Foudil Abdessemed

A fuzzy-based reactive controller for a non-holonomic mobile robot

Abstract This paper presents the theoretical development of a complete navigation problem of an autonomous mobile robot. The situation for which the vehicle tries to reach the endpoint is treated using a fuzzy logic controller. The problem of extracting the optimized IF–THEN rule base is solved using an evolutionary algorithm. A new approach based on fuzzy concepts is presented in this paper to avoid any collision with the surrounding environment when this latter becomes relatively complex. Simulation results show that the designed fuzzy controller achieves effectively any movement control of the vehicle from its current position to its end motion and without any collision.

Modelling and control of an omnidirectional mobile manipulator

Abstract A new approach to control an omnidirectional mobile manipulator is developed. The robot is considered to be an individual agent aimed at performing robotic tasks described in terms of a displacement and a force interaction with the environment. A reactive architecture and impedance control are used to ensure reliable task execution in response to environment stimuli. The mechanical structure of our holonomic mobile manipulator is built of two joint manipulators mounted on a holonomic vehicle. The vehicle is equipped with three driven axles with two spherical orthogonal wheels. Taking into account the dynamical interaction between the base and the manipulator, one can define the dynamics of the mobile manipulator and design a nonlinear controller using the input-state linearization method. The control structure of the robot is built in order to demonstrate the main capabilities regarding navigation and obstacle avoidance. Several simulations were conducted to prove the effectiveness of this approach.

A Two-Layer Robot Controller Design Using Evolutionary Algorithms

The results obtained by a rule-based proportional, integral, derivative (PID) precompensator controller applied to a two-joint manipulator are discussed. The end effector is made to follow a specified trajectory obtained from the inverse kinematics by an appropriate design of a fuzzy control law. The desired trajectory is determined by the values of the joint variables and the structural kinematics parameters of the manipulator. The performance of the PID controller is exploited here to build a fuzzy precompensator that will enhance the conventional PID and to obtain better performances and results. The fuzzy rule base of the precompensator designed is found by associating two evolutionary algorithms that search for the optimal solution.

A Hierarchical Fuzzy Control Design for Indoor Mobile Robot

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robots mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot.

SVM-Based Control System for a Robot Manipulator

Real systems are usually non-linear, ill-defined, have variable parameters and are subject to external disturbances. Modelling these systems is often an approximation of the physical phenomena involved. However, it is from this approximate system of representation that we propose - in this paper - to build a robust control, in the sense that it must ensure low sensitivity towards parameters, uncertainties, variations and external disturbances. The computed torque method is a well-established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose the estimation of the dynamics model of the nonlinear system with a machine learning regression method. The output of this regressor is used in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator. In cases where some of the parameters of the plant undergo a change in their values, poor performance may result. To cope with this drawback, a fuzzy precompensator is inserted to reinforce the SVM computed torque-based controller and avoid any deterioration. The theory is developed and the simulation results are carried out on a two-degree of freedom robot manipulator to demonstrate the validity of the proposed approach.

Quadrotor position and attitude control via backstepping approach

The purpose of this paper is to apply a nonlinear control law for an unmanned quadrotor helicopter. First, the quadrotor dynamic model is established using Newton-Euler formalism and taking into account various physical phenomena that can influence its dynamic behavior. Subsequently, Backstepping controller is designed. Its job is to generate commands to the four rotors to drive the quadrotor to track desired Cartesians positions and desired tilt angels. The designed methodology is based on the Lyapunovs theory of stability. This method was checked satisfactorily in simulation.

Modelling and feedback control of an omni-directional mobile manipulator

In this article we develop a new approach to control an omnidirectional mobile manipulator. The robot is considered as an individual agent aimed to perform robotic tasks described in terms of displacement and force interaction with the environment. A reactive architecture and impedance control are used to ensure reliable task execution in response to environment stimuli. The mechanical structure of our holonomic mobile manipulator is built from two joint manipulator mounted on an holonomic vehicle. The vehicle is equipped using three motorized axles with two spherical orthogonal wheels. The dynamics of the mobile manipulator robot is defined tacking into account the dynamical interaction between the base and the manipulator. Then we design a nonlinear controller for the robot using input-state linearization method. The control structure of the robot is built in order to demonstrate the main capabilities in navigation and obstacle avoidance. Several simulations were conducted to prove the effectiveness of our concept.

SVM Based Computed Torque for Robot Manipulator Control

Abstract Because of t he strong need of finding easy and robust methods to control robots, many solutions has been proposed in the literature. The computed torque method is a well established robot control technique which takes account of the dynamic coupling between the robot links. However, its main disadvantage lies on the assumption of an exactly known dynamic model which is not realizable in practice. To overcome this issue, we propose in this paper to estimate the dynamics model of the nonlinear system with a machine learning regression method, namely support vector regressor. Then the output of this regressor is thereafter in conjunction with a PD controller to achieve the tracking trajectory task of a robot manipulator.

Fuzzy model based predictive control of nonlinear systems

In this paper, the generalized predictive control with the fuzzy model is developed. GPC has been developed to control linear time invariant plants. So, for controlling the nonlinear process, fuzzy model is used as the base model of the predictor. The fuzzy model predictive control is demonstrated by application to the problem of balancing and swing-up of an inverted pendulum on a cart.

Obstacle avoidance using wall-following strategy for indoor mobile robots

This paper proposes a fuzzy logic methodology to control an indoor mobile robot for a complete navigation in an unknown environment. The methodology incorporates two basic behaviors, namely: reaching the goal and avoiding obstacles. The obstacle avoidance behavior is treated using wall-following scheme based on a fuzzy technique proposed for this proposed. The mobile robot control mechanism uses some sort of knowledge-base arranged in a set of fuzzy-rule-base to implement the wanted behavior that makes the mobile robot follow the boundary of an obstacle or a wall. A constant distance to the obstacle/wall is maintained while the robot tries successfully to get around this difficulty. Once the path is clear, the obstacle avoidance behavior is inhibited and reaching the goal behavior is activated using a second fuzzy controller. In order to handle data uncertainties, Type-2 fuzzy sets are considered. This methodology was successfully tested on a real mobile robot for different sort of scenarios.