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the aim of this paper is to present a strategy describing a hybrid approach for the navigation of a mobile robot in a partially known environment. The main idea is to combine between fuzzy logic approach suitable for the navigation in an unknown environment and spiking neural networks approach for solving the problem of navigation in a known environment. In the literature, many approaches exist for the navigation purpose, for solving separately the problem in both situations. Our idea is based on the fact that we consider a mixed environment, and try to exploit the known environment parts for improving the path and time of navigation between the starting point and the target. The Simulation results, which are shown on two simulated scenarios, indicate that the hybridization improves the performance of robot navigation with regard to path length and the time of navigation.

In this paper an enhanced approach based on a modified biogeography optimization with predator and prey behavior (PMBBO) is presented. The approach uses several predators with new proposed prey's movement formula. The potential of using a modified predator and prey model is to increase the diversification along the optimization process so to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems (Mass spring damper and an inverted pendulum) and has given remarkable results when compared to genetic algorithm and classical BBO.

In this paper, we present a combination of sequential trained radial basis function networks and fuzzy techniques to enhance the variable structure controllers dedicated to robotics systems. In this aim, four RBFs networks were used to estimate the model based part parameters (Inertia, Centrifugal and Coriolis, Gravity and Friction matrices) of a variable structure controller so to respond to model variation and disturbances, a sequential online training algorithm based on Growing-Pruning "GAP" strategy and Kalman filter was implemented. To eliminate the chattering effect, the corrective control of the VS control was computed by a fuzzy controller. Simulations are carried out to control three degrees of freedom SCARA robot manipulator where the obtained results show good disturbance rejection and chattering elimination.

Adaptive control is a set of approaches used for the automatic adjustment of the controller parameters in order to achieve or maintain a certain level of performance when the parameters of the control process vary over time or unknown. The parameters of the controller are adapted to ensure also that there is no overestimation of the gain with real a priori unknown value of uncertainties. The proposed control approach consists in using second-order sliding mode with a dynamically adapted control gain that guarantees the stability and robustness of the control law even with the presence of uncertainties and perturbation of the system. The control is applied by simulation (Matlab-Simulink) on a doubly fed induction generator (DFIG) integrated into a variable speed wind turbine and controlled by super twisting sliding mode control. The simulations indicate the efficiency of the proposed approach especially in the case of change of the reference speed and/or the parameters variation.

Emergency department (ED) is one of the most complex systems, due to its unstable environment and the stochastic characteristics of patient flows, pathologies, and limited resources, which lead to a long waiting time. Effective management of patient flows for 'ED' services has become a critical matter for most hospital supervisors. Many authors have tried to solve those issues based on different methodologies, with shared aims represented in the optimisation of patient care, by minimising patient waiting time at different levels in 'ED'. Modern studies mainly focus on limiting these issues by proposing a solution based on extending 'ED' resources. The case study was realised at the 'ED' of the hospital Chalabi Abdelkader, Mascara, Algeria. Then, the system was modelled by a coloured Petri net framework. Afterward, we performed simulations of numerous improvement scenarios. As a result, patient waiting times encountered in 'ED' were reduced, thus patient length of stay.

This paper is dedicated to present an overview of harmony search algorithm and its main variants based on parameters settings updating and hybridization. The considered algorithms are compared via Wilcoxon and Friedman nonparametric tests using benchmark functions. Statistical comparison highlights the Global best harmony search as the best variant.

This paper addresses the problem of covering an open area by using multiple robots. Our paper proposes a heuristic algorithm to maximize area coverage of mobile robots team and maintain connectivity. This algorithm adopts the principle of spreading a wave in order to guide the movements of robots. This wave starts from a point and spreading in regular way. So in each step, the central robot builds a wave around it using the adequate robots from its direct neighbors. The mobile robots coordinate to determine the next destinations to reach, so the coverage time and traveled distance is minimized. Also the robots network has less overhead messages because each robot communicates only with it directs neighbors. We present also an approach for self-redeployment in case of failure. The simulation results are presented to validate the performance of our approach.

This paper deals with the nonlinear observation theory as it is applied to the class of rigid robot manipulator systems. A survey based on results and design approaches of non linear observers for this class of nonlinear systems is presented. We cannot make an analytic comparative study of the proposed methods since they are conceptually different. However, we try to provide the reader insight into the performance of each method based on experimental results found in literature, and through simulation analysis on a 2-DOF-robot.

