A survey on uninhabited underwater vehicles (UUV)

ASME Early Career Technical Conference, ASME ECTC, October 2-3, 2009, Tuscaloosa, Alabama, USAThis work presents the initiation of our underwater robotics research which will be focused on underwater vehicle-manipulator systems. Our aim is to build an underwater vehicle with a robotic manipulator which has a robust system and also can compensate itself under the influence of the hydrodynamic effects. In this paper, overview of the existing underwater vehicle systems, thruster designs, their dynamic models and control architectures are given. The purpose and results of the existing methods in underwater robotics are investigated

A Robot Arithmetic Processor Concept for Cartesian Closed-Loop Control with Prescribed Dynamics

The hardware implementation of a cartesian closed-loop control scheme will be presented which allows to define the dynamic behaviour of each degree of freedom of the cartesian coordinate system in a prescribed sense. The control system at joint level is designed by multivariate design methods with an additional feedforward component using the concept of inverse dynamics. To achieve high accuracy for cartesian motions quasi-continuous control mode with cartesian sampling periods of not greater than 5 ms is aimed at. A special purpose processor for calculation of kinematic and dynamic terns is designed and integrated into a multiprocessor architecture. This implementation concept with Robot Arithmetic Processor provides the necessary computational power and allows real-time cartesian closed-loop control which is also essential for cartesian sensory control tasks

A Comparative Study of LQR and Integral Sliding Mode Control Strategies for Position Tracking Control of Robotic Manipulators

This paper provides a systematic comparative study of position tracking control of nonlinear robotic manipulators. The main contribution of this study is a comprehensive numerical simulation assessing position tracking performances and energy consumption of integral sliding mode control (ISMC), a linear-quadratic regulator with integral action (LQRT), and optimal integral sliding mode control (OISMC) under three conditions; namely, Case I) without the coupling effect, Case II) with the coupling effect on Link 1 only, and Case III) with the coupling effect on Link 2 only. The viability of the concept is evaluated based on three performance criteria, i.e., the step-response characteristics, position tracking error, and energy consumption of the aforementioned controllers. Based upon the simulation study, it has been found that OISMC offers performances almost similar to ISMC with more than 90% improvement of tracking performance under several cases compared to LQRT; however, energy consumption is successfully reduced by 3.6% in comparison to ISMC. Energy consumption of OISMC can be further reduced by applying optimization algorithms in tuning the weighting matrices. This paper can be considered significant as a robotic system with high tracking accuracy and low energy consumption is highly demanded to be implemented in smart factories, especially for autonomous systems

Biologically inspired control and modeling of (bio)robotic systems and some applications of fractional calculus in mechanics

U ovom radu, prezentovane su primene biološki inspirisanog modeliranja i upravljanja (bio)mehaničkim (ne)redundantnim mehanizmima, kao i novodobijeni rezultati autora u oblasti primenjene mehanike koji su zasnovani na primeni računa necelobrojnog reda. Prvo, predloženo je korišćenje biološkog analogona-sinergije zahvaljujući postojanju nepromenljivih odlika u izvršavanju funkcionalnih pokreta. Drugo, model (bio)mehaničkog sistema može se dobiti primenom drugog biološkog koncepta poznatim pod nazivom distribuirano pozicioniranje (DP), koji je zasnovan na inercijalnim svojstva i pokretanju zglobova razmatranog mehaničkog sistema. Takođe,predlaže se korišćenje drugih bioloških principa kao što su: princip minimalne interakcije, koji ima glavnu ulogu u hijerarhijskoj strukturi upravljanja i princip samopodešavanja (uvodi lokalne pozitivnu/negativnu povratnu spregu u upravljačkoj petlji i to sa velikim pojačanjem), koji omogućava efikasno ostvarivanje upravljanja na bazi iterativnog prirodnog učenja. Takođe, novi, nedavno publikovani rezultati autora su takođe predstavljeni u oblasti stabilnosti, elektro-viskoelastičnosti i teoriji upravljanja a koji su zasnovani na korišćenju računa necelobrojnog reda.In this paper, the applications of biologically inspired modeling and control of (bio)mechanical (non)redundant mechanisms are presented, as well as newly obtained results of author in mechanics which are based on using fractional calculus. First, it is proposed to use biological analog-synergy due to existence of invariant features in the execution of functional motion. Second, the model of (bio)mechanical system may be obtained using another biological concept called distributed positioning (DP), which is based on the inertial properties and actuation of joints of considered mechanical system. In addition, it is proposed to use other biological principles such as: principle of minimum interaction, which takes a main role in hierarchical structure of control and self-adjusting principle (introduce local positive/negative feedback on control with great amplifying), which allows efficiently realization of control based on iterative natural learning. Also, new, recently obtained results of the author in the fields of stability, electroviscoelasticity, and control theory are presented which are based on using fractional calculus (FC)

Modeling, Analysis, and Control of a Mobile Robot for \u3ci\u3eIn Vivo\u3c/i\u3e Fluoroscopy of Human Joints during Natural Movements

In this dissertation, the modeling, analysis and control of a multi-degree of freedom (mdof) robotic fluoroscope was investigated. A prototype robotic fluoroscope exists, and consists of a 3 dof mobile platform with two 2 dof Cartesian manipulators mounted symmetrically on opposite sides of the platform. One Cartesian manipulator positions the x-ray generator and the other Cartesian manipulator positions the x-ray imaging device. The robotic fluoroscope is used to x-ray skeletal joints of interest of human subjects performing natural movement activities. In order to collect the data, the Cartesian manipulators must keep the x-ray generation and imaging devices accurately aligned while dynamically tracking the desired skeletal joint of interest. In addition to the joint tracking, this also requires the robotic platform to move along with the subject, allowing the manipulators to operate within their ranges of motion. A comprehensive dynamic model of the robotic fluoroscope prototype was created, incorporating the dynamic coupling of the system. Empirical data collected from an RGB-D camera were used to create a human kinematic model that can be used to simulate the joint of interest target dynamics. This model was incorporated into a computer simulation that was validated by comparing the simulation results with actual prototype experiments using the same human kinematic model inputs. The computer simulation was used in a comprehensive dynamic analysis of the prototype and in the development and evaluation of sensing, control, and signal processing approaches that optimize the subject and joint tracking performance characteristics. The modeling and simulation results were used to develop real-time control strategies, including decoupling techniques that reduce tracking error on the prototype. For a normal walking activity, the joint tracking error was less than 20 mm, and the subject tracking error was less than 140 mm

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

Iterative learning control of multivariable plants

In recent years, many researchers have proposed different iterative learningcontrollers, which unfortunately mostly require that the plants under control beregular. Therefore, in order to remove this limitation, various analogue and digitaliterative learning controllers are proposed in this thesis.Indeed, it is shown that analogue iterative learning controllers can be designed forplants with any order of irregularity using initial state shifting or initial impulsiveaction. However, such analogue controllers have to be digitalised for purpose ofimplementation. In addition, in the synthesis of their control laws, such controllersrequire some knowledge of the plants' Markov parameters. Ilerefore, new digitaliterative learning controllers are proposed. Such digital controllers circumvent theneed for detailed mathematical models of the plants in any form. Indeed, theproposed digital iterative learning controllers rely on input/output data in thesynthesis of their control laws. It is shown that digital iterative learning controllerscan be readily designed for multivariable plants of any order or irregularity using onlysuch input/output data in the form of step-responsem atrices.The learning rates achievable in both the analogue and digital iterative learningcontrol of linear multivariable plants are investigated. It is shown that the irregularityand stability characteristics of the plants under control impose severe constrains on theachievable learning rates. Indeed, it is shown that the learning parameter in the caseof digital iterative learning controllers increases as the order of plant irregularityincreases. This increase in the learning parameter affects the learning performanceand the speed of convergence adversely. This discovery led to the introduction ofcompensators in the design of digital iterative learning controllers for irregular plants which help to improve the learning performance and convergence by reducing theeffective learning parameter. Since such digital iterative learning controllers use stepresponsematrices in the synthesis of their control laws and since the step-responsecharacteristics can be identified in real time, it is shown in this thesis that iterativelearning controllers can readily be rendered adaptive in case plant dynamics areinitially unknown or time-varying.In order to demonstrate the applicability of these results to the control of roboticmanipulators, both analogue and digital iterative learning controllers are designed fora two-link manipulator in both joint and task spaces. Finally, digital iterativelearning controllers are designed and practically implemented in the real-timepositional control of a dc servo actuator