Chokri Rekik

A hybrid fuzzy-sliding mode controller for a mobile robot

This paper deals with the autonomous navigation problem. Its objective is the driving of a mobile robot to the goal position without colliding with obstacles. Therefore, a new hybrid approach based on the combination of fuzzy logic systems with sliding mode method is proposed. More specifically, fuzzy logic is a reactive decision making method and it is devoted to bring the robot towards the target, whereas sliding mode is used to ensure the obstacle avoidance behaviour. The robot should follow accurately a limit cycle trajectory using a sliding mode controller. This limit cycle trajectory allows the generation of a smooth trajectory in the vicinity of obstacles. Through simulation results, we can conclude that the robot is able to generate suitable trajectories in different environments, which demonstrates the efficiency and the reliability of the proposed approach.

Two coupled neural-networks-based solution of the Hamilton-Jacobi-Bellman equation

Abstract: This work is aimed at looking into the determination of optimal neuro-feedback control for discrete time nonlinear systems. The basic idea consists in the use of two coupled neural networks to approximate the solution of the Hamilton-Jacobi-Bellman equation (HJB) and to obtain a robust feedback closed-loop control law. The used learning algorithm is a modified version of the backpropagation one. As an illustration, a numerical nonlinear discrete time example is considered. Simulation results show the effectiveness of the proposed method.

Optimised fuzzy logic controller for a mobile robot navigation

This paper presents fuzzy logic controller (FLC) design using optimisation techniques. The FLC has been developed and implemented for the motion of the robot from an initial position towards another desired position, taking into account the kinematic constraints. First, we have carried out a simulation of a fuzzy logic based controller which determines the speed values of each driving wheel, while the robot seeking the goal. Second, an optimisation of this controller has been realised using gradient method or genetic algorithms. Simulation and experimental results demonstrates the effectiveness of the proposed approach.

Optimal trajectory of a mobile robot by a Genetic Design Fuzzy logic controller

Obstacle avoidance and path planning are the most important problems in mobile robots. Besides, finding methods for controlling and decreasing the real robot error in path have been another target for researchers investigation. Usual methods have two separated parts, research target and path planning with obstacle avoidance. In this context, a fuzzy logic controller has been constructed in order to train an intelligent robot. Genetic algorithms are used to optimize the consequences of a Sugeno fuzzy logic optimal controller for the mobile robot navigation. This is to search a target in an environment with and without obstacles. Simulation results verify successful applications of method for real motion situations. An application have been effected in the Khepera robot demonstrating. The Khepera robot can go to the goal and avoid the obstacles successfully.

Hierarchical control for discrete large-scale complex systems by intelligent controllers

This paper presents a new method to approximate optimal control strategies of discrete time large-scale nonlinear systems using intelligent approaches. The idea is based on the decomposition principle of the global system into interconnected subsystems for which nonlinearities are located in the interconnection terms. Then, the mixed coordination procedure between different subsystems is formulated as a hierarchical method for the solution of large-scale optimal control problems. So, for each subsystem, local optimal feedback gains are expressed in terms of the interconnection vector. For this purpose, neural networks and fuzzy logic controllers have been constructed in order to identify these gains. A comparison with the differential dynamic programming procedure as a reference method is done. Simulation results of two numerical examples show that the proposed method yields to satisfactory performances, and the robustness of the proposed approaches has been tested.

Optimized fuzzy controller for mobile robot navigation in a cluttered environment

Obstacle avoidance and path planning are the most important problems in mobile robots. In this paper, a fuzzy logic controller has been constructed in order to train an intelligent robot. Gradient method is used to optimize consequences of a Sugeno fuzzy logic controller for the mobile robot navigation, in order to reach a target in a clutterd environment. Not only simulation results are shown in this paper, but also the “real-time” implementation has been realized onto the mini robot Khepera II. Simulation results verify successfully the application of the proposed method to real motion situations.

Interval type-2 Takagi-Sugeno-Kang fuzzy logic approach for three-tank system modeling

This paper concerns the use of fuzzy structures to model non linear dynamic systems. An interval type-2 Takagi Sugeno Kang fuzzy logic systems (IT2 TSK FLSs) is proposed. The proposed approach is a combination of IT2 fuzzy system and TSK fuzzy models and it presents an extension of the type-1 Takagi Sugeno Kang fuzzy logic system (T1 TSK FLSs). The interval type-2 fuzzy sets provide additional degrees of freedom and offer the capability to directly handle uncertainties. Different steps of this approach are described. The performance of the IT2 TSK FLSs is compared to the traditional T1 TSK FLSs. Simulation results performed on three tank system illustrate the efficiency of the suggested method.

On the suboptimal solution for large scale non-linear control systems using neural networks

This work is aimed at looking into the determination of optimal control strategies of large scale non-linear systems. The basic idea consists of the decomposition of the global system into interconnected subsystems. We consider non-linear systems whose subsystems are linear with respect to their state and control; in other words, non-linearities are located in the interconnection terms. Then, for each subsystem, local optimal feedback gains are easily expressed in terms of interconnection vectors. Such optimisation has been successfully achieved using a neural net yielding the optimal gain at each step time. Some numerical examples are considered in order to illustrate the proposed approach.

Decomposition and hierarchical control for discrete complex systems by fuzzy logic controllers

This paper proposes a method to compute sub-optimal control strategies of discrete time large-scale non-linear systems by fuzzy logic controllers. The method is based on the principle of decomposition of the global system into inter-connected subsystems. We consider that the non-linearities are located in the interconnections terms. Then, a mixed method of coordination procedure between different subsystems is formulated. So, for each subsystem, local optimal feedback gains are expressed as a function of the interconnection vector. Within this approach, first order Tkagi-Sugeno fuzzy logic systems have been constructed in order to identify these gains. Simulation results of a rotary crane show the effectiveness of the method and the robustness of the proposed approach.

On the neuro-genetic approach for determining optimal control of a rotary crane

The aim of this paper considers the determination of optimal control trajectories of a complex process. The proposed method is based on the decomposition of the system into interconnected subsystems. We consider the cases where subsystems are linear in terms of their state and control vectors. For this reason, a neural network is identified which compute local gains. Genetic algorithms are used to optimize the networks weights. Simulation results show that the proposed approximations yield satisfactory performances.