Youcef Touati

A neural network-based approach for an assembly cell control

In several robotics applications, the robot must interact with the workspace, and thus its motion is constrained by the task. In this case, pure position control will be ineffective since forces appearing during the contacts must also be controlled. However, simultaneous position and force control called hybrid control is then required. Moreover, the nonlinear plant dynamics, the complexity of the dynamic parameters determination and computation constraints makes more difficult the synthesis of control laws. In order to satisfy all these constraints, an effective hybrid force/position approach based on artificial neural networks for multi-inputs/multi-outputs systems is proposed. This approach realizes, simultaneously, an identification and control of systems, and it is implemented according to two phases: At first, a neural observer is trained off-line on the basis of the data acquired during contact motion, in order to realize a smooth transition from free to contact motion. Then, an online learning of the neural controller is implemented using neural observer parameters so that the closed-loop system maintains a good performance and compensates for uncertain/unknown dynamics of the robot and the environment. A typical example on which we shall focus is an assembly task. Experimental results on a C5 links parallel robot demonstrate that the robots skill improves effectively and the force control performances are satisfactory, even if the dynamics of the robot and the environment change.

On the robust PID adaptive controller for exoskeletons: A particle swarm optimization based approach

This article proposes a robust PID adaptive controller for nonlinear systems with one or more degrees of freedom (DoF). The adaptive controller aims at minimizing the errors in trajectory tracking without requiring a prior modeling of the targeted nonlinear system. Furthermore, the proposed controller requires only the inputs and outputs of the system. And it is based on modified particle swarm optimization algorithm whose goal is to find the best PID parameters that optimize the execution of desired task by minimizing an objective function. The adaptation by the controller addresses two critical problems: The first problem is the instability of the control signal provided by the convergence phase of the classical PSO algorithm. This behavior adversely affects the lifetime of any actuator and, therefore, is undesirable. The second problem is the stagnation of the classical PSO algorithm after convergence at the immediately found optimal solution. The proposed adaptive PID controller is initially tested in simulation on a dynamical model of a robot manipulator evolving in the vertical plan. Which is followed by experimental tests performed on an actuated joint orthosis worn by human subjects having different morphologies. A comparative study with two other algorithms has been also conducted. Based on the obtained results, we conclude the efficiency of the proposed approach.

Hierarchical routing approach-based energy optimization in wireless sensor networks

We propose a critical improvement of the LEACH (Low-Energy Adaptive Clustering Hierarchy) routing protocol for the optimization of the energy consumption as well as memory occupation of Wireless Sensor Network (WSN). Our protocol LEACH-M uses a cascade of clustering algorithms, every step of which chooses the next one. We expect at best an improvement of 5% of energy consumption and since the lifetime of the application is itself improved the amount of data routed is improved in the same amount. Of course, testing our approach using it along a comparative study with the standard LEACH confirms our expectation.

Routing Information for Energy Management in WSNs

The main objective in a WSN application is to guarantee the transmission of information between different sensor nodes according to a pre-established routing protocol. Improving performance in terms of longevity, connectivity and robustness requires some consideration of constraints such as energy consumption, bandwidth and the optimal use of calculation and memory resources. The conception and implementation of a routing protocol can be influenced by several factors that must be addressed before it is possible to ensure efficient data communication. Among these factors, we can refer to the deployment of nodes in operational environments. Being dependent on the kind of applications under consideration, the deployment phase can greatly affect routing performances. It can be determinist and, in this case, sensor nodes are placed manually and the data are transmitted according to pre-determined routes from source nodes to the destination. This can also be done randomly by haphazardly distributing the nodes, as in ad hoc networks. In this case, an optimal classification of the network in clusters is highly necessary in order to guarantee connectivity and enable efficient energy management. Various studies have shown that the communication between nodes in restricted areas, i.e. clusters, provides better results in terms of energy consumption and bandwidth, and information routing can be guaranteed in this case through multiple hops. Moreover, the lifetime of a sensor node is strongly linked to the lifetime of its battery and the malfunctioning of one or several sensor nodes, due to battery failures or a lack of energy resources, can cause significant topological changes that require the network to be completely reorganized in order to ensure the rerouting of data. It is therefore important to consider the issue of how to manage energy consumption without a loss of performance and network precision, particularly in the case of a multi-hop WSN where each node can act as either a data transmitter or a simple router.

