Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The approach of sliding mode control (SMC) was proposed as it documented an effective tool for designing robust controllers for complex high-order linear and nonlinear dynamic systems operating under uncertain conditions. In this work Intelligent particle swarm optimization (PSO) and social spider optimization (SSO) were used for obtaining the best values for the parameters of sliding mode control (SMC) to achieve consistency, stability and robustness. Additional design of integral sliding mode control (ISMC) was implemented to the dynamic system to achieve the high control theory of sliding mode controller. For designing particle swarm optimizer (PSO) and social spider optimization (SSO) processes, mean square error performances index was considered. The effectiveness of the proposed system was tested with six degrees of freedom robot manipulator by using (PUMA) robot. The iteration of SSO and PSO algorithms with mean square error and objective function were obtained, with best fitness for (SSO) =4.4876 -6 and (PSO)=3.4948 -4

Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone

Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other

Control System Design for a Centrifuge Motion Simulator Based on a Dynamic Model

This paper presents a dynamic model-based design of a control system and an approach toward a drive selection of a centrifuge motion simulator (CMS). The objective of the presented method is to achieve the desired performance while taking into account the complexity of the control system and the overall device cost An estimation of a dynamic interaction of the interconnected CMS links motions is performed using the suitable inverse dynamics simulation. An algorithm based on the approximate inverse dynamics model is used within the drive selection method. The model of the actuator's mechanical subsystem includes the effective inertia (inertia reflected on the rotor shaft) calculated from the inverse dynamics model. A centralized control strategy based on a computed torque method is considered and compared to traditional decentralized motion controllers To obtain an accurate comparison of the suggested control methods through a realistic simulation, structural natural frequencies of the manipulator links are considered, and the actuator capabilities are taken into account The control system design and simulation methods and the drive selection strategies, presented here for the CMS, are applicable within the general robot manipulator's domain

A Comparative Study Between Convolution and Optimal Backstepping Controller for Single Arm Pneumatic Artificial Muscles

This study was based on the dynamic modeling and parameter characterization of the one-link robot arm driven by pneumatic artificial muscles. This work discusses an up-to-date control design based on the notion of a conventional and optimal backstepping controller for regulating a one-link robot arm with conflicting biceps and triceps positions supplied by pneumatic artificial muscles. The main problems found in systems that utilize pneumatic artificial muscle as actuators are primarily the large uncertainties, non-linearities, and time-varying features that severely impede movement performance in tracking control. In consideration of the uncertainty, high nonlinearity, and external disturbances that can exist during the motion. Lyapunov-based backstepping control technique was utilized to assure the stability of the system with improved dynamic performance. The bat algorithm optimization method is utilized in order to modify the variables used in the design of the controller to enhance the efficiency of the suggested controller. According to the conclusions, a quantitative comparison of the response in the PAM actuated the arm model in the current study and earlier investigations with the Backstepping controlled system revealed fair agreement with a variation of 37.5% from the optimal classical synergetic controller. In addition, computer simulations were utilized in order to compare the effectiveness of the proposed conventional controls and the optimal background. It has been proven that an optimal controller can control the uncertainties and maintain the controlled system’s stability

Dynamics and control of robotic systems for on-orbit objects manipulation

Multi-body systems (MSs) are assemblies composed of multiple bodies (either rigid or structurally flexible) connected among each other by means of mechanical joints. In many engineering fields (such as aerospace, aeronautics, robotics, machinery, military weapons and bio-mechanics) a large number of systems (e.g. space robots, aircraft, terrestrial vehicles, industrial machinery, launching systems) can be included in this category. The dynamic characteristics and performance of such complex systems need to be accurately and rapidly analyzed and predicted. Taking this engineering background into consideration, a new branch of study, named as Multi-body Systems Dynamics (MSD), emerged in the 1960s and has become an important research and development area in modern mechanics; it mainly addresses the theoretical modeling, numerical analysis, design optimization and control for complex MSs. The research on dynamics modeling and numerical solving techniques for rigid multi-body systems has relatively matured and perfected through the developments over the past half century. However, for many engineering problems, the rigid multi-body system model cannot meet the requirements in terms of precision. It is then necessary to consider the coupling between the large rigid motions of the MS components and their elastic displacements; thus the study of the dynamics of flexible MSs has gained increasing relevance. The flexible MSD involves many theories and methods, such as continuum mechanics, computational mechanics and nonlinear dynamics, thus implying a higher requirement on the theoretical basis. Robotic on-orbit operations for servicing, repairing or de-orbiting existing satellites are among space mission concepts expected to have a relevant role in a close future. In particular, many studies have been focused on removing significant debris objects from their orbit. While mission designs involving tethers, nets, harpoons or glues are among options studied and analyzed by the scientific and industrial community, the debris removal by means of robotic manipulators seems to be the solution with the longest space experience. In fact, robotic manipulators are now a well-established technology in space applications as they are routinely used for handling and assembling large space modules and for reducing human extravehicular activities on the International Space Station. The operations are generally performed in a tele-operated approach, where the slow motion of the robotic manipulator is controlled by specialized operators on board of the space station or at the ground control center. Grasped objects are usually cooperative, meaning they are capable to re-orient themselves or have appropriate mechanisms for engagement with the end-effectors of the manipulator (i.e. its terminal parts). On the other hand, debris removal missions would target objects which are often non-controlled and lacking specific hooking points. Moreover, there would be a distinctive advantage in terms of cost and reliability to conduct this type of mission profile in a fully autonomous manner, as issues like obstacle avoidance could be more easily managed locally than from a far away control center. Space Manipulator Systems (SMSs) are satellites made of a base platform equipped with one or more robotic arms. A SMS is a floating system because its base is not fixed to the ground like in terrestrial manipulators; therefore, the motion of the robotic arms affects the attitude and position of the base platform and vice versa. This reciprocal influence is denoted as "dynamic coupling" and makes the dynamics modeling and motion planning of a space robot much more complicated than those of fixed-base manipulators. Indeed, SMSs are complex systems whose dynamics modeling requires appropriate theoretical and mathematical tools. The growing importance SMSs are acquiring is due to their operational ductility as they are able to perform complicated tasks such as repairing, refueling, re-orbiting spacecraft, assembling articulated space structures and cleaning up the increasing amount of space debris. SMSs have also been employed in several rendezvous and docking missions. They have also been the object of many studies which verified the possibility to extend the operational life of commercial and scientific satellites by using an automated servicing spacecraft dedicated to repair, refuel and/or manage their failures (e.g. DARPA's Orbital Express and JAXA's ETS VII). Furthermore, Active Debris Removal (ADR) via robotic systems is one of the main concerns governments and space agencies have been facing in the last years. As a result, the grasping and post-grasping operations on non-cooperative objects are still open research areas facing many technical challenges: the target object identification by means of passive or active optical techniques, the estimation of its kinematic state, the design of dexterous robotic manipulators and end-effectors, the multi-body dynamics analysis, the selection of approaching and grasping maneuvers and the post-grasping mission planning are the main open research challenges in this field. The missions involving the use of SMSs are usually characterized by the following typical phases: 1. Orbital approach; 2. Rendez-vous; 3. Robotic arm(s) deployment; 4. Pre-grasping; 5. Grasping and post-grasping operations. This thesis project will focus on the last three. The manuscript is structured as follows: Chapter 1 presents the derivation of a multi-body system dynamics equations further developing them to reach their Kane's formulation; Chapter 2 investigates two different approaches (Particle Swarm Optimization and Machine Learning) dealing with a space manipulator deployment maneuver; Chapter 3 addresses the design of a combined Impedance+PD controller capable of accomplishing the pre-grasping phase goals and Chapter 4 is dedicated to the dynamic modeling of the closed-loop kinematic chain formed by the manipulator and the grasped target object and to the synthesis of a Jacobian Transpose+PD controller for a post-grasping docking maneuver. Finally, the concluding remarks summarize the overall thesis contribution