This paper is dedicated to present one of newly developed evolutionary algorithm; we considered a new way to tune the PID controller of nonlinear systems where the parameters of the controller are optimized using the Artificial Immune Algorithm (AIA) based on the immune system which defends the body against harmful diseases and infections. This work describes the application of AIA Technique based on two fitness functions to optimally tune the three terms of the classical PID controller for nonlinear process. Matlab simulations of the proposed algorithm are carried out over a mass-spring damper system. The obtained results indicate that Artificial Immune Algorithm is a reasonable and effective method for parameter optimization of PID controllers.

This paper is dedicated to the presentation of enhanced swarm intelligence based training algorithm for Radial basis functions neural networks. The proposed training algorithm (ABC-PP) is hybridization between the Artificial Bees Colony (ABC) and a predator and prey behavior to improve the diversification mechanism of the ABC. Statistical analysis is carried out with nonparametric tests to evaluate the proposed training algorithm comparing it with ABC, GA and PSO based RBF training algorithms in function approximation. The proposed algorithm is applied to identify a real inverted pendulum model giving acceptable results.

International audience; — This paper proposes a hybrid approach based on limit-cycles method and fuzzy logic controller for the problem of obstacle avoidance of mobile robots in unknown environment. The purpose of hybridization consists on the improvement of basic limit-cycle method in order to obtain safe and flexible navigation. The proposed algorithm has been successfully tested in different configurations on simulation.

In this paper, we present a new tuning method for a PID controller of a nonlinear system. The proposed approach is an hybridization of the Artificial Bees Colony (ABC) and the Predator and prey (P\&P) behavior. The employed bees will explore new sources in the search space while running away from their predators. Simulations of the proposed algorithm are carried out over an inverted pendulum nonlinear system. Obtained performances are better then Genetic algorithm when compared in the same conditions.

This paper deals with the application of a randomized optimization method to obtain the optimum PID gains. This method is based on a heuristic Kalman algorithm (HKA) and is described as more speedy and more accurate optimization methods. First, we introduce the general form of the H∞ control law obtained by solving a partial differential equation labeled Hamilton-Jacobi-Isaacs equation. An analytic solution to this equation is described for the Euler-Lagrange Systems. Second, based on this solution and on the PID control law resulting, it is shown how to use the optimization method to adjust optimally the controller's gains ensuring a minimum L2 - gain and thus disturbance attenuation. Third, we apply this optimization algorithm in the trajectory tracking and disturbance attenuation problem of a three degree of freedom robot manipulator. The simulation results show the effectiveness of the H∞-PID control law optimized by the HKA method.

In this paper, we try to present a sliding mode with fuzzy-neural network controller for nonlinear systems. It is a special nonlinear control method (SMC) which has quick response, insensitive to parameters variation and disturbance. Online identification for plants is not needed, it's very suitable for nonlinear system control, but in reality using the chattering reduction and elimination are key problem in SMC. By using a function-augmented sliding hyper plane, it is guaranteed that the output tracking error converges to zero in finite time which can be set arbitrarily. The fuzzy-neural network mainly Self Tuning Fuzzy Inference System (STFIS) is used to approximate the unknown system functions and switch item. Finally, the sliding-mode with fuzzy-neural network control is used to control single inverted pendulum and confirms the validity of the proposals. Results of simulations containing tests of robustness are presented and realized in MATLAB environment.

The paper deals with the modeling and simulation of a public transportation system wherein passengers need to use more than one transportation mode for their moving. Many studies have been developed in the literature for such problematic, and many modeling tools are proposed. In this paper we focus on the use of hybrid Petri nets (HPN) and the mathematical techniques they propose for performance evaluation and minimization of passengers waiting times in connection stations. HPN is used as an adequate formal tool to model both continuous and discrete behavior of the considered system. The continuous behavior corresponds to the exchange of the passenger flows between two different transportation modes (bus and train). The discrete behavior represents the moving of transportation entities on their own lines. The system is evaluated and analyzed using the fundamental equation and its evolution is studied using IB-State techniques in HPN. The proposed approach is illustrated with a numerical example.