Algorithms of control by thought in robotics: Active and passive BMIs based on prior knowledge

One of the objectives of the control using the human thought is to make useful robotic systems for persons with high dependency (quadriplegics, paraplegics, etc.). When the human subject is not able to move his limbs, upper or lower, he is no longer able to perform basic and necessary tasks in his daily life. Recently, robotic systems have reached a very advanced level. For example, humanoid robots have become able to walk, recognize and carry objects simultaneously. On the other hand, wearable robots or exoskeletons can help dependent human subject to move and perform tasks previously difficult to imagine. Of course, all these robotic systems cannot perform these tasks except if they are fitted with advanced control schemes. To make these robotic systems, having already some intelligence, more useful, many researchers have studied the problem of controllers based on the user thought. The real challenge is to translate/classify correctly the thought of the user into robotic actions. When the brain activities are not correctly classified or the action thought by the user is not quite performed, it is important to discover it at time. This allows us to update the classifier/controller parameters in order to interpret more precisely the brain activities concerning the following action. This paper deals with looking for relevant prior knowledge that can anticipate any classification error. Thereafter, we propose some reflections regarding the control of robots by passive thought. Our analysis and results are based on the brain machine interface (BMI) using the Steady State Visual Evoked Potentials technique (SSVEP).

Energy Management in Wireless Sensor Networks

Over the last few years, the technological advances in wireless sensor network (WSN) applications have sparked great curiosity and a growing interest among both users and manufacturers, as well as in the research community. Significant challenges have been overcome to ensure their implementation by addressing problems arising from deployment and connectivity, and from routing and securing information, although much remains to be done at the energy management stage. A WSN is made up of a set of sensor nodes, using supply devices or batteries to operate and interconnected via radio links to ensure data reception, processing and transmission. Increasing the autonomy of sensors and extending the network lifetime can therefore be considered among the main objectives by examining interesting methods and studies that optimize energy consumption, and suggesting mechanisms to improve it. These mechanisms can involve several action levels which can range from the deployment stage to the information exploitation stage.

Hierarchical Hybrid Routing: the HRP-DCM Solution

In this chapter, we will present an alternative based on the hybrid HRP-DCM protocol, which allows improvements to be made at all levels, from the network recognition stage to the path optimization stage, during the exchange of information.

Optimization Techniques for Energy Consumption in WSNs

In recent years, several pieces of research have been undertaken to study and address energy consumption in WSNs. All of these works are based on different parameters relating to the operational mode of the sensor, mobility, quality of service, routing and securing information, and so on. Therefore, on the basis that radio transmission requires greater amounts of energy to route information to a destination, undertaking such research is useful for designing and developing new mechanisms that will provide solutions to improve energy efficiency. Depending on the context, these solutions can be categorized according to three different techniques: the partition of operating time, the structure of data and the mobility of sensors.

Adaptive Routing for Large-Scale WSNs

In WSNs, the efficient use of information largely depends not only on the processing and exploitation of data, but also on the methods that allow it to be routed. It is therefore necessary to consider the operational and/or structural constraints, namely the intrinsic characteristics of sensors (energy consumption, calculation and memory) and environments (network topology, lack of infrastructure, loss of nodes). Otherwise, communications between different sensor nodes must obey a routing protocol determined in advance in accordance with the type of application and the network architecture, be it flat or hierarchical. A suitable choice of protocol must not only allow the fluidity of information but also the optimization of energy consumption and the use of resources (calculation time and storage capacity). This is an ongoing challenge, particularly in the case of dense networks.

5 – Inheritance-based Adaptive Protocol for WSN Information Routing

: The inheritance based-adaptive protocol for WSN information routing is one of the proposed routing solutions that uses a dynamic clustering mechanism of networks. Its implementation covers three main stages: first, a deployment and network initialization stage in which sensor nodes are deployed randomly in the operational environment. There is then a cluster construction stage, which is the heart of the mechanism and consists of structuring the network into a set of areas, i.e. clusters, in which there are master nodes (CHs) that guarantee coordination of all member nodes (MNs) and processing. CHs are able to communicate directly with the base station or over several hops by passing through other master nodes acting as relays. Finally, there is a communication stage that corresponds to the transmission of data according to pre-established routing protocols. The objective is thereby to select, by optimizing criteria, the best path for conveying data to the destination. The idea is to determine one or several performance functions that allow a range of parameters to be taken into account, such as the distances covered between the source and the destination, the time taken from beginning to end, the signal strength, the energy consumption and/or the number of hops.