Mohammad AlShabi Avatar

Mohammad AlShabi

Assistant Professor

Currently working as an assistant professor for the Department of Mechanical Engineering at the University of Sharjah. I am working on UAVs, UGVs, manipulators, mobile robots, control, estimation, system identification, artificial intelligent controls and systems, and mathematical models projects.


Assistant Professor  
University of Sharjah, September 2015 to Present, Sharjah United Arab Emirates

Assistant Professor  
Jordan University of Science and Technology, June 2011 to August 2015, Irbid Jordan

Part time. Taught Mechanical Design II, Automatic Control, Thermodynamics II, Design of Thermal System

Assistant Professor  
Philadelphia University, March 2011 to August 2015, Amman Jordan


McMaster Univ/Ontario  
Doctor of Philosophy, Mechanical/Mechatronics Engineering, Sep, 2006 to Mar, 2011

Jordan University of Science & Technology  
Master Of Science, Mechanical/Mechatronics Engineering, Aug, 2002 to Jan, 2005

Jordan University of Science & Technology  
Bachelor Of Science, Mechanical/Mechatronics Engineering, Aug, 1998 to Jun, 2002


UAV, Design and Implement of a Quadcopter  by  Tarek Tutunji, Mohammad AlShabi    
January, 2013 - October, 2013

Design and Implement a Quadcopter  by  Tarek Tutunji, Mohammad AlShabi    
January, 2014 - October, 2014

Designing and implementing a smart vacuum cleaner.  by  Mohammad AlShabi, Abdullah Al-Mutiri, Ahmad Derbas    
January, 2012 - January, 2013
This scans an empty room space and optimizes the path that it should follow to clean that room.

Designing an automated car’s gate which is operated by an image processing program  by  Mohammad AlShabi    
January, 2001 - June, 2002

Developing software that can be used to count customers that enter a place using image processing  by  Mohammad AlShabi    
January, 2005 - January, 2006

Designing and implementing a Tripod  by  Ibrahim Alnoimi, Mohammad AlShabi, Saleh Alsaleh, Haithm AlAdam    
January, 2015 - Present


An Improved K-Means Clustering Algorithm for Two Half-Moon Classification     
Published by (International Symposium on Mechatronics Applications, ISMA 2015)
Authors: L. Sawaqed, M. Al-Shabi, S. AlShaer and I Salameh.  Published December 30, 2015

Classification problems of machine learning use supervised learning under specific targets to determine the class that a new observation belongs to. Two half-moon rings classification is a well-known benchmark problem. The problem complexity increases when the new observation is located in the region of intersection of the two half-moons. This work presents a new algorithm that combines an evolutionary algorithm with the K-means algorithm for two half-moon clustering and classification. A data set of two half-moon rings with complex overlap situations is investigated and compared to the conventional K-means clustering algorithm. The data set represent the Cartesian coordinates of each data point. In the proposed algorithm, whitening approach is considered so the overlapped points do not affect the clustering and classification process since they belong to more than one class (ring) at the same time. The proposed algorithm obtains the optimal cluster centers using genetic algorithm. Simulation results showed enhancement over the K-means performance with different ring dimensions, and overlap situations. This work points out the value of merging different algorithms to attain their combined advantages when solving such complicated problems.

A Comparison of Vibration Control Strategies for a Flexible-Link Robot Arm,     
Published by (International Symposium on Mechatronics Applications, ISMA 2015)
Authors: S. Gadsden and M. Al-shabi.  Published 

“Flexible links in a robot arm often experience unwanted vibrations at the end points typically due to elastic deflections and system disturbances. This leads to reduced endpoint positioning accuracy, as well as negatively affects the overall control performance of the robot arm. Typical control strategies introduce active damping to reduce oscillations at the robot arm end points, whereas other methods apply interaction strategies based on closed-loop inverse kinematics. Other controllers, such as proportional-integral-derivative (PID) methods and the robust sliding mode controller (SMC), have also been applied to robot arms in an effort to minimize endpoint vibration. This paper studies two popular vibration control strategies found in literature, namely PID and SMC. Simulation results are generated based on applications to a flexible-link robot arm, and the results are compared and discussed.