Quadrotor team modeling and control for DLO transportation

94 p.Esta Tesis realiza una propuesta de un modelado dinámico para el transporte de sólidos lineales deformables (SLD) mediante un equipo de cuadricópteros. En este modelo intervienen tres factores: - Modelado dinámico del sólido lineal a transportar. - Modelo dinámico del cuadricóptero para que tenga en cuenta la dinámica pasiva y los efectos del SLD. - Estrategia de control para un transporte e ciente y robusto. Diferenciamos dos tareas principales: (a) lograr una con guración cuasiestacionaria de una distribución de carga equivalente a transportar entre todos los robots. (b) Ejecutar el transporte en un plano horizontal de todo el sistema. El transporte se realiza mediante una con guración de seguir al líder en columna, pero los cuadricópteros individualmente tienen que ser su cientemente robustos para afrontar todas las no-linealidades provocadas por la dinámica del SLD y perturbaciones externas, como el viento. Los controladores del cuadricóptero se han diseñado para asegurar la estabilidad del sistema y una rápida convergencia del sistema. Se han comparado y testeado estrategias de control en tiempo real y no-real para comprobar la bondad y capacidad de ajuste a las condiciones dinámicas cambiantes del sistema. También se ha estudiado la escalabilidad del sistema

Evolutionary algorithms for active vibration control of flexible manipulator

Flexible manipulator systems offer numerous advantages over their rigid counterparts including light weight, faster system response, among others. However, unwanted vibration will occur when flexible manipulator is subjected to disturbances. If the advantages of flexible manipulator are not to be sacrificed, an accurate model and efficient control system must be developed. This thesis presents the development of a Proportional-Integral-Derivative (PID) controller tuning method using evolutionary algorithms (EA) for a single-link flexible manipulator system. Initially, a single link flexible manipulator rig, constrained to move in horizontal direction, was designed and fabricated. The input and output experimental data of the hub angle and endpoint acceleration of the flexible manipulator were acquired. The dynamics of the system was later modeled using a system identification (SI) method utilizing EA with linear auto regressive with exogenous (ARX) model structure. Two novel EAs, Genetic Algorithm with Parameter Exchanger (GAPE) and Particle Swarm Optimization with Explorer (PSOE) have been developed in this study by modifying the original Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithms. These novel algorithms were introduced for the identification of the flexible manipulator system. Their effectiveness was then evaluated in comparison to the original GA and PSO. Results indicated that the identification of the flexible manipulator system using PSOE is better compared to other methods. Next, PID controllers were tuned using EA for the input tracking and the endpoint vibration suppression of the flexible manipulator structure. For rigid motion control of hub angle, an auto-tuned PID controller was implemented. While for vibration suppression of the endpoint, several PID controllers were tuned using GA, GAPE, PSO and PSOE. The results have shown that the conventional auto-tuned PID was effective enough for the input tracking of the rigid motion. However, for end-point vibration suppression, the result showed the superiority of PID-PSOE in comparison to PID-GA, PID-GAPE and PID-PSO. The performance of the best simulated controller was validated experimentally later. Through experimental validation, it was found that the PID-PSOE was capable to suppress the vibration of the single-link flexible manipulator with highest attenuation of 31.3 dB at the first mode of the vibration. The outcomes of this research revealed the effectiveness of the PID controller tuned using PSOE for the endpoint vibration suppression of the flexible manipulator amongst other evolutionary methods

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