King Fahd University of Petroleum and Minerals

King Abdulaziz City for Science and Technology

Amar Khoukhi

Moustafa Elshafei

Abdulaziz Al-Maged

Mohammad Talib

Lahouari Ghouti

Muhammad Imtiaz Hossain

Abdulazeez Abdulraheem

Md Rafiul Hassan

Mutaz M Hamdan

Syed Faraz Ahmed

Tarek Ahmed Helmy El-Basuny

Syed Masiur Rahman

A directional drilling system consisting of a four-motor drilling head for better steering in directional drilling and vertical/horizontal drilling is developed. The rotational speed of each motor is independently controlled. The use of four motors in coordination with other traditional drilling variables allow precise control of the drilling direction and optimization of the rate of penetration (ROP). The top and right motors rotate in opposite directions to the bottom and left rotors to stabilize the roll rotation of the drilling head. Inclination (pitch) movement is obtained by increasing/decreasing the speed of the top motor while decreasing/increasing the speed of the lower motor. The Azimuth (yaw) movement is obtained similarly using the right and left motors. The drilling power is derived from down hole motors. A drill string transmits the drilling fluid and force on bit.

Multi-motor steerable drilling system and method

Directional system drilling and method

In the control method for mobile parallel manipulators, kinematic singularity and redundancy are solved through joint limits avoidance and manipulability criteria. By taking the MPM self-motion into consideration due to its redundancy, the inverse kinematic is derived using a hybrid neuro-fuzzy system, such as NeFIK. The discrete augmented Lagrangian (AL) technique is used to solve the highly nonlinear constrained multi-objective optimal control problem. An adaptive neuro-fuzzy inference system (ANFIS)-based structure (based on the result of the AL solution) is used to solve the online trajectory planning of the MPM.

Control method for mobile parallel manipulators

The parallel kinematic machine (PKM) trajectory planning method is operable via a data-driven neuro-fuzzy multistage-based system. Offline planning based on robot kinematic and dynamic models, including actuators, is performed to generate a large dataset of trajectories, covering most of the robot workspace and minimizing time and energy, while avoiding singularities and limits on joint angles, rates, accelerations and torques. The method implements an augmented Lagrangian solver on a decoupled form of the PKM dynamics in order to solve the resulting non-linear constrained optimal control problem. Using outcomes of the offline-planning, the data-driven neuro-fuzzy inference system is built to learn, capture to and optimize the desired dynamic behavior of the PKM. The optimized system is used to achieve near-optimal online planning with a reasonable time complexity. The effectiveness of the method is illustrated through a set of simulation experiments proving the technique on a 2-degrees of freedom planar PKM.

Parallel kinematic machine trajectory planning method

The method of predicting gas composition in a multistage separator includes solutions to the regression problem of gas composition prediction that are developed using an ensemble of hybrid computational intelligence (CI) models. Three separate homogeneous and one heterogeneous ensemble of hybrid computational intelligence (EHCI) models are developed using a parallel scheme. The homogeneous models have the same types of CI models used as base learners, and the heterogeneous model has of different types of CI models used as base learners. Various popular CI models, including multi-layer perceptron (MLP), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS), are used as base learners of ensemble models.

Method of predicting gas composition

The method of performing facial recognition using genetic algorithm-modified fuzzy linear discriminant analysis (LDA) is based on the Fisherface LDA, with a modification being made in calculation of the membership function. Particularly, the membership function is computed using a pair of parameters &#x3b1; and &#x3b2;, which are optimized by a genetic algorithm in order to minimize the recognition error.

Method of performing facial recognition using genetically modified fuzzy linear discriminant analysis

Growing community of inventors

Dhahran, Saudi Arabia

Amar Khoukhi