Experimental evaluation of some classical and adaptive iterative learning control schemes on a 5DOF robot manipulator

In many process industries (e.g., VLSI production lines, Automotive industries, IC welding process, inspections, manipulations), robot manipulators are used to perform the same tasks repeatedly over a finite time interval. The ultimate goal of robotic research is to design intelligent and autonomous robot control systems to perform repetitive tasks that are dull, hazardous, or require skill beyond the capability of humans. The nonlinear nature of the robot dynamics has made this problem a challenging one in robotics research. This highly demanding control problem of driving an industrial robot to follow a desired trajectory perfectly under constrained or unconstrained environment has led to the application of sophisticated control techniques. From the classical or modern control view point, it is a very difficult task to design an intelligent robot control system that can achieve perfect tracking over a finite time interval due to the effect of highly coupled robot dynamics and the presence of the unmodeled dynamics such as friction and backlash that are usually exhibited in the robot system during actual operation

Robot visual servoing with iterative learning control.

YesThis paper presents an iterative learning scheme for vision guided robot trajectory tracking. At first, a stability criterion for designing iterative learning controller is proposed. It can be used for a system with initial resetting error. By using the criterion, one can convert the design problem into finding a positive definite discrete matrix kernel and a more general form of learning control can be obtained. Then, a three-dimensional (3-D) trajectory tracking system with a single static camera to realize robot movement imitation is presented based on this criterion

Fractional-Order Iterative Learning Control for Robotic Arm-(PDD alpha)-D-2 Type

In this paper, a new open-loop (PDDa)-D-2 type a fractional order iterative learning control (ILC) is studied for joint space trajectory tracking control of a linearized uncertain robotic arm. The robust convergent analysis of the tracking errors has been done in time domain where it is theoretically proven that the boundednesses of the tracking error are guaranteed in the presence of model uncertainty. The convergence of the proposed open-loop ILC law is proven mathematically using Gronwall integral inequality for a linearized robotic system and sufficient conditions for convergence and robustness are obtained

Fractional-Order Iterative Learning Control for Robotic Arm-(PDD alpha)-D-2 Type

In this paper, a new open-loop (PDDa)-D-2 type a fractional order iterative learning control (ILC) is studied for joint space trajectory tracking control of a linearized uncertain robotic arm. The robust convergent analysis of the tracking errors has been done in time domain where it is theoretically proven that the boundednesses of the tracking error are guaranteed in the presence of model uncertainty. The convergence of the proposed open-loop ILC law is proven mathematically using Gronwall integral inequality for a linearized robotic system and sufficient conditions for convergence and robustness are obtained

Iterative learning control: algorithm development and experimental benchmarking

This thesis concerns the general area of experimental benchmarking of Iterative Learning Control (ILC) algorithms using two experimental facilities. ILC is an approach which is suitable for applications where the same task is executed repeatedly over the necessarily finite time duration, known as the trial length. The process is reset prior to the commencement of each execution. The basic idea of ILC is to use information from previously executed trials to update the control input to be applied during the next one. The first experimental facility is a non-minimum phase electro-mechanical system and the other is a gantry robot whose basic task is to pick and place objects on a moving conveyor under synchronization and in a fixed finite time duration that replicates many tasks encountered in the process industries. Novel contributions are made in both the development of new algorithms and, especially, in the analysis of experimental results both of a single algorithm alone and also in the comparison of the relative performance of different algorithms. In the case of non-minimum phase systems, a new algorithm, named Reference Shift ILC (RSILC) is developed that is of a two loop structure. One learning loop addresses the system lag and another tackles the possibility of a large initial plant input commonly encountered when using basic iterative learning control algorithms. After basic algorithm development and simulation studies, experimental results are given to conclude that performance improvement over previously reported algorithms is reasonable. The gantry robot has been previously used to experimentally benchmark a range of simple structure ILC algorithms, such as those based on the ILC versions of the classical proportional plus derivative error actuated controllers, and some state-space based optimal ILC algorithms. Here these results are extended by the first ever detailed experimental study of the performance of stochastic ILC algorithms together with some modifications necessary to their configuration in order to increase performance. The majority of the currently reported ILC algorithms mainly focus on reducing the trial-to-trial error but it is known that this may come at the cost of poor or unacceptable performance along the trial dynamics. Control theory for discrete linear repetitive processes is used to design ILC control laws that enable the control of both trial-to-trial error convergence and along the trial dynamics. These algorithms can be computed using Linear Matrix Inequalities (LMIs) and again the results of experimental implementation on the gantry robot are given. These results are the first ever in this key area and represent a benchmark against which alternatives can be compared. In the concluding chapter, a critical overview of the results presented is given together with areas for both short and medium term further researc

