Direct adaptive control of robotic systems

This paper presents a new approach to adaptive motion control of an important class of robotic systems. The control schemes developed using this approach are very simple and computationally efficient since they do not require knowledge of either the mathematical model or the parameter values of the robotic system dynamics. It is shown that the control strategies are globally stable in the presence of bounded disturbances, and that the size of the tracking errors can be made arbitrarily small. The proposed controllers are very general and are implementable with a wide variety of robotic systems, including both open- and closed-kinematic-chain manipulators. Computer simulation results are given for a seven degree-of-freedom (DOF) Robotics Research Corporation Model K-1607 arm. These results demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed schemes.     

An observer-based adaptive neural network tracking control of robotic systems

Robotic systems have inherently nonlinear phenomena as joints undergo sliding and/or rotating. This in turn requires that the system running states be predicted correctly. This paper makes a full analysis of the robot states by applying observer-based adaptive wavelet neural network (OBAWNN) tracking control scheme to tackle these phenomena such as system uncertainties, multiple time-delayed state uncertainties, and external disturbances such that the closed loop system signals must obey uniform ultimate boundedness and achieve H^∞ tracking performance. The recurrent adaptive wavelet neural network model is used to approximate the dynamics of the robotic system, while an observer-based adaptive control scheme is to stabilize the system. The advantage of employing adaptive wavelet neural dynamics is that we can utilize the neuron information by activation functions to on-line tune the hidden-to-output weights, and the adaptation parameters to estimate the robot parameters and the bounds of the gains of delay states directly using linear analytical results. It is shown that the stability of the closed-loop system is guaranteed by some sufficient conditions derived from Lyapunov criterion and Riccati-inequality. Finally, a numerical example of a three-links rolling cart is given to illustrate the effectiveness of the proposed control scheme.     

Decentralized adaptive controller design of large-scale uncertain robotic systems

In this paper, we develop a decentralized neural network control design for robotic systems. Using this design, it is not necessary to derive the robotic dynamical system (robotic model) for the control of each of the robotic components, as in traditional robot control. The advantage of the proposed neural network controller is that, under a mild assumption, unknown nonlinear dynamics such as inertia matrix and Coriolis/centripetal matrix and friction, as well as interconnections with arbitrary nonlinear bounds can be accommodated with on-line learning.     

Automated modeling of modular robotic configurations

This research presents an automated method to build kinematic and dynamic models for assembling modular components of modular robotic systems. By comparison with other approaches, the proposed method is applicable to any robotic configuration with serial, parallel, or hybrid structures. In addition, it is object oriented so that each modular component is an element with a submodel and the overall model can be assembled from submodels subject to the connection constraints.     

Hardware and software for intelligent robotic systems

A hardware and software methodology for the design of the three interactive levels of intelligent robotic systems is proposed. The organization level is modeled as an expert system, the coordination level as a loosely coupled parallel processing system and the execution level as a series of specific hardware components which execute specific tasks. Microprocessor-based configurations and discrete logic design techniques are utilized for the overall system hardware configuration. The proposed methodology, does not violate the system hierarchical structure. A case study demonstrates the feasibility of the approach.     

Robust adaptive tracking control of robotic systems with uncertainties

To deal with the uncertainty factors of robotic systems, a robust adaptive tracking controller is proposed. The knowledge of the uncertainty factors is assumed to be unidentified; the proposed controller can guarantee robustness to parametric and dynamics uncertainties and can also reject any bounded, immeasurable disturbances entering the system. The stability of the proposed controller is proven by the Lyapunov method. The proposed controller can easily be implemented and the stability of the closed system can be ensured; the tracking error and adaptation parameter error are uniformly ultimately bounded （UUB）. Finally, some simulation examples are utilized to illustrate the control performance.     

Design principles of adaptive cellular immunity for artificial immune systems

Artificial immune systems (AISs) have been proposed as a new computing paradigm. This paper reviews design principles of adaptive cellular immunity, based on the immunological literature rather than the simplified mathematical models which have thus far dominated the development of framework for design, interpretation, and application of AISs. An earlier version of this work was presented as a position paper at the ARTIST Network for Artificial Immune Systems meeting held on 8th–9th November 2004.     

An adaptive control algorithm for robotic deburring

This paper presents the topic of adaptive control for robotic deburring by presenting some nonreal-time and some real-time schemes found in literature. Thereafter, a real-time adaptive control algorithm for robotic deburring is presented. Experiments are presented in which an IBM RS/1 robot is used to simulate the operation of the algorithm.     

