Due to the significant advantages of flexible link manipulators (FLMs) and the high demand for developing a robust controller in order to perform fast and accurate operations, this research proposes the PD/H-∞ integrated controller for the purpose of position tracking and vibration suppression of a FLM system and verifies its robustness by utilizing longer lengths and test the proposed controller under a disturbance. The PD/H-∞ integrated controller was compared with the Linear quadratic regulator (LQR) controller in terms of position tracking and vibration suppression for the lengths of 50 cm, 75 cm and 100 cm, then the controllers were assessed under a disturbance for the three lengths of the FLM system. The PD/H-∞ integrated controller has shown higher capability to control longer lengths and to reject a disturbance than the LQR controller which has been verified via simulation using MATLAB/Simulink software.

SUMMARY There is a high demand for developing effective controllers to perform fast and accurate operations for either flexible link manipulators (FLMs) or rigid link manipulators (RLMs). Thus, this paper is beneficial for such vast field, and it is also advantageous and indispensable for researchers who are interested in robotics to have sufficient knowledge about various controllers of FLMs and RLMs as the controllers’ concepts are elaborated in detail. The paper concentrates in critically reviewing classical controllers, intelligent controllers, robust controllers, and hybrid controllers for both FLMs and RLMs. The advantages and disadvantages of the aforementioned control methods are summarized in this paper; it also has a detailed comparison for the controllers in terms of the design difficulty, performance, and the suitability for controlling FLMs or RLMs.

Mathematical modelling plays an important role for robotic manipulators in order to design their particular controllers. Also, it is hard challenge to obtain an accurate mathematical model or obtain a suitable modelling method in such vast field. Thus, this critical review is advantageous and indispensable for researchers who are interested in the area to gain fruitful knowledge on the mathematical modelling methods. This paper is classified based on the type of robotic manipulators such as flexible link manipulators (FLMs), rigid link manipulators (RLMs) and hybrid manipulators which involves rigid links and flexible links. The used modelling methods for FLMs are the assumed mode method, the finite element method, and the lumped parameter method as approximation techniques which are well explained and reviewed. The Lagrangian method has inclusive explanation and review which is widely participated for obtaining the dynamic equations of FLMs, and it is appropriate and commonly employed for modelling RLMs. The Newtonian method, the forward kinematic, and the inverse kinematic are also well discussed and reviewed which are suitable and commonly employed for modelling RLMs. The critical discussion of 170 articles reported in this paper guides researchers to select the suitable method for modelling. This paper reviews the published articles in the period of 2010-2020 except for few older articles for the need of providing essential theoretical knowledge. The advantages and disadvantages of each method are well summarized at the end of the paper. The intelligent identification methods are briefly discussed due to the lack of publications especially on the period of 2010-2020.

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Background Due to the high demand of robots to perform several industrial tasks, such as welding, machining, pick and place, position control in robotics has attracted high attention recently. Controllers’ improvement is also continuous specifically in terms of design simplicity and performance accuracy. This research plans to obtain the SimMechanics model of a two-degree of freedom (DOF) robot and to propose an integrated controller of a proportional–derivative (PD) controller and a fuzzy logic (FL) controller. Methodology The SimMechanics model of the 2-DOF robot is obtained using MATLAB SimMechanics toolbox from a CAD assembly design of the 2-DOF robot. Then, the proposed PD-FL integrated controller is designed and simulated in MATLAB Simulink. The PD controller is widely used for its simplicity, but it doesn’t have a satisfactory performance in difficult tasks. Furthermore, the FL controller is also easy for design and implementation even by non-experts in control theory, but it has the disadvantage of long computational time for multi-input systems due to the increased fuzzy rules. Results The FL controller is integrated with the PD controller for enhanced performance of the 2-DOF robot. The PD-FL integrated controller is developed and tested to control the 2-DOF robot for point-to-point position control and also tip trajectory tracking (TTT) such as triangular TTT and rhombic TTT. Conclusion The PD-FL integrated controller demonstrates enhanced performance compared to the conventional PD controller in both point-to-point position control and TTT. Furthermore, the PD-FL integrated controller has the advantage of less fuzzy rules which helps to overcome the computational time issue of the FL controller.

Robotic manipulation refers to how robots intelligently interact with the objects in their surroundings, such as grasping and carrying an object from one place to another. Dexterous manipulating skills enable robots to assist humans in accomplishing various tasks that might be too dangerous or difficult to do. This requires robots to intelligently plan and control the actions of their hands and arms. Object manipulation is a vital skill in several robotic tasks. However, it poses a challenge to robotics. The motivation behind this review paper is to review and analyze the most relevant studies on learning-based object manipulation in clutter. Unlike other reviews, this review paper provides valuable insights into the manipulation of objects using deep reinforcement learning (deep RL) in dense clutter. Various studies are examined by surveying existing literature and investigating various aspects, namely, the intended applications, the techniques applied, the challenges faced by researchers, and the recommendations adopted to overcome these obstacles. In this review, we divide deep RL-based robotic manipulation tasks in cluttered environments into three categories, namely, object removal, assembly and rearrangement, and object retrieval and singulation tasks. We then discuss the challenges and potential prospects of object manipulation in clutter. The findings of this review are intended to assist in establishing important guidelines and directions for academics and researchers in the future.

This paper addresses a rotary motion type of electromagnetic actuator that compares two types of electromagnetic actuators; i.e the Permanent Magnet Switching Flux (PMSF) and the Switching Reluctance (SR) actuator. The Permanent Magnet Switching Flux (PMSF) actuator is the combination of permanent magnets (PM) and the Switching Reluctance (SR) actuator. The force optimizations are accomplished by manipulating the actuator parameters; i.e. (i) the poles ratio of the stator and rotor; (ii) the actuator’s size; (iii) the number of winding turns; and (iv) the air gap thickness between the stator and rotor through Finite Element Analysis Method (FEM) using the ANSYS Maxwell 3D software. The materials implemented in the actuator’s parameters optimizations are readily available materials, especially in Malaysia. The excitation current used in FEM analysis for both actuators was between 0A and 2A with interval of 0.25A. Based on the FEM analyses, the best result was achieved by the Permanent Magnet Switching Flux (PMSF) actuator. The PMSF actuator produced the largest magnetostatic thrust force (4.36kN) once the size is scaled up to 100% with the input current, 2A respectively. The maximum thrust force generated by the Switching Reluctance (SR) actuator was 168.85μN, which is significantly lower in compared to the results of the PMSF actuator.

Abstract. This project is mainly about the dynamic modelling and parameter estimation of Unmanned Air Cushion Vehicle (UACV). The purpose of developing mathematical model of the Unmanned Air Cushion Vehicle (UACV) is due to its under actuated nonlinearities where it has less input compared to the output required. This system able to maneuver over land, water and other surfaces either at certain speed or maintain at a stationary position. In order to model the UACV, the system is set to have two propellers which are responsible to lift the vehicle by forcing high pressure air under the system. The air inflates the "skirt" under the vehicle, causing it to rise above the surface while another two propellers are used to steer the UACV forward. UACV system can be considered as under actuated since it possess fewer controller inputs that its degree of freedom. The system's motions are defined by the six degrees of freedom which are; heaved, sway and surge. Another three components are rotational motions which can be elaborated as roll, pitch and yaw. The problem related to UACV is normally related to obtaining accurate parameters of the system to be included into the mathematical model of the system. This is due to the body inertia of the system during the static and moving condition. Besides, the air that flows into the UACV skirt to create the cushion causes imbalance and will affect the system stability and controllability. In this research, UACV need to be mathematically modelled using Euler-Lagrange method. Then, parameters of the system can be obtained through direct calculation and Solidworks software. The parameters acquired are compared and verified using simulation and experimental studies.