Papers by Zaharuddeen Haruna
Drones
Livestock management is an emerging area of application of the quadrotor, especially for monitori... more Livestock management is an emerging area of application of the quadrotor, especially for monitoring, counting, detecting, recognizing, and tracking animals through image or video footage. The autonomous operation of the quadrotor requires the development of an obstacle avoidance scheme to avoid collisions. This research develops an obstacle avoidance-based autonomous navigation of a quadrotor suitable for outdoor applications in livestock management. A Simulink model of the UAV is developed to achieve this, and its transient and steady-state performances are measured. Two genetic algorithm-based PID controllers for the quadrotor altitude and attitude control were designed, and an obstacle avoidance algorithm was applied to ensure the autonomous navigation of the quadrotor. The simulation results show that the quadrotor flies to the desired altitude with a settling time of 6.51 s, an overshoot of 2.65%, and a steady-state error of 0.0011 m. At the same time, the attitude controller r...
Development of a Dynamic Cuckoo Search Algorithm
This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo... more This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo search algorithm (dCSA). The standard cuckoo search algorithm is a metaheuristics search algorithm that mimic the behavior of brood parasitism of some cuckoo species and Levy flight behavior of some fruit flies and birds. It, however uses fixed value for control parameters (control probability and step size) and this method have drawbacks with respect to quality of the solutions and number of iterations to obtain optimal solution. Therefore, the dCSA is developed to address these problems in the CSA by introducing random inertia weight strategy to the control parameters so as to make the control parameters dynamic with respect to the proximity of a cuckoo to the optimal solution. The developed dCSA was compared with CSA using ten benchmark test functions. The results obtained indicated the superiority of dCSA over CSA by generating a near global optimal result for 9 out of the ten bench...
Obstacle Avoidance Scheme Based Elite Opposition Bat Algorithm for Unmanned Ground Vehicles
Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment ... more Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment without an onboard human operator but can be controlled autonomously using an obstacle avoidance system or by a human operator from a remote location. In this research, an obstacle avoidance scheme-based elite opposition bat algorithm (EOBA) for UGVs was developed. The obstacle avoidance system comprises a simulation map, a perception system for obstacle detection, and the implementation of EOBA for generating an optimal collision-free path that led the UGV to the goal location. Three distance thresholds of 0.1 m, 0.2 m, and 0.3 m was used in the obstacle detection stage to determine the optimal distance threshold for obstacle avoidance. The performance of the obstacle avoidance scheme was compared with that of bat algorithm (BA) and particle swarm optimization (PSO) techniques. The simulation results show that the distance threshold of 0.3 m is the optimal threshold for obstacle avoidan...
Performance Analysis of a Ball-on-Sphere System using Linear Quadratic Regulator Controller
2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON)
Graphical User Interface (GUI) for Position and Trajectory Tracking Control of the Ball and Plate System Using H-Infinity Controller
In this paper, a graphical user interface (GUI) for position and trajectory tracking of the ball ... more In this paper, a graphical user interface (GUI) for position and trajectory tracking of the ball and plate system (BPS) control scheme using the double feedback loop structure i.e. a loop within a loop is proposed. The inner and the outer loop was designed using linear algebraic method by solving a set of Diophantine equations and sensitivity function. The results were simulated in MATLAB 2018a, and the trajectory tracking was displayed on a GUI, which showed that the plate was able to be stabilized at a time of 0.3546 seconds, and also the ball settled at 1.7087 seconds, when a sinusoidal circular reference trajectory of radius 0.4m with an angular frequency of 1.57rad/sec was applied to the BPS, the trajectory tracking error was 0.0095m. This shows that the controllers possess the following properties for the BPS, which are; good adaptability, strong robustness and a high control performance.
This paper presents the position and trajectory tracking control scheme for the ball and plate sy... more This paper presents the position and trajectory tracking control scheme for the ball and plate system (BPS) using the double feedback loop structure (a loop within a loop) for effective control of the system. The inner loop was designed using linear algebraic method by solving a set of Diophantine equations. The outer inner loop was designed using sensitivity approach. Simulation results showed that the plate was stabilized at 0.3546 seconds, and the ball was able to settle at 1.7087 seconds, when given a circular trajectory of radius 0.4 m with an angular frequency of 1.57 rad/sec, with a trajectory tracking error of 0.0095 m, which shows that the controllers have adaptability, strong robustness and control performance for the ball and plate system.
