MODELLING AND CONTROL OF MULTI-FINGERED ROBOT HAND USING INTELLIGENT TECHNIQUES

Research and development of robust multi-fingered robot hand (MFRH) have been going on for more than three decades. Yet few can be found in an industrial application. The difficulties stem from many factors, one of which is that the lack of general and effective control techniques for the manipulation of robot hand. In this research, a MFRH with five fingers has been proposed with intelligent control algorithms. Initially, mathematical modeling for the proposed MFRH has been derived to find the Forward Kinematic, Inverse Kinematic, Jacobian, Dynamics and the plant model. Thereafter, simulation of the MFRH using PID controller, Fuzzy Logic Controller, Fuzzy-PID controller and PID-PSO controller has been carried out to gauge the system performance based parameters such rise time, settling time and percent overshoot

A first approach to a proposal of a soft robotic link acting as a neck

[Abstract] The purpose of this paper is to design a soft robotic neck prototype with two Degrees of Freedom (DOF) and propose a control system based on a fractional order PD controller (FPD). The neck will be able to perform movements of flexion, extension and lateral bending. To achieve these movements, the design is made based on a cable-driven mechanism, with components easy to manufacture in a 3D printer. Simulations are performed to validate the feasibility of the developed parallel robot prototype and the robustness of the proposed control scheme to mass changes at the tip.Ministerio de Economía y Empresa; DPI2016-75330-

DESIGN AND ANALYSIS OF BRAIN EMOTIONAL LEARNING BASED INTELLIGENT CONTROLLER (BELBIC) FOR TEMPERATURE CONTROL

This report presents the project undertaken to design and analyze the performance of temperature control using brain emotional learning control approach. In recent years, theory and applications of intelligent control systems have been a focus in control engineering. Among intelligent control approaches are Artificial Neural Network, Fuzzy Control and Genetic Algorithm

Efficient PID Controller based Hexapod Wall Following Robot

This paper presents a design of wall following behaviour for hexapod robot based on PID controller. PID controller is proposed here because of its ability to control many cases of non-linear systems. In this case, we proposed a PID controller to improve the speed and stability of hexapod robot movement while following the wall. In this paper, PID controller is used to control the robot legs, by adjusting the value of swing angle during forward or backward movement to maintain the distance between the robot and the wall. The experimental result was verified by implementing the proposed control method into actual prototype of hexapod robot

MODELLING AND CONTROL OF MULTI-FINGERED ROBOT HAND USING INTELLIGENT TECHNIQUES

Research and development of robust multi-fingered robot hand (MFRH) have been going on for more than three decades. Yet few can be found in an industrial application. The difficulties stem from many factors, one of which is that the lack of general and effective control techniques for the manipulation of robot hand. In this research, a MFRH with five fingers has been proposed with intelligent control algorithms. Initially, mathematical modeling for the proposed MFRH has been derived to find the Forward Kinematic, Inverse Kinematic, Jacobian, Dynamics and the plant model. Thereafter, simulation of the MFRH using PID controller, Fuzzy Logic Controller, Fuzzy-PID controller and PID-PSO controller has been carried out to gauge the system performance based parameters such rise time, settling time and percent overshoot

Human-robot interaction using a behavioural control strategy

PhD ThesisA topical and important aspect of robotics research is in the area of human-robot interaction (HRI), which addresses the issue of cooperation between a human and a robot to allow tasks to be shared in a safe and reliable manner. This thesis focuses on the design and development of an appropriate set of behaviour strategies for human-robot interactive control by first understanding how an equivalent human-human interaction (HHI) can be used to establish a framework for a robotic behaviour-based approach. To achieve the above goal, two preliminary HHI experimental investigations were initiated in this study. The first of which was designed to evaluate the human dynamic response using a one degree-of-freedom (DOF) HHI rectilinear test where the handler passes a compliant object to the receiver along a constrained horizontal path. The human dynamic response while executing the HHI rectilinear task has been investigated using a Box-Behnken design of experiments [Box and Hunter, 1957] and was based on the McRuer crossover model [McRuer et al. 1995]. To mimic a real-world human-human object handover task where the handler is able to pass an object to the receiver in a 3D workspace, a second more substantive one DOF HHI baton handover task has been developed. The HHI object handover tests were designed to understand the dynamic behavioural characteristics of the human participants, in which the handler was required to dexterously pass an object to the receiver in a timely and natural manner. The profiles of interactive forces between the handler and receiver were measured as a function of time, and how they are modulated whilst performing the tasks, was evaluated. Three key parameters were used to identify the physical characteristics of the human participants, including: peak interactive force (fmax), transfer time (Ttrf), and work done (W). These variables were subsequently used to design and develop an appropriate set of force and velocity control strategies for a six DOF Stäubli robot manipulator arm (TX60) working in a human-robot interactive environment. The optimal design of the software and hardware controller implementation for the robot system has been successfully established in keeping with a behaviour-based approach. External force control based on proportional plus integral (PI) and fuzzy logic control (FLC) algorithms were adopted to control the robot end effector velocity and interactive force in real-time. ii The results of interactive experiments with human-to-robot and robot-to-human handover tasks allowed a comparison of the PI and FLC control strategies. It can be concluded that the quantitative measurement of the performance of robot velocity and force control can be considered acceptable for human-robot interaction. These can provide effective performance during the robot-human object handover tasks, where the robot was able to successfully pass the object from/to the human in a safe, reliable and timely manner. However, after careful analysis with regard to human-robot handover test results, the FLC scheme was shown to be superior to PI control by actively compensating for the dynamics in the non-linear system and demonstrated better overall performance and stability. The FLC also shows superior performance in terms of improved sensitivity to small error changes compared to PI control, which is an advantage in establishing effective robot force control. The results of survey responses from the participants were in agreement with the parallel test outcomes, demonstrating significant satisfaction with the overall performance of the human-robot interactive system, as measured by an average rating of 4.06 on a five point scale. In brief, this research has contributed the foundations for long-term research, particularly in the development of an interactive real-time robot-force control system, which enables the robot manipulator arm to cooperate with a human to facilitate the dextrous transfer of objects in a safe and speedy manner.Thai government and Prince of Songkla University (PSU