Analysis and design of a new piezoresistive tactile sensor system for robotic applications

The development of an experimental tactile sensor system fitted on a robot work-table is analyzed in this paper. In the first stage of this research a 16 × 16 piezoresistive sensor was used, attached on the work-table of an ASEA IRB-2000 robot. The keypoint of the above design is that the sensor is not used just to obtain texture information, as it is happens when it is fitted on the gripper, but also to obtain tactile data from the object nonvisible base-surface and finally the object weight. The experimental system is designed so as to allow variation in the design parameters to determine the best set of parameter values for optimal performance of the sensor. Experiments carried out show the operability of the above system and, furthermore, the advantages using this sensor topology.     

Wave-variable framework for networked robotic systems with time delays and packet losses

This paper investigates the problem of networked control system for nonlinear robotic manipulators under time delays and packet loss by using passivity technique. With the utilisation of wave variables and a passive remote controller, the networked robotic system is demonstrated to be stable with guaranteed position regulation. For the input/output signals of robotic systems, a discretisation block is exploited to convert continuous-time signals to discrete-time signals, and vice versa. Subsequently, we propose a packet management, called wave-variable modulation, to cope with the proposed networked robotic system under time delays and packet losses. Numerical examples and experimental results are presented to demonstrate the performance of the proposed wave-variable-based networked robotic systems.     

Robust adaptive finite‐time parameter estimation and control for robotic systems

This paper studies adaptive parameter estimation and control for nonlinear robotic systems based on parameter estimation errors. A framework to obtain an expression of the parameter estimation error is proposed first by introducing a set of auxiliary filtered variables. Then three novel adaptive laws driven by the estimation error are presented, where exponential error convergence is proved under the conventional persistent excitation (PE) condition; the direct measurement of the time derivatives of the system states are avoided. The adaptive laws are modified via a sliding mode technique to achieve finite‐time convergence, and an online verification of the alternative PE condition is introduced. Leakage terms, functions of the estimation error, are incorporated into the adaptation laws to avoid windup of the adaptation algorithms. The adaptive algorithm applied to robotic systems permits that tracking control and exact parameter estimation are achieved simultaneously in finite time using a terminal sliding mode (TSM) control law. In this case, the PE condition can be replaced with a sufficient richness requirement of the command signals and thus is verifiable a priori. The potential singularity problem encountered in TSM controls is remedied by introducing a two‐phase control procedure. The robustness of the proposed methods against disturbances is investigated. Simulations based on the ‘Bristol‐Elumotion‐Robotic‐Torso II’ (BERT II) are provided to validate the efficacy of the introduced methods. Copyright © 2014 John Wiley & Sons, Ltd.     

A multimodal teaching advisor for sensor-enhanced robotic systems in manufacturing

A multimodal teaching advisor (MTA) has been developed for sensor-enhanced robotic systems used in manufacturing. The MTA utilizes the work-site operator's know-how and robotic-systems information, including that from sensors, in a complementary manner. This system integrates information from various sources to carry out robotic tasks and presents this information through user interfaces that are easily understood by the operator. That is, the system integrates task specifications including tolerances, system constraints, sensory information, and operator- and robot-related information. The synthesized information is then presented in realtime to the operator at the work-site through a mobile multimodal interface. Experimental results show that this system can significantly improve total robotic system performance by ensuring a high-quality teaching task.     

Proposal of a shape adaptive gripper for robotic assembly tasks

This paper proposes a novel robotic gripper used for assembly tasks that can adaptively grasp objects with different shapes. The proposed hand has a combined structure between two kinds of shape adaptive mechanisms where one is the granular jamming and the other is a multi-finger mechanism driven by a single wire. Due to the effect of the two shape adaptive mechanisms, the pose of a grasped object does not change during an assembly operation. The proposed hand has four fingers where two are the active ones and the other two are the passive ones. The pose of the grasped object can be uniquely determined since the passive fingers are used to orient an object placed on a table before the active fingers are closed to grasp it. Assembly experiments of some kinds of parts are shown to validate the effectiveness of our proposed gripper.     

The immersive effect of adaptive architecture

This paper explores the role of immersion in the generation of specific interactive effects, within the context of the emerging research field of Adaptive Architecture. Drawing on an existing biofeedback-driven prototype that links a person’s respiration to the form of their environment, the study presented here compared an immersive condition with a non-immersive condition to capture differences in participant experiences. The immersive condition afforded the majority of participants a relaxed, embodied experience, whereas the non-immersive condition left people unconnected. The study did not surface statistically significant differences in participants’ physiological responses between the two conditions. The study findings contribute to the understanding of our relationship with adaptive environments, underpinned by pervasive computing technologies, as they emerge in the Arts and Architecture.     