Iterative learning control for robot manipulators

When a system is performing the same task repeatedly it is, from an engineering perspective, advantageous to use the knowledge from the previous iterations of the same task in order to reduce the error on successive trials. In control systems, the aim is to force the system output to follow a desired trajectory as closely as possible. Specific norms and measures of optimality are used to determine how close the output is to the desired trajectory. Although control theory provides many different possible solutions for such problem, it is not always possible to achieve a desired set of performance requirements. This may be due to the presence of unmodeled dynamics or parametric uncertainties exhibited during the system operation, or due to the lack of suitable design techniques for particular class of systems. Iterative learning control (ILC) is a relatively new addition to these techniques that, for a particular class of problems, can be used to overcome some of the difficulties associated with performance design of control systems

Design and development of the ‘POD Adventures’ smartphone game: a blended problem-solving intervention for adolescent mental health in India

Introduction: Digital technology platforms offer unparalleled opportunities to reach vulnerable adolescents at scale and overcome many barriers that exist around conventional service provision. This paper describes the design and development of POD Adventures, a blended problem-solving game-based intervention for adolescents with or at risk of anxiety, depression and conduct difficulties in India. This intervention was developed as part of the PRemIum for ADolEscents (PRIDE) research programme, which aims to establish a suite of transdiagnostic psychological interventions organised around a stepped care system in Indian secondary schools. Methods and materials: Intervention development followed a person-centered approach consisting of four iterative activities: (i) review of recent context-specific evidence on mental health needs and preferences for the target population of school-going Indian adolescents, including a multiple stakeholder analysis of school counselling priorities and pilot studies of a brief problem-solving intervention; (ii) new focus group discussions with N=46 student participants and N=8 service providers; (iii) co-design workshops with N=22 student participants and N=8 service providers; and (iv) user-testing with N=50 student participants. Participants were aged 12-17 years and recruited from local schools in New Delhi and Goa, including a subgroup with self-identified mental health needs (N=6). Results: Formative data from existing primary sources, new focus groups and co-design workshops supported a blended format for delivering a brief problem-solving intervention, with counsellors supporting use of a game-based app on ‘offline’ smartphones. User-testing with prototypes identified a need for simplification of language, use of concrete examples of concepts and practice elements to enhance engagement. There were also indications that participants most valued relatability and interactivity within real-world stories with judicious support from an in-app guide. The final prototype comprised a set of interactive and gamified vignettes and a structured set of problem-solving questions to consolidate and generalise learning while encouraging real-world application. Discussion: Findings shaped the design of POD Adventures and its delivery as an open-access blended intervention for secondary school students with a felt need for psychological support, consistent with an early intervention paradigm. A randomised controlled trial is planned to evaluate processes and impacts of POD Adventures when delivered for help-seeking students in low-resource school settings

Iterative learning control of integer and noninteger order: An overview

Ovaj rad daje pregledni prikaz nedavno prezentiranih i objavljenih rezultata autora koji se odnose na primenu iterativnog upravljanja putem učenja (ILC) i to celog reda kao i necelog reda. ILC predstavlja jedno od važnih oblasti u teoriji upravljanja i ono je moćan koncept upravljanja koji na iterativan način poboljšava ponašanje procesa koji su po prirodi ponovljivi. ILC je pogodno za upravljanje šire klase mehatroničkih sistema i posebno su pogodni za upravljanje kretanja robotskih sistema koji imaju važnu ulogu u biomehatroničkim, tehničkim sistemima koji uključuju primenu i vojnoj industriju itd. U prvom delu rada predstavljeni su rezultati koji se odnose na primenu višeg celobrojnog reda PD tipa sa pratećom numeričkom simulacijom. Takođe, još jedna druga ILC šema celobrojnog reda je predložena za dati robotski sistem sa tri stepena slobode u rešavanju zadatka praćenja što je i verifikovano kroz simulacioni primer. U drugom delu, predstavljeni su rezultati koji se odnose na primenu ILC frakcionog reda gde je prvo PDα tip predložen za linearni sistem frakcionog reda. Pokazano je da se pod odredjenim dovoljnim uslovima koji uključuju operatore učenja konvergencija datog sistema može biti garantovana. Takodje, PIβDα tip ILC upravljanja je predložen za linearni sistem frakcionog reda sa kašnjenjem. Konačno, dovoljni uslovi za konvergenciju u vremenskom domenu predloženog ILC upravljanja su dati odgovarajućom teoremom sa pratećim dokazom.This paper provides an overview of the recently presented and published results relating to the use of iterative learning control (ILC) based on and integer and fractional order. ILC is one of the recent topics in control theories and it is a powerful control concept that iteratively improves the behavior of processes that are repetitive in nature. ILC is suitable for controlling a wider class of mechatronic systems - it is especially suitable for motion control of robotic systems that attract and hold an important position in biomechatronical, technical systems involving the application, military industry, etc. The first part of the paper presents the results relating to the application of higher integer order PD type ILC with numerical simulation. Also, another integer order ILC scheme is proposed for a given robotic system with three degrees of freedom for task-space trajectory tracking where the effectiveness of the suggested control is demonstrated through a simulation procedure. In the second part, the results related to the application of the fractional order of ILC are presented where PDα type of ILC is proposed firstly, for a fractional order linear time invariant system. It is shown that under some sufficient conditions which include the learning operators, convergence of the learning system can be guaranteed. Also, PIβDα type of ILC is suggested for a fractional order linear time delay system. Finally, sufficient conditions for the convergence in the time domain of the proposed ILC were given by the corresponding theorem together with its proof

Iterative learning control of integer and noninteger order: An overview

Ovaj rad daje pregledni prikaz nedavno prezentiranih i objavljenih rezultata autora koji se odnose na primenu iterativnog upravljanja putem učenja (ILC) i to celog reda kao i necelog reda. ILC predstavlja jedno od važnih oblasti u teoriji upravljanja i ono je moćan koncept upravljanja koji na iterativan način poboljšava ponašanje procesa koji su po prirodi ponovljivi. ILC je pogodno za upravljanje šire klase mehatroničkih sistema i posebno su pogodni za upravljanje kretanja robotskih sistema koji imaju važnu ulogu u biomehatroničkim, tehničkim sistemima koji uključuju primenu i vojnoj industriju itd. U prvom delu rada predstavljeni su rezultati koji se odnose na primenu višeg celobrojnog reda PD tipa sa pratećom numeričkom simulacijom. Takođe, još jedna druga ILC šema celobrojnog reda je predložena za dati robotski sistem sa tri stepena slobode u rešavanju zadatka praćenja što je i verifikovano kroz simulacioni primer. U drugom delu, predstavljeni su rezultati koji se odnose na primenu ILC frakcionog reda gde je prvo PDα tip predložen za linearni sistem frakcionog reda. Pokazano je da se pod odredjenim dovoljnim uslovima koji uključuju operatore učenja konvergencija datog sistema može biti garantovana. Takodje, PIβDα tip ILC upravljanja je predložen za linearni sistem frakcionog reda sa kašnjenjem. Konačno, dovoljni uslovi za konvergenciju u vremenskom domenu predloženog ILC upravljanja su dati odgovarajućom teoremom sa pratećim dokazom.This paper provides an overview of the recently presented and published results relating to the use of iterative learning control (ILC) based on and integer and fractional order. ILC is one of the recent topics in control theories and it is a powerful control concept that iteratively improves the behavior of processes that are repetitive in nature. ILC is suitable for controlling a wider class of mechatronic systems - it is especially suitable for motion control of robotic systems that attract and hold an important position in biomechatronical, technical systems involving the application, military industry, etc. The first part of the paper presents the results relating to the application of higher integer order PD type ILC with numerical simulation. Also, another integer order ILC scheme is proposed for a given robotic system with three degrees of freedom for task-space trajectory tracking where the effectiveness of the suggested control is demonstrated through a simulation procedure. In the second part, the results related to the application of the fractional order of ILC are presented where PDα type of ILC is proposed firstly, for a fractional order linear time invariant system. It is shown that under some sufficient conditions which include the learning operators, convergence of the learning system can be guaranteed. Also, PIβDα type of ILC is suggested for a fractional order linear time delay system. Finally, sufficient conditions for the convergence in the time domain of the proposed ILC were given by the corresponding theorem together with its proof