Sheikh Izzal Azid

Unmanned aerial vehicles (UAVs) have been employed for a variety of inspection and monitoring tasks, including agricultural applications and search and rescue (SAR) in remote areas. However, traditional monitoring methods tend to focus on optimizing one aspect. This study aims to propose a complete framework by integrating advanced methods to provide a robust and accurate path coverage solution. The combination of edge detection and area decomposition with a pathfinding algorithm can improve the overall performance. An effective edge detection model is developed that simultaneously detects the boundary and segments the area of interest (AOI) from the aerial land images and provides precise area mapping of the area. An intuitive grid decomposition with grid-to-graph mapping improves the flexibility of the area decomposition and ensures maximal coverage and safe operation routes for the UAVs. Finally, a robust modified simulated annealing (MSA) algorithm is introduced to determine the shortest path coverage route. The performance of the proposed methodology is tested on aerial imagery. Area decomposition ensures that there are no gaps in the AOI during the coverage planning. The MSA algorithm obtains the minimum length cost, charge consumption cost, and minimum number of turns to cover the area. It is shown that the integration of these techniques enhances the performance of the coverage path planning (CPP). A comparison of the proposed approach with benchmark algorithms further demonstrates its effectiveness. This study contributes to creating a complete CPP application for UAVs, which may assist with precision agriculture as well as safe and secure rescue operations.

Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.

With the advancement in robotics technology over the recent years, underwater robots’ design and development are gaining interest. Unmanned underwater vehicles (UUVs) have many applications in aquaculture, deep-sea exploration, research, and enhanced rescue tasks. However, various factors must be considered when developing any underwater vehicle system to explore the deep ends of the underwater world. In this paper, we develop the most suitable model for understanding various system parameters. The new mathematical model considers certain constraints and external disturbances exerted on the system. Also, a control strategy is suggested for the UUV’s stability and robustness. The suggested observer and model are simple, allowing for accurate estimations of all system states and the global impacts of unknown limped perturbations with a minimal computational cost.

This paper focuses on the control of two self-driven vehicles (leader-follower) in a multi-obstacle environment, while maintaining formation. The acceleration-based control input design governs the overall movement and control of the rovers. This is accomplished through the application of APF functions that support the leader robot to reach the desired target while avoiding obstacles and maintaining formation. The Lyapunov theorem was used for the control design of the leader and follower vehicles. An effective mathematical model was designed and run through the MATLAB software for simulation verification. The simulation results obtained illustrate the behavior of the leader-follower vehicles with respect to the controllers designed. Therefore, this paper looks at the efficiency of the vehicles to converge at a predefined target, from random points in a predefined workspace, while avoiding fixed and moving obstacles. The technique may be applied in transportation and defense sectors where environments are a risk prone to human health or safety.

This article presents a simple yet novel method of designing a fractional-order proportional derivative (namely, PDμ) controller for all types of integrating plants. Considering the importance of the direct synthesis approach, the method obtains robust closed-loop performance. The setpoint parameter is obtained using the multi-optimization Pareto solution, considering the optimal values in control efforts and performance index together. The numerical investigations have shown improved servo and regulatory responses compared to recently published strategies with fractional orders. It is also known that classical PIDs cannot accurately follow a ramp setpoint. A real-world situation also demands that the reference inputs become an acceleration ramp type. The proposed technique can handle rising ramp setpoints with plant uncertainty and measurement noise. Hardware verification is also performed on a quadrotor minidrone to check for real-time issues.

This paper discusses the resilience of the UAV quadrotor to wind disturbances. An unknown input-state observer is presented that uses the Lipschitz method to estimate the internal states and disturbances of the quadrotor and compensate for them by varying the velocities of the four rotors. The observer intends to use existing sensor measurements to estimate the unknown states of the quadrotor and reconstruct the three-dimensional wind disturbances. The estimated states and external disturbances are sent to the PD controller, which compensates for the disturbances to achieve the desired position and attitude, as well as robustness and accuracy. The Lipschitz observer was designed using the LMI approach, and the results were validated using Matlab/Simulink and using the Parrot Mambo mini quadrotor.

In this paper, a fractional-order PI plus D (PIλ-D) structure is proposed for second-order integrating plants. A feedback controller is designed to locate the integrating pole(s) to improve the stability region of the controlling system. Then, the explicit formulae are derived to construct the complex root boundary (CRB) for a given plant model with fractional integrator. By stabilizing approach, it is feasible to put the constraint to calculate the optimal controller parameters. Its effectiveness is presented through numerical examples and a hardware experiment on physical integrating system, namely the altitude control of a quadrotor. The results reveal the improved performances with robustness by the new approach.

The effectiveness of the inferential control scheme based on robust fractional-order proportional integral (FOPI) controller is presented for automatic voltage regulation (AVR) applications. The method uses two degree of freedom (2DOF) in FOPI scheme, which is tuned with the whale optimization algorithm (WOA). Actually, any AVR needs to keep the reactive power of synchronous generator at demand level, stable voltage and frequency of the electrical power supplies. In this study, the 2DOF FOPI controller is proposed to deviate away from the standard integer order, to show the superiority of extra degree of freedom in both structure and controller. To improve the AVR performance, a new performance measure is proposed for the parameter tuning. The method acquires the significant robustness in parameter perturbation and disturbance interruptions. It is observed in the step response quality that the overshoot and settling time can be reduced to approximately by half than the recently published scheme. The various analyses are shown to accept the dominance of the proposed controller in terms of robustness.

This paper aims to develop a fractional control approach for quadrotor trajectory tracking. A fractional-order integrator (PI λ ) with a feedback derivative scheme is designed to control each state of the MIMO system. The designed feedback controller stabilizes the initially unstable decoupled states and widens the stability, while PI λ provides precise trajectory tracking capabilities. After a successful simulation study, the new PI λ -D controller is implemented in the hardware environment. The various performance and load disturbance analyses reveal the effectiveness of the proposed scheme compared with the classical PD/PID controllers. The real-time study also shows that this scheme is a simple yet robust solution.

This paper focuses on the control of quadrotor vehicles without wind sensors that are required to accurately track low-speed trajectories in the presence of moderate yet unknown wind gusts. By modeling the wind disturbance as exogenous inputs, and assuming that compensation of its effects can be achieved through quasi-static vehicle motions, this paper proposes an innovative estimation and control scheme comprising a linear dynamic filter for the estimation of such unknown inputs and requiring only position and attitude information. The filter is built upon results from Unknown Input Observer theory and allows estimation of wind and vehicle state without measurement of the wind itself. A simple feedback control law can be used to compensate for the offset position error induced by the disturbance. The proposed filter is independent of the recovery control scheme used to nullify the tracking error, as long as the corresponding applied rotor speeds are available. The solution is first checked in simulation environment by using the Robot Operating System middleware and the Gazebo simulator and then experimentally validated with a quadcopter system flying with real wind sources.