Modelling and intelligent control of double-link flexible robotic manipulator

The use of robotic manipulator with multi-link structure has a great influence in most of the current industries. However, controlling the motion of multi-link manipulator has become a challenging task especially when the flexible structure is used. Currently, the system utilizes the complex mathematics to solve desired hub angle with the coupling effect and vibration in the system. Thus, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the improvement on hub angle position and vibration suppression. A laboratory sized DLFRM moving in horizontal direction is developed and fabricated to represent the actual dynamics of the system. The research utilized neural network as the model estimation. Results indicated that the identification of the DLFRM system using multi-layer perceptron (MLP) outperformed the Elman neural network (ENN). In the controllers’ development, this research focuses on two main parts namely fixed controller and adaptive controller. In fixed controller, the metaheuristic algorithms known as Particle Swarm Optimization (PSO) and Artificial Bees Colony (ABC) were utilized to find optimum value of PID controller parameter to track the desired hub angle and supress the vibration based on the identified models obtained earlier. For the adaptive controller, self-tuning using iterative learning algorithm (ILA) was implemented to adapt the controller parameters to meet the desired performances when there were changes to the system. It was observed that self-tuning using ILA can track the desired hub angle and supress the vibration even when payload was added to the end effector of the system. In contrast, the fixed controller degraded when added payload exceeds 20 g. The performance of these control schemes was analysed separately via real-time PC-based control. The behaviour of the system response was observed in terms of trajectory tracking and vibration suppression. As a conclusion, it was found that the percentage of improvement achieved experimentally by the self-tuning controller over the fixed controller (PID-PSO) for settling time are 3.3 % and 3.28 % of each link respectively. The steady state errors of links 1 and 2 are improved by 91.9 % and 66.7 % respectively. Meanwhile, the vibration suppression for links 1 and 2 are improved by 76.7 % and 67.8 % respectively

Computational modeling of the brain limbic system and its application in control engineering

This study mainly deals with the various aspects of modeling the learning processes within the brain limbic system and studying the various aspects of using it for different applications in control engineering. The current study is a multi-aspect research effort which not only requires a background of control engineering, but also a basic knowledge of some biomorphic systems. The main focus of this study is on biological systems which are involved in emotional processes. In mammalians, a part of the brain called the limbic system is mainly responsible for emotional processes. Therefore, general brain emotional processes and specific aspects of the limbic system are reviewed in the early parts of this study. Next, we describe developing a computational model of the limbic system based on these concepts. Since the focus of this study is on the application of the model in engineering systems and not on the biological concepts, the model established is not a very complicated model and does not include all the components of the limbic system. In fact, we are trying to develop a model which captures the minimal and basic properties of the limbic system which are mainly known as the Amygdala-Orbitofrontal Cortex system. The main chapter of this thesis, Chapter IV, shows the utilization of the Brain Emotional Learning (BEL) model in different applications of control and signal fusion systems. The main effort is focused on applying the model to control systems where the model acts as the controller block. Furthermore, the application of the model in signal fusion is also considered where simulation results support the applicability of the model. Finally, we studied different analytical aspects of the model including the behavior of the system during the adaptation phase and the stability of the system. For the first issue, we simplify the model, e.g. remove the nonlinearities, to develop mathematical formulations for behavior of the system. To study the stability of the system, we use the cell-to-cell mapping algorithm which reveals the stability conditions of the system in different representations. This thesis finishes with some concluding remarks and some topics for future research on this field