Development of a modified bat algorithm using elite opposition — Based learning
2017 IEEE 3rd International Conference on Electro-Technology for National Development (NIGERCON), 2017
This research work presents the development of a modified bat algorithm (mBA) using elite opposit... more This research work presents the development of a modified bat algorithm (mBA) using elite opposition — based learning. The bat algorithm (BA), which is a nature inspired meta-heuristic algorithm, works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The mBA is developed by modifying the BA with elite opposition — based learning (EOBL) in order to diversify the solution search space and the inertial weight in order to improve its exploitation capability. The performance of the proposed mBA was compared with that of the standard BA using seven benchmark optimization test functions. The simulation results showed that the mBA is superior to the standard BA by obtaining global optimal result of most of the test functions. All simulations were carried out using MATLAB R2013b.
Yanbu Journal of Engineering and Science, 2021
This research work presents the development of an optimal path planning using elite opposition ba... more This research work presents the development of an optimal path planning using elite opposition based bat algorithm (EOBA) for mobile robot, such that the robot avoids obstacle(s) without making contact with them. The bat algorithm (BA) is a nature inspired meta-heuristic algorithm that works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The EOBA is developed by modifying the BA with the elite opposition-based learning (EOBL) so as to diversify the solution search space and the inertial weight in order to balance its exploration and exploitation. The performance of the proposed path planning technique was compared with that of the standard BA based on the ability to generate an optimal path for a mobile robot in a developed simulation environment. The simulation results showed that EOBA provide an optimal path with minimum elapsed time as compared to that of the standard BA. All simulations were carried out using MATLAB R2013b.
his research aimed at development of a dynamic path planning technique for autonomous mobile robo... more his research aimed at development of a dynamic path planning technique for autonomous mobile robot using a modified bat algorithm. Autonomous mobile robots are programmable and mechanical device with the ability of moving from one location (called the source location) to another position (known as the target location) in an environment containing obstacles without human intervention. Thus, for a mobile robot to be autonomous, it has to be intelligent enough in perceiving the environment so as to acquire information in the environment and make decision based on it. Therefore, path planning becomes essential for the autonomous mobile robot to reach its target location. To achieve this, an objective function was modelled in form of distance function using the coordinates of the source and target locations. A path planning technique was then developed using modified bat algorithm that optimized the objective function to generate an optimal collision free motion path for the autonomous m...
Path Tracking Control of Four Wheel Unmanned Ground Vehicle Using Optimized FOPID Controller
2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
IEEE 3rd International Conference on Electro-Technology for National Development, 2017
This research work presents the development of a modified bat algorithm (mBA) using elite opposit... more This research work presents the development of a modified bat algorithm (mBA) using elite opposition-based learning. The bat algorithm (BA), which is a nature inspired meta-heuristic algorithm, works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The mBA is developed by modifying the BA with elite opposition-based learning (EOBL) in order to diversify the solution search space and the inertial weight in order to improve its exploitation capability. The performance of the proposed mBA was compared with that of the standard BA using seven benchmark optimization test functions. The simulation results showed that the mBA is superior to the standard BA by obtaining global optimal result of most of the test functions. All simulations were carried out using MATLAB R2013b.
Yanbu Journal of Engineering and Science, 2018
This research work presents the development of an optimal path planning using elite opposition ba... more This research work presents the development of an optimal path planning using elite opposition based bat algorithm (EOBA) for mobile robot, such that the robot avoids obstacle(s) without making contact with them. The bat algorithm (BA) is a nature inspired meta-heuristic algorithm that works on the basis of the echolocation behavior of bat. It, however, has a poor exploration capability leading to it easily getting stuck in local optima. The EOBA is developed by modifying the BA with the elite opposition-based learning (EOBL) so as to diversify the solution search space and the inertial weight in order to balance its exploration and exploitation. The performance of the proposed path planning technique was compared with that of the standard BA based on the ability to generate an optimal path for a mobile robot in a developed simulation environment. The simulation results showed that EOBA provide an optimal path with minimum elapsed time as compared to that of the standard BA. All simulations were carried out using MATLAB R2013b.
