Robust finite-time tracking control of nonholonomic mobile robots without velocity measurements

The problem of robust finite-time trajectory tracking of nonholonomic mobile robots with unmeasurable velocities is studied. The contributions of the paper are that: first, in the case that the angular velocity of the mobile robot is unmeasurable, a composite controller including the observer-based partial state feedback control and the disturbance feed-forward compensation is designed, which guarantees that the tracking errors converge to zero in finite time. Second, if the linear velocity as well as the angular velocity of mobile robot is unmeasurable, with a stronger constraint, the finite-time trajectory tracking control of nonholonomic mobile robot is also addressed. Finally, the effectiveness of the proposed control laws is demonstrated by simulation.

Robust finite-time consensus tracking algorithm for multirobot systems

This paper studies the finite-time consensus tracking control for multirobot systems. We prove that finite-time consensus tracking of multiagent systems can be achieved on the terminal sliding-mode surface. Also, we show that the proposed error function can be modified to achieve relative state deviation between agents. These results are then applied to the finite-time consensus tracking control of multirobot systems with input disturbances. Simulation results are presented to validate the analysis.

Observer-based robust finite-time cooperative consensus control for multi-agent networks

This paper studies the finite-time consensus tracking control for multi-agent networks. The time-varying control input and the velocity of the leader is unknown to any follower. Only the position of the leader is known to its neighbors. We first propose a new finite-time multiple-surface sliding mode observer to estimate the leader's velocity. It is seen that the estimation error of the observer can converge to zero in a finite time. Then, we prove that finite-time consensus tracking of multi-agent networks can be achieved on a new terminal sliding mode surface. Simulation results are presented to validate the analysis.

Destination brand performance measurement over time: Tracking consumer perceptions of a competitive set of destinations over a 10 year period

There has been a paucity of research published in relation to the temporal aspect of destination image change over time. Given increasing investments in destination branding, research is needed to enhance understanding of how to monitor destination brand performance, of which destination image is the core construct, over time. This article reports the results of four studies tracking brand performance of a competitive set of five destinations, between 2003 and 2012. Results indicate minimal changes in perceptions held of the five destinations of interest over the 10 years, supporting the assertion of Gartner (1986) and Gartner and Hunt (1987) that destination image change will only occur slowly over time. While undertaken in Australia, the research approach provides DMOs in other parts of the world with a practical tool for evaluating brand performance over time; in terms of measures of effectiveness of past marketing communications, and indicators of future performance.

Rigorous finite-time guarantees for optimization on continuous domains by simulated annealing

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee finite-time performance in the optimization of functions of continuous variables. The results hold universally for any optimization problem on a bounded domain and establish a connection between simulated annealing and up-to-date theory of convergence of Markov chain Monte Carlo methods on continuous domains. This work is inspired by the concept of finite-time learning with known accuracy and confidence developed in statistical learning theory.