitz constant of the system is maximized and the disturbance attenuation level is minimized through convex multi-objective optimization. Finally, the proposed observer is illustrated with an example.

Effect of kinematic parameters on MPC based on-line motion planning for an articulated vehicle

The aim of this article is to analyze the effect of kinematic parameters on a novel proposed on-line motion planning algorithm for an articulated vehicle based on Model Predictive Control. The kinematic parameters that are going to be investigated are the vehicles velocity, the maximum allowable change in the articulated steering angle, the safety distance from the obstacles and the total number of obstacles in the operating arena. The proposed modified path planning algorithm for the articulated vehicle belongs to the family of Bug-Like algorithms and is able to take under consideration, the mechanical and physical constraints of the articulated vehicle, as well as its full kinematic model. During the on-line motion planning algorithm, the MPC controller controls the lateral motion of the vehicle, through the rate of the articulation angle, while driving it accurately and safely over the on-line formulated desired path. The efficiency of the proposed combined path planning and control scheme is being evaluated under numerous simulated test cases, while exhaustive simulations have been made for analyzing the dependency of the proposed framework on the kinematic parameters. A motion planning scheme for an articulated vehicle based on a modified bug like algorithm and MPC has been presented.The motion planning scheme has been developed based on the real non linear kinematic equations of the articulated vehicle.An extended sensitivity analysis of the motion planning scheme has been performed.

Phasor extremum seeking and its application in Kaplan turbine control

The Combinator is an important part in Kaplan turbine control. It ensures that the turbine will operate in an optimum way, in terms of maximum efficiency of the plant. This work suggests a new sinusoidal perturbation based extremum seeking algorithm based on the phasor of the output. We propose to use this algorithm for generating the required data to build and correct the combinator. Simulations are presented showing the applicability of the proposed methods.