Unmanned Aerial Vehicles parameter estimation using Artificial Neural Networks and Iterative Bi-Section Shooting method     
Published by (Applied Soft Computing)
Authors: KS Hatamleh, M Al-Shabi, A Al-Ghasem and AA Asad.  Published 

Quadrotor Unmanned Aerial Vehicles (UAVs) can perform numerous tasks fearless of unnecessary loss of human life. Lately, to enhance UAV control performance, system identification and states estimation has been an active field of research. This work presents a simulation study that investigates unknown dynamics model parameters estimation of a Quadrotor UAV under presence of noisy feedback signals. The latter constitute a challenge for UAV control performance especially with the presence of uncertainties. Therefore, estimation techniques are usually used to reduce the effect of such uncertainties. In this paper, three estimation methods are presented to estimate unknown parameters of the “OS4” Quadrotor. Those methods are Iterative Bi-Section Shooting method “IBSS”, Artificial Neural Network method “ANN”, and “Hybrid ANN_IBSS”, which is a novel method that integrates ANN with IBSS. The “Hybrid ANN_IBSS” is the main contribution of this work. Percentage error of the estimated parameters is used to evaluate accuracy of the aforementioned methods. Results show that IBSS and ANN are capable of estimating most of the parameters even with the presence of noisy feedback signals. However, their performance lacks accuracy when estimating small-value parameters. On the other hand, Hybrid ANN_IBSS achieved higher estimation accuracy compared to the other two methods. Accurate parameter estimation is expected to enhance reliability of the “OS4” dynamics model and hence improve control quality.

Sigma-Point Filters in Robotic Applications     
Published by (Intelligent Control and Automation )
Authors: M. Al-Shabi.  Published 

Sigma-Point Kalman Filters (SPKFs) are popular estimation techniques for high nonlinear system applications. The benefits of using SPKFs include (but not limited to) the following: the easiness of linearizing the nonlinear matrices statistically without the need to use the Jacobian matrices, the ability to handle more uncertainties than the Extended Kalman Filter (EKF), the ability to handle different types of noise, having less computational time than the Particle Filter (PF) and most of the adaptive techniques which makes it suitable for online applications, and having acceptable performance compared to other nonlinear estimation techniques. Therefore, SPKFs are a strong candidate for nonlinear industrial applications, i.e. robotic arm. Controlling a robotic arm is hard and challenging due to the system nature, which includes sinusoidal functions, and the dependency on the sensors’ number, quality, accuracy and functionality. SPKFs provide with a mechanism that reduces the latter issue in terms of numbers of required sensors and their sensitivity. Moreover, they could handle the nonlinearity for a certain degree. This could be used to improve the controller quality while reducing the cost. In this paper, some SPKF algorithms are applied to 4-DOF robotic arm that consists of one prismatic joint and three revolute joints (PRRR). Those include the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF), and the Central Differences Kalman Filter (CDKF). This study gives a study of those filters and their responses, stability, robustness, computational time, complexity and convergences in order to obtain the suitable filter for an experimental setup.

The sigma-point central difference smooth variable structure filter application into a robotic arm     
Published by (the 12th International Multi-Conference on Systems, Signals & Devices (SSD))
Authors: M. Al-Shabi, M. Bani Yonis, and K. Hatamleh.  Published 

Recent Mobile-robots/Robotic-manipulators based industrial applications require accurate control despite the blurry and the noisy feedback signals. As a result, there is an increasing demand for new estimation techniques and filters to overcome accompanying system disturbances especially when nonlinear-ity present in the system. Industrial applications control quality will improve if a robust filter is used to reduce the effect of noise and to improve the quality of feedback signals by handling those nonlinearities. In this work, a new filter that combines the Smooth Variable Structure Filter (SVSF) with the Central Difference Kalman Filter (CDKF) is proposed. The presented method results in robust, stable and accurate estimation algorithm for motion states which are measured to be feedback signals. Results are demonstrated by applying the proposed filter to estimate the states of a 4-axis industrial robot arm with one Prismatic, and three Rotational joints (PRRR).

The cubature smooth variable structure filter estimation strategy applied to a quadrotor controller     
Published by (SPIE)
Authors: M. Al-Shabi, S. A. Gadsden, S. A. Wilkerson.  Published 

" Unmanned aerial systems (UAS) are becoming increasingly popular in industry, military, and social environments. An UAS that provides good operating performance and robustness to disturbances is often quite expensive and prohibitive to the general public. To improve UAS performance without affecting the overall cost, an estimation strategy can be implemented on the internal controller. The use of an estimation strategy or filter reduces the number of required sensors and power requirement, and improves the controller performance. UAS devices are highly nonlinear, and implementation of filters can be quite challenging. This paper presents the implementation of the relatively new cubature smooth variable structure filter (CSVSF) on a quadrotor controller. The results are compared with other state and parameter estimation strategies.

Square-root formulation of the SVSF with applications to nonlinear target tracking problems     
Published by (SPIE)
Authors: S. A. Gadsden, M. Al-Shabi and T. Kirubarajan.  Published 

The smooth variable structure filter (SVSF) is a state and parameter estimation strategy based on sliding mode concepts. It has seen significant development and research activity in recent years. In an effort to improve upon the numerical stability of the SVSF, a square-root formulation is derived. The square-root SVSF is based on Potter’s algorithm. The proposed formulation is computationally more efficient and reduces the risks of failure due to numerical instability. The new strategy is applied on target tracking scenarios for the purposes of state estimation. The results are compared with the popular Kalman filter.

Two-pass smoother based on the SVSF estimation strategy     
Published by (SPIE)
Authors: S. A. Gadsden, M. Al-Shabi and T. Kirubarajan.  Published 

The smooth variable structure filter (SVSF) has seen significant development and research activity in recent years. It is based on sliding mode concepts, which utilizes a switching gain that brings an inherent amount of stability to the estimation process. In this paper, the SVSF is reformulated to present a two-pass smoother based on the SVSF gain. The proposed method is applied on an aerospace flight surface actuator, and the results are compared with the popular Kalman-based two-pass smoother.

The Unscented Smooth Variable Structure Filter Application into a Robotic Arm     
Published by (ASME 2014 International Mechanical Engineering Congress and Exposition)
Authors: M. Al-Shabi and K. Hatamleh.  Published 

Robotic arms are becoming increasingly popular in industrial applications. However, improving the response and accuracy of robotic arms while reducing their cost has become challenging. The Kalman Filter (KF) has attracted a significant amount of research as it improves the control quality by filtering the feedback signal. On the other hand, KF solution becomes very challenging when the system under study is nonlinear. This work proposes a new online state estimation algorithm that combines the Smooth Variable Structure Filter (SVSF) with the Unscented Kalman Filter (UKF). The proposed method overcomes the limitations of SVSF and UKF in terms of stability and sensitivity to noise. A simulation study is conducted in this paper to demonstrate the results of the proposed method when applied to estimate the states of a PRRR industrial robotic arm.

Application of SMC into a PRRR Robotic     
Published by (ASME 2014 International Mechanical Engineering Congress and Exposition)
Authors: Khaled S. Hatamleh, M Al-Shabi, Qais A. Khasawneh and Mohammad Abo Al-Asal.  Published 

Industrial robotic arms are widely used nowadays. Accuracy and efficiency that fulfill user’s requirements are achieved through robust controller. This paper investigates dynamics modeling and control of a four DOF (PRRR) robot that is dedicated to perform a Pick-and-Place move of a certain product. The arm is undergoing manufacturing process. Forward and inverse kinematics solutions are introduced to solve the joint space trajectories associated with the desired End Effector (EE) Cartesian space path. The performance of two controllers under the presence of model uncertainties is inspected through a simulation study; Non-Linear Feedback Control (NLFC) and Sliding Mode Control (SMC) are designed and tested over the required joint space trajectories and Cartesian space path. Results showed that NLFC achieved better results than SMC in terms of RMSE when model uncertainties were absent. However, when model uncertainties were introduced, SMC performance was more robust than NLFC. Simulation results are very encouraging towards using the SMC over the actual robotic arm.

Combined Cubature Kalman and Smooth variable Structure Filtering: A Robust Nonlinear Estimation Strategy     
Published by (Elsevier, Signal Processing)
Authors: S. A. Gadsden, M. Al-Shabi, I. Arasaratnam and S. Habibi.  Published 

In this paper, nonlinear state estimation problems with modeling uncertainties are considered. As demonstrated recently in literature, the cubature Kalman filter (CKF) provides the closest known approximation to the Bayesian filter in the sense of preserving second-order information contained in noisy measurements under the Gaussian assumption. The smooth variable structure filter (SVSF) has also been recently introduced and has been shown to be robust to modeling uncertainties. In an effort to utilize the accuracy of the CKF and the robustness of the SVSF, the CKF and SVSF have been combined resulting in an algorithm referred to as the CK–SVSF. The robustness and accuracy of the CK–SVSF was validated by testing it on two different computer problems, namely, a target tracking problem and the estimation of the effective bulk modulus in an electrohydrostatic actuator.

