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

A Systematic Literature Review of Path-Planning Strategies for Robot Navigation in Unknown Environment

The Many industries, including ports, space, surveillance, military, medicine and agriculture have benefited greatly from mobile robot technology.  An autonomous mobile robot navigates in situations that are both static and dynamic. As a result, robotics experts have proposed a range of strategies. Perception, localization, path planning, and motion control are all required for mobile robot navigation. However, Path planning is a critical component of a quick and secure navigation. Over the previous few decades, many path-planning algorithms have been developed. Despite the fact that the majority of mobile robot applications take place in static environments, there is a scarcity of algorithms capable of guiding robots in dynamic contexts. This review compares qualitatively mobile robot path-planning systems capable of navigating robots in static and dynamic situations. Artificial potential fields, fuzzy logic, genetic algorithms, neural networks, particle swarm optimization, artificial bee colonies, bacterial foraging optimization, and ant-colony are all discussed in the paper. Each method's application domain, navigation technique and validation context are discussed and commonly utilized cutting-edge methods are analyzed. This research will help researchers choose appropriate path-planning approaches for various applications including robotic cranes at the sea ports as well as discover gaps for optimization

Robots in Agriculture: State of Art and Practical Experiences

The presence of robots in agriculture has grown significantly in recent years, overcoming some of the challenges and complications of this field. This chapter aims to collect a complete and recent state of the art about the application of robots in agriculture. The work addresses this topic from two perspectives. On the one hand, it involves the disciplines that lead the automation of agriculture, such as precision agriculture and greenhouse farming, and collects the proposals for automatizing tasks like planting and harvesting, environmental monitoring and crop inspection and treatment. On the other hand, it compiles and analyses the robots that are proposed to accomplish these tasks: e.g. manipulators, ground vehicles and aerial robots. Additionally, the chapter reports with more detail some practical experiences about the application of robot teams to crop inspection and treatment in outdoor agriculture, as well as to environmental monitoring in greenhouse farming

Evolutionary algorithm based controller for double link flexible robotic manipulator

The paper investigates the development of intelligent hybrid collocated and non-collocated PID controller for hub motion and end point vibration suppression of double-link flexible robotic manipulator. The system was modelled using multi-layer perceptron neural network structure based on Nonlinear Autoregressive Exogenous (NARX) model. The hybrid controllers are incorporated with optimization algorithm that is ABC and PSO to find out the parameters of the PID controllers. Numerical simulation was carried out in MATLAB/Simulink to evaluate the system in term of tracking capability and vibration suppression for both links. The results show that PSO revealed the superiority over ABC in controlling the system

Implementation of PID Based Controller Tuned by Evolutionary Algorithm for Double Link Flexible Robotic Manipulator

© 2018 IEEE. The paper investigates the development of intelligent hybrid collocated and non-collocated PID controller for hub motion and end point vibration suppression of double-link flexible robotic manipulator. The system was modeled using multi-layer perceptron neural network structure based on Nonlinear Autoregressive Exogenous (NARX) model. The hybrid controllers are incorporated with optimization algorithm that is ABC and PSO to find out the parameters of the PID controllers. Numerical simulation was carried out in MATLAB/Simulink to evaluate the system in term of tracking capability and vibration suppression for both links. Performance of the controllers are compared with the hybrid PID-PID Ziegler Nichols (ZN) controller in term of input tracking and vibration suppression. The results show that PSO revealed the superiority over ABC in controlling the system. The system managed to reach desired angle for both hub at lower overshoot using proposed method. Meanwhile, the vibration reduction shows great improvement for both link 1 and 2. This signifies that, the PSO algorithm is very effective in optimizing the PID parameters

PID Tuning Method Using Chaotic Safe Experimentation Dynamics Algorithm for Elastic Joint Manipulator

This paper proposed the chaotic safe experimentation dynamics algorithm (CSEDA) to regulate angular tracking and vibration of the self-tuning PID controller for elastic joint manipulators. CSEDA was a modified version of the safe experimentation dynamics algorithm (SEDA) that used a chaos function in the updated equation. The chaos function increased the exploration capability, thus improving the convergence accuracy. In this study, two self-tuning PID controllers were used to regulate the rotating angle tracking and vibration for elastic joint manipulators in this control challenge. The suggested self-tuning PID controller's performance was evaluated in angular motion trajectory tracking, vibration suppression, and the pre-determined control fitness function. A self-tuned PID controller based on CSEDA could achieve superior control accuracy than a traditional SEDA and its variants