Predictive control using feedback linearization based on dynamic neural models

This paper presents a hybrid control strategy integrating dynamic neural networks and feedback linearization into a predictive control scheme. Feedback linearization is an important nonlinear control technique which transforms a nonlinear system into a linear system using nonlinear transformations and a model of the plant. In this work, empirical models based on dynamic neural networks have been employed. Dynamic neural networks are mathematical structures described by differential equations, which can be trained to approximate general nonlinear systems. A case study based on a mixing process is presented.

Performance of asynchronous channel assignment scheme in non-uniform and dynamic topology WLANs

Dense deployments of wireless local area networks (WLANs) are fast becoming a permanent feature of all developed cities around the world. While this increases capacity and coverage, the problem of increased interference, which is exacerbated by the limited number of channels available, can severely degrade the performance of WLANs if an effective channel assignment scheme is not employed. In an earlier work, an asynchronous, distributed and dynamic channel assignment scheme has been proposed that (1) is simple to implement, (2) does not require any knowledge of the throughput function, and (3) allows asynchronous channel switching by each access point (AP). In this paper, we present extensive performance evaluation of this scheme when it is deployed in the more practical non-uniform and dynamic topology scenarios. Specifically, we investigate its effectiveness (1) when APs are deployed in a nonuniform fashion resulting in some APs suffering from higher levels of interference than others and (2) when APs are effectively switched `on/off' due to the availability/lack of traffic at different times, which creates a dynamically changing network topology. Simulation results based on actual WLAN topologies show that robust performance gains over other channel assignment schemes can still be achieved even in these realistic scenarios.

An asynchronous distributed dynamic channel assignment scheme for dense WLANs

Wireless local area networks (WLANs) have changed the way many of us communicate, work, play and live. Due to its popularity, dense deployments are becoming a norm in many cities around the world. However, increased interference and traffic demands can severely limit the aggregate throughput achievable if an effective channel assignment scheme is not used. In this paper, we propose an enhanced asynchronous distributed and dynamic channel assignment scheme that is simple to implement, does not require any knowledge of the throughput function, allows asynchronous channel switching by each access point (AP) and is superior in performance. Simulation results show that our proposed scheme converges much faster than previously reported synchronous schemes, with a reduction in convergence time and channel switches by tip to 73.8% and 30.0% respectively.

An improved distributed dynamic channel assignment scheme for dense WLANs

The popularity of wireless local area networks (WLANs) has resulted in their dense deployment in many cities around the world. The increased interference among different WLANs severely degrades the throughput achievable. This problem has been further exacerbated by the limited number of frequency channels available. An improved distributed and dynamic channel assignment scheme that is simple to implement and does not depend on the knowledge of the throughput function is proposed in this work. It also allows each access point (AP) to asynchronously switch to the new best channel. Simulation results show that our proposed scheme converges much faster than similar previously reported work, with a reduction in convergence time and channel switches as much as 77.3% and 52.3% respectively. When it is employed in dynamic environments, the throughput improves by up to 12.7%.

Impedance mismatch: some differences between the way humans and robots control interaction forces

In over forty years of research robots have made very little progress still largely confined to industrial manufacture and cute toys, yet in the same period computing has followed Moores Law where the capacity double roughly every two years. So why is there no Moores Law for robots? Two areas stand out as worthy of research to speedup progress. The first is to get a greater understanding of how human and animal brains control movement, the second to build a new generation of robots that have greater haptic sense, that is a better ability to adapt to the environment as it is encountered. A remarkable property of the cognitive-motor system in humans and animals is that it is slow. Recognising an object may take 250 mS, a reaction time of 150 mS is considered fast. Yet despite this slow system we are well designed to allow contact with the world in a variety of ways. We can anticipate an encounter, use the change of force as a means of communication and ignore sensory cues when they are not relevant. A better understanding of these process has allowed us to build haptic interfaces to mimic the interaction. Emerging from this understanding are new ways to control the contact between robots, the user and the environment. Rehabilitation robotics has all the elements in the subject to not only enable and change the lives of people with disabilities, but also to facilitate revolution change in classic robotics.