Covenant Journal of Informatics and Communication Technology, 2021
Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment ... more Unmanned Ground Vehicles (UGVs) are intelligent vehicles that operate in an obstacle environment without an onboard human operator but can be controlled autonomously using an obstacle avoidance system or by a human operator from a remote location. In this research, an obstacle avoidance scheme-based elite opposition bat algorithm (EOBA) for UGVs was developed. The obstacle avoidance system comprises a simulation map, a perception system for obstacle detection, and the implementation of EOBA for generating an optimal collision-free path that led the UGV to the goal location. Three distance thresholds of 0.1 m, 0.2 m, and 0.3 m was used in the obstacle detection stage to determine the optimal distance threshold for obstacle avoidance. The performance of the obstacle avoidance scheme was compared with that of bat algorithm (BA) and particle swarm optimization (PSO) techniques. The simulation results show that the distance threshold of 0.3 m is the optimal threshold for obstacle avoidance provided that the size of the obstacle does not exceed the size of the UGV. The EOBA based scheme when compared with BA and PSO schemes obtained an average percentage reduction of 21.82% in terms of path length and 60% in terms of time taken to reach the target destination. The uniqueness of this approach is that the UGV avoid collision with an obstacle at a distance of 0.3 m from nearby obstacles as against taking three steps backward before avoiding obstacle.
International Journal of Mechatronics, Electrical and Computer Technology, 2019
his research aimed at development of a dynamic path planning technique for autonomous mobile robo... more his research aimed at development of a dynamic path planning technique for autonomous mobile robot using a modified bat algorithm. Autonomous mobile robots are programmable and mechanical device with the ability of moving from one location (called the source location) to another position (known as the target location) in an environment containing obstacles without human intervention. Thus, for a mobile robot to be autonomous, it has to be intelligent enough in perceiving the environment so as to acquire information in the environment and make decision based on it. Therefore, path planning becomes essential for the autonomous mobile robot to reach its target location. To achieve this, an objective function was modelled in form of distance function using the coordinates of the source and target locations. A path planning technique was then developed using modified bat algorithm that optimized the objective function to generate an optimal collision free motion path for the autonomous mobile robot. The performance of the developed algorithm was determined by implementing in an unknown static environment under different complexities of obstacles. The simulation result obtained showed that the path planning algorithm was effective for the control of autonomous mobile robot as it generated an optimal path without colliding with obstacles in different environment under different complexities as compared to results obtained using bat algorithm and ant colony optimization algorithm.
Covenant Journal of Informatics & Communication Technology, 2019
This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo... more This research is aimed at the developing a modified cuckoo search algorithm called dynamic cuckoo search algorithm (dCSA). The standard cuckoo search algorithm is a metaheuristics search algorithm that mimic the behavior of brood parasitism of some cuckoo species and Levy flight behavior of some fruit flies and birds. It, however uses fixed value for control parameters (control probability and step size) and this method have drawbacks with respect to quality of the solutions and number of iterations to obtain optimal solution. Therefore, the dCSA is developed to address these problems in the CSA by introducing random inertia weight strategy to the control parameters so as to make the control parameters dynamic with respect to the proximity of a cuckoo to the optimal solution. The developed dCSA was compared with CSA using ten benchmark test functions. The results obtained indicated the superiority of dCSA over CSA by generating a near global optimal result for 9 out of the ten benchmark test functions.
Zaria Journal of Electrical Engineering Technology,, 2019
Custom power devices in power distribution systems compensates reactive power, minimizes power lo... more Custom power devices in power distribution systems compensates reactive power, minimizes power loss and enhances voltage profile. In this paper, a Linear Adaptive Bacterial Foraging Algorithm (LABFA) is applied for the allocation of Distribution Static Compensator (D-STATCOM) in radial distribution networks with the aim of minimizing active power losses and enhancing voltage profile. Power losses and voltage deviation are computed from a direct power flow method based on the Bus Injected to Branch Current (BIBC) technique. A multi-objective function comprising of total active power loss (PT(loss)) and voltage deviation (VD) is formulated. The LABFA is modelled with a linear step-size unit, and the cell-to-cell signalling mechanism is eliminated. The effectiveness of the LABFA technique is tested on a standard IEEE 33-bus network. The proposed method produced a 28.96 % reduction in total active power loss and 43.11 % improvement in overall network voltage profile as compared to the base-case scenario. Also, the LABFA results procured 0.83 % reduction in active power loss and a 11.95 % improvement in overall system voltage profile as compared to an existing bat algorithm (BA) approach. .
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Papers by Zaharuddeen Haruna