Kalman Filtering Strategies Utilizing the Chattering Effects of the Smooth Variable Structure Filter     
Published by (Elsevier, Signal Processing)
Authors: M. Al-Shabi, S. A. Gadsden and S. Habibi.  Published 

The Kalman filter (KF) remains the most popular method for linear state and parameter estimation. Various forms of the KF have been created to handle nonlinear estimation problems, including the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The robustness and stability of the EKF and UKF can be improved by combining it with the recently proposed smooth variable structure filter (SVSF) concept. The SVSF is a predictor–corrector method based on sliding mode concepts, where the gain is calculated based on a switching surface. A phenomenon known as chattering is present in the SVSF, which may be used to determine changes in the system. In this paper, the concept of SVSF chattering is introduced and explained, and is used to determine the presence of modeling uncertainties. This knowledge is used to create combined filtering strategies in an effort to improve the overall accuracy and stability of the estimates. Simulations are performed to compare and demonstrate the accuracy, robustness, and stability of the Kalman-based filters and their combinations with the SVSF.

Iterative Smooth Variable Structure Filter for Parameter Estimation     
Published by (ISRN Signal Processing)
Authors: M. Al-Shabi and S. Habibi.  Published 

The smooth variable structure filter (SVSF) is a recently proposed predictor-corrector filter for state and parameter estimation. The SVSF is based on the sliding mode control concept. It defines a hyperplane in terms of the state trajectory and then applies a discontinuous corrective action that forces the estimate to go back and forth across that hyperplane. The SVSF is robust and stable to modeling uncertainties making it suitable for fault detection application. The discontinuous action of the SVSF results in a chattering effect that can be used to correct modeling errors and uncertainties in conjunction with adaptive strategies. In this paper, the SVSF is complemented with a novel parameter estimation technique referred to as the iterative bi-section/shooting method (IBSS). This combined strategy is used for estimating model parameters and states for systems in which only the model structure is known. This combination improves the performance of the SVSF in terms of rate of convergence, robustness, and stability. The benefits of the proposed estimation method are demonstrated by its application to an electrohydrostatic actuator.

Estimation Strategies for the Condition Monitoring of a Battery System in a Hybrid Electric Vehicle     
Published by (ISRN Signal Processing)
Authors: S. A. Gadsden, M. Al-Shabi, and S. R. Habibi.  Published 

This paper discusses the application of condition monitoring to a battery system used in a hybrid electric vehicle (HEV). Battery condition management systems (BCMSs) are employed to ensure the safe, efficient, and reliable operation of a battery, ultimately to guarantee the availability of electric power. This is critical for the case of the HEV to ensure greater overall energy efficiency and the availability of reliable electrical supply. This paper considers the use of state and parameter estimation techniques for the condition monitoring of batteries. A comparative study is presented in which the Kalman and the extended Kalman filters (KF/EKF), the particle filter (PF), the quadrature Kalman filter (QKF), and the smooth variable structure filter (SVSF) are used for battery condition monitoring. These comparisons are made based on estimation error, robustness, sensitivity to noise, and computational time.

Text Detection and Character Recognition Using Fuzzy Image Processing     
Published by (Journal of Electrical Engineering)
Authors: M. Alata and M. Al-Shabi.  Published 

The current investigation presents an algorithm and software to detect and recognize character in an image. Three types of fonts were under investigation, namely, case (I): Verdana, case (II): Arial and case (III): Lucida Console. The font size will be within the range of 17–29 font size. These types were chosen because the characters have low variance and there is less redundancy in the single character. Also, they have no breakpoints in the single character or merge in group of characters as noticed in Times New Roman type. The proposed algorithm assumed that there are at least three characters of same color in a single line, the character is on its best view which means no broken point in a single character and no merge between group of characters and at last, a single character has only one color. The basic algorithm is to use the 8-connected component to binirize the image, then to find the characters in the image and recognize them. Comparing this method with other methods, we note that the main difference is in the binarizing technique. Previous work depends mainly on histogram shape. They calculate the histogram, and then smooth it to find the threshold points. These methods are not working well when the text and background colors are very similar, and also if it may create an area of negative text. The negative text may be treated as a graphic region which may affect the system efficiency. The presented approach will take each color alone. This will make the merge between the text and the background unlikely to happen. Also there will be no negative text in the whole image because the negative text to a color will be normal text for another. The shown and discussed results will show the efficiency of the proposed algorithm and how it is different compared with other algorithms.