On the robust adaptive control of a contour-following robotic system

To demonstrate the force sensing and control, a possible model of a contour-following system is represented by a fourth-order linear continuous-time time-invariant system, in which stiffness k_t, robot natural frequency, ω_n, linear accommodation gain k_x, and angular accommodation gain k_φ are all constants obtained by measurement and experiment. This model works well for following a short contour. To satisfactorily follow a longer contour, k_t, ω_n, k_x and k_φ can be treated as unknown constants or time-varying variables. When they are considered as unknown constants, a robust model reference adaptive controller can be used to achieve both stability and tracking without having to know or find the true values of those constants, given bounded input or output disturbances and stable unmodeled dynamics. If ω_n is assumed to be a given constant but K_t, k_x, and k_φ are assumed to be unknown variables, then one has a linear time-varying plant and other types of model reference adaptive controllers have to be used to achieve the same purpose. In this paper, the schemes from various robust model reference adaptive control design will be studied and comparison and suggestions will also be made based on the simulation results for the contour-following robotic system mentioned above.     

A SysML-based methodology for mechatronic systems architectural design

Mechatronic systems are characterized by the synergic interaction between their components from different technological domains. These interactions enable the system to achieve more functionalities than the sum of the functionalities of its components considered independently. Traditional design approaches are no longer adequate and there is a need for new synergic and multidisciplinary design approaches with close cooperation between specialists from different disciplines.SysML is a general purpose multi-view language for systems modeling and is identified as a support to this work.In this paper, a SysML-based methodology is proposed. This methodology consists of two phases: a black box analysis with an external point of view that provides a comprehensive and consistent set requirements, and a white box analysis that progressively leads to the internal architecture and behavior of the system.     

An architecture for universal construction via modular robotic components

A set of modular components is presented for use in reconfigurable robotic construction systems. The set includes passive and active components. The passive components can be formed into static structures and adaptable grids carrying electrical power and signals. Passive and active components can be combined into general purpose mobile manipulators which are able to augment and reconfigure the grid, construct new manipulators, and potentially perform general purpose fabrication tasks such as additive manufacturing. The components themselves are designed for low-cost, simple fabrication methods and could potentially be fabricated by constructors made of the same components. This work represents a step toward a Cyclic Fabrication System, a network of materials, tools, and manufacturing processes that can produce all of its constituent components. These and similar systems have been proposed for a wide range of far-term applications, including space-based manufacturing, construction of large-scale industrial facilities, and also for driving development of low-cost 3D printing machines.     

Intelligent adaptive model-based control of robotic dynamic systems with a hybrid fuzzy-neural approach

We describe in this paper a new method for adaptive model-based control of robotic dynamic systems using a new hybrid fuzzy-neural approach. Intelligent control of robotic systems is a difficult problem because the dynamics of these systems is highly nonlinear. We describe an intelligent system for controlling robot manipulators to illustrate our fuzzy-neural hybrid approach for adaptive control. We use a new fuzzy inference system for reasoning with multiple differential equations for model selection based on the relevant parameters for the problem. In this case, the fractal dimension of a time series of measured values of the variables is used as a selection parameter. We use neural networks for identification and control of robotic dynamic systems. We also compare our hybrid fuzzy-neural approach with conventional fuzzy control to show the advantages of the proposed method for control.     

A language for high-level description of adaptive web systems

Adaptive Web systems (AWS) are Web-based systems that can adapt their features such as, presentation, content, and structure, based on users’ behaviour and preferences, device capabilities, and environment attributes. A framework was developed in our research group to provide the necessary components and protocols for the development of adaptive Web systems; however, there were several issues and shortcomings (e.g. low productivity, lack of verification mechanisms, etc.) in using the framework that inspired the development of a domain-specific language for the framework. This paper focuses on the proposal, design, and implementation of AWL, the Adaptive Web Language, which is used to develop adaptive Web systems within our framework. Not only does AWL address the existing issues in the framework, but it also offers mechanisms to increase software quality attributes, especially, reusability. An example application named PENS (a personalized e-News system) is explained and implemented in AWL. AWL has been designed based on the analysis of the adaptive Web domain, having taken into account the principles of reuse-based software engineering (product-lines), domain-specific languages, and aspect-oriented programming. Specially, a novel design decision, inspired by aspect-oriented programming paradigm, allows separate specification of presentation features in an application from its adaptation features. The AWL’s design decisions and their benefits are explained.     

A global adaptive learning control for robotic manipulators

This paper addresses the problem of designing a global adaptive learning control for robotic manipulators with revolute joints and uncertain dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive learning PD control is designed which ‘learns’ the input reference signals by identifying their Fourier coefficients: global asymptotic and local exponential stability of the tracking error dynamics are obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics.