M. Benzaoui

Bio: M. Benzaoui is an academic researcher from University of Boumerdes. The author has contributed to research in topics: Trajectory & Obstacle avoidance. The author has an hindex of 2, co-authored 2 publications receiving 61 citations.

Redundant robot manipulator control with obstacle avoidance using extended Jacobian method

Abstract: In this paper, we develop a control of a redundant robot manipulator. That has to carry out a trajectory tracking in operational space while avoiding an obstacle. For this purpose, extended Jacobian method is used. The Self-motion vector is introduced at the level of the inverse kinematic solution in order to produce the obstacle avoidance (the secondary task). Thus, robot avoids obstacles without influencing the main task (trajectory tracking). The self-motion is computed from the optimization of scalar function depending on an anti collision constraint. Finally, this control method has leaded to a trajectory tracking in Cartesian space while avoiding the obstacle.

Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges

Abstract: Autonomous vehicle field of study has seen considerable researches within three decades. In the last decade particularly, interests in this field has undergone tremendous improvement. One of the main aspects in autonomous vehicle is the path tracking control, focusing on the vehicle control in lateral and longitudinal direction in order to follow a specified path or trajectory. In this paper, path tracking control is reviewed in terms of the basic vehicle model usually used; the control strategies usually employed in path tracking control, and the performance criteria used to evaluate the controller's performance. Vehicle model is categorised into several types depending on its linearity and the type of behaviour it simulates, while path tracking control is categorised depending on its approach. This paper provides critical review of each of these aspects in terms of its usage and disadvantages/advantages. Each aspect is summarised for better overall understanding. Based on the critical reviews, main challenges in the field of path tracking control is identified and future research direction is proposed. Several promising advancement is proposed with the main prospect is focused on adaptive geometric controller developed on a nonlinear vehicle model and tested with hardware-in-the-loop (HIL). It is hoped that this review can be treated as preliminary insight into the choice of controllers in path tracking control development for an autonomous ground vehicle.

Compatible Convex–Nonconvex Constrained QP-Based Dual Neural Networks for Motion Planning of Redundant Robot Manipulators

Abstract: Redundant robot manipulators possess huge potential of applications because of their superior flexibility and outstanding accuracy, but their real-time control is a challenging problem. In this brief, a novel compatible convex–nonconvex constrained quadratic programming (CCNC-QP)-based dual neural network (DNN) scheme is proposed for motion planning of redundant robot manipulators. The proposed CCNC-QP-DNN scheme not only has the advantages of DNN, e.g., parallel processing and real-time control, but also possesses the advantages of CCNC-QP, such as the zeroing initial error, considering convex or nonconvex constraints. Being different from most neural networks, the proposed approach is training-free and is able to track reference signals with superior accuracy and speedability. The detailed derivation process and theoretical analysis are presented. Computer simulations with five end-effector tasks verify the effectiveness and accuracy of the proposed control method in both the convex constraints condition and nonconvex constraints condition whether an initial error exists or not.

Analytical Inverse Kinematics and Self-Motion Application for 7-DOF Redundant Manipulator

Abstract: This paper studies multiple inverse kinematics solutions for a 7-DOF human redundant manipulator with a special joint configuration. A method is proposed for determining the continuous joint angle vector by selecting the inverse solution from discrete multiple solutions to the continuous end path of the mechanical arm. The elbow angle constraint is introduced, and the mapping relationship from the elbow angle to the joint angle is established. Subspaces are found in the multiple solution spaces to avoid joints exceeding the limit to obtain elbow angle interval, and then combined with the collision detection technique, subspaces are sought in multiple solution spaces to avoid collisions between robotic arms and obstacles. Two subspaces are then obtained, and with use of their intersection, all feasible manipulator inverse kinematic solutions that avoid the joint limit and the obstacles at a given pose are obtained. The above method explicitly determines the complete feasible kinematic inverse solution of redundant manipulators. Finally, the validity of the methods is verified via kinematic simulations.

Constrained neural adaptive PID control for robot manipulators

Abstract: The problem of designing an analytical gain tuning and stable PID controller for nonlinear robotic systems is a long-lasting open challenge. This problem becomes even more intricate if unknown system dynamics and external disturbances are involved. This paper presents a novel adaptive neural-based control design for a robot with incomplete dynamical modeling and facing disturbances based on a simple structured PID-like control. Radial basis function neural networks are used to estimate uncertainties and to determine PID gains through a direct Lyapunov method. The controller is further augmented to provide constrained behavior during system operation, while stability is guaranteed by using a barrier Lyapunov function. The paper provides proof that all signals in the closed-loop system are bounded while the constraints are not violated. Finally, numerical simulations provide a validation of the effectiveness of the reported theoretical developments.