This paper is dedicated to present the newly developed evolutionary algorithm: Biogeography based optimization (BBO). It is based on the migration of information between habitats like in Biogeography. The BBO is then used to tune a PID controller of nonlinear systems where the parameters are optimized. Simulations of the proposed algorithm are carried out over an inverted pendulum and second on mass-spring damper system. Performances of the BBO are compared to those of genetic algorithm in PID tuning problem and the BBO gives acceptable results even best then GA.

A robust control approach denoted sliding control of MIMO nonlinear system based on the output feedback linearization is developed to attenuate the parametric uncertainties. The scheme is dedicated to model of unmanned aerial vehicles (the quadrotor UAV). We assume that the model of the plant is not precise, due to nondeterministic knowledge of inertias parameters. Tracking control is used to stabilize the equilibrium of the system, while all the state supposed to be measurable. The analysis is based on tracking errors during transients and at the steady state, on performance, stability and robustness with respect to plant uncertainties. Simulation results are carried out.

The aim of this work is the combination of radial basis function networks (RBF) and fuzzy techniques to enhance the sliding mode controllers. In fact, three RBFs networks were used to estimate the model parameters and to respond to model variation and disturbances, a sequential training algorithm based on Kalman filter was implemented, and to eliminate the chattering effect, a fuzzy controller was designed. The hybrid sliding mode controller had shown a strong ability to get over noise and uncertainties. The former controller was used to control a two degree of freedom robot manipulator.

A sliding mode control has been a great interest in the control engineering community, with many applications particularly in the robot manipulators control. This paper presents investigations into the development of Sliding Mode control approach based neural network, where the model parameters are used in the equivalent control law. A neural model of robot parameters is calculated. The first one, to estimate the inertia matrix while the second, is dedicated to estimate the parameters of the matrix of the Coriolis/centripetal terms. The last one estimates the gravity vector. To demonstrate the applicability of the methods, a simulated two degrees of freedom robot manipulator is considered in order to evaluate the tracking properties and robustness capacities of neural sliding mode control technique.

Technological Advances in microelectronic and telecommunication fields enable the existence of tiny computing units, very small and more efficient. In the context of these new technologies, Wireless Sensor Networks (WSN) have emerged. The main research objective in WSN domain is the development of algorithms and protocols ensuring minimal energy consumption. Most proposed solutions are based on one-layer stack model approach. Recently, other works tend to exploit together many layers in order to optimize energy consumption. In this paper, we propose a Cross-Layer Medium Access Control (CL-MAC) protocol using two adjacent layers (MAC and Network) to economize energy for WSN. The basic idea behind our protocol is to wake-up only nodes belonging to a routing path from the source to the base station (Sink) by exploiting routing information while other nodes leave maintained as long time as possible in a sleep mode. The protocol is modeled using a time Petri net and validated by TiNA tool. The simulation results demonstrate the effectiveness of our proposal in terms of energy consumption and latency.

This paper deals with the design problem of an Hinfin observer for robot manipulators in the presence of external perturbations. The proposed scheme is an application of an Hinfin filter to the class of robotic manipulator systems in order to guarantee disturbance attenuation of the observation error and the asymptotic stabilization of the estimation error. Using a stabilizing control law, a stabilization result based on the weak detectability of the system is obtained and the semi-global asymptotic stability of the equilibrium point of the combined system is shown. Simulation results on a six D-O-F PUMA 560 robot manipulator show the asymptotic convergence of the reconstruction and tracking error vectors.

This paper proposes a modelling and control approach using hybrid Petri nets HPNs together with fuzzy logic FL for modelling, analysis and control of a multimodal transportation system. More precisely, the study focuses on the improvement of users' service in terms of minimising their waiting times at the exchange platforms of passengers. Many studies have been developed in the literature for such problematic, and many modelling tools are proposed. In this paper, we focus on the use of HPN as a powerful tool to describe the discrete and continuous behaviour of such a system. The continuous part corresponds to the exchange of passenger flows between two different transportation modes e.g., bus and train. While the discrete part represents the means of transportation as discrete entities. The system is evaluated and analysed using the HPN fundamental equation. Its evolution is studied and analysed using IB-state techniques. In the second step, the fuzzy logic techniques are used to control the system during its evolution in order to minimise the arrival gap of connected transportation means at the exchange points of passengers. An example of illustration is worked out and the obtained results are reported.