Present and future robot control development—An industrial perspective

Robot control is a key competence for robot manufacturers and a lot of development is made to increase robot performance, reduce robot cost and introduce new functionalities. Examples of development areas that get big attention today are multi robot control, safe control, force control, 3D vision, remote robot supervision and wireless communication. The application benefits from these developments are discussed as well as the technical challenges that the robot manufacturers meet. Model-based control is now a key technology for the control of industrial robots and models and control schemes are continuously refined to meet the requirements on higher performance even when the cost pressure leads to the design of robot mechanics that is more difficult to control. Driving forces for the future development of robots can be found in, for example, new robot applications in the automotive industry, especially for the final assembly, in small and medium size enterprises, in foundries, in food industry and in the processing and assembly of large structures. Some scenarios on future robot control development are proposed. One scenario is that light-weight robot concepts could have an impact on future car manufacturing and on future automation of small and medium size enterprises (SMEs). Such a development could result in modular robots and in control schemes using sensors in the robot arm structure, sensors that could also be used for the implementation of redundant safe control. Introducing highly modular robots will increase the need of robot installation support, making Plug and Play functionality even more important. One possibility to obtain a highly modular robot program could be to use a recently developed new type of parallel kinematic robot structure with large work space in relation to the robot foot print. For further efficient use of robots, the scenario of adaptive robot performance is introduced. This means that the robot control is optimised with respect to the thermal and fatigue load on the robot for the specific program that the robot performs. The main conclusion of the presentation is that industrial robot development is far away from its limits and that a lot of research and development is needed to obtain a more widely use of robot automation in industry.     

Development of CAM system based on industrial robotic servo controller without using robot language

This paper describes the development of a robotic CAM system for an articulated industrial robot RV1A from the view point of robotic servo controller. It is defined here that the CAM system includes an important function which allows an industrial robot to move along cutter location data (CL data) consisting of position and orientation components. In addition, the developed CAM system has a high applicability to other industrial robots whose servo systems are technically opened to end-users. The developed robotic CAM system works as a straightforward interface between a general CAD/CAM and an industrial robot. At the present stage, the relationship between CAD/CAM and industrial robots is not well established compared to NC machine tools that are widely spread in manufacturing industries. The CAM systems for NC machine tools are already established, however, the CAM system for industrial robots has not been sufficiently considered and developed yet. A teaching pendant is generally used to obtain position and orientation data of the arm tip before an industrial robot works. Here, in order to enhance the relationship between a conventional CAD/CAM system and an industrial robot, a simple and straightforward CAM system without using any robot language is developed and implemented. The basic design of the robotic CAM system and the experimental results are presented in this paper.     

Vision‐based control architecture for human–robot hand‐over applications

Human–robot collaboration will be an essential part of the production processes in the factories of tomorrow. In this paper, a human–robot hand‐over control strategy is presented. Human and robot can be both giver and receiver. A path‐planning algorithm drives the robotic manipulator towards the hand of the human and permits to adapt the pose of the tool center point of the robot to the pose of the hand of the human worker. The movements of the operator are acquired with a multi 3D‐sensors setup so to avoid any possible occlusion related to the presence of the robot or other dynamic obstacles. Estimation of the predicted position of the hand is performed to reduce the waiting time of the operator during the hand‐over task. The hardware setup is described, and the results of experimental tests, conducted to verify the effectiveness of the control strategy, are presented and discussed.     

Control of a pilot plant using QP based min–max predictive control

The practical implementation of min–max MPC (MMMPC) is limited by the computational burden required to compute the control law. This problem can be circumvented by using approximate solutions or upper bounds of the worst possible case of the performance index. In a previous work, the authors presented a computationally efficient MMMPC control strategy in which a close approximation of the solution of the min–max problem is computed using a quadratic programming problem. In this paper, this approach is validated through its application to a pilot plant in which the temperature of a reactor is controlled. The behavior of the system and the controller are illustrated by means of experimental results.     

A parallel polynomial Jacobi–Davidson approach for dissipative acoustic eigenvalue problems

We consider a rational algebraic large sparse eigenvalue problem arising in the discretization of the finite element method for the dissipative acoustic model in the pressure formulation. The presence of nonlinearity due to the frequency-dependent impedance poses a challenge in developing an efficient numerical algorithm for solving such eigenvalue problems. In this article, we reformulate the rational eigenvalue problem as a cubic eigenvalue problem and then solve the resulting cubic eigenvalue problem by a parallel restricted additive Schwarz preconditioned Jacobi–Davidson algorithm (ASPJD). To validate the ASPJD-based eigensolver, we numerically demonstrate the optimal convergence rate of our discretization scheme and show that ASPJD converges successfully to all target eigenvalues. The extraneous root introduced by the problem reformulation does not cause any observed side effect that produces an undesirable oscillatory convergence behavior. By performing intensive numerical experiments, we identify an efficient correction-equation solver, an effective algorithmic parameter setting, and an optimal mesh partitioning. Furthermore, the numerical results suggest that the ASPJD-based eigensolver with an optimal mesh partitioning results in superlinear scalability on a distributed and parallel computing cluster scaling up to 192 processors.     

Intelligent control of a stepping motor drive using an adaptive neuro–fuzzy inference system

Stepping motors are widely used in robotics and in the numerical control of machine tools where they have to perform high-precision positioning operations. However, the variations of the mechanical configuration of the drive, which are common to these two applications, can lead to a loss of synchronism for high stepping rates. Moreover, the classical open-loop speed control is weak and a closed-loop control becomes necessary. In this paper, fuzzy logic is applied to control the speed of a stepping motor drive with feedback. A neuro-fuzzy hybrid approach is used to design the fuzzy rule base of the intelligent system for control. In particular, we used the ANFIS methodology to build a Sugeno fuzzy model for controlling the stepping motor drive. An advanced test bed is used in order to evaluate the tracking properties and the robustness capacities of the fuzzy logic controller.     

A robust adaptive dynamic surface control for a class of nonlinear systems with unknown Prandtl–Ishilinskii hysteresis

In this paper, a robust adaptive dynamic surface control for a class of uncertain perturbed strict‐feedback nonlinear systems preceded by unknown Prandtl–Ishlinskii hysteresis is proposed. The main advantages of our scheme are that the explosion of complexity problem can be eliminated when the hysteresis is fused with backstepping design and, by introducing an initialization technique, the ??_∞ performance of system tracking error can be achieved. It is proved that the new scheme can guarantee semi‐global uniform ultimate boundedness of all closed‐loop signals and make the convergence of the tracking error to an arbitrarily small residual set. Simulation results are presented to demonstrate the efficiency of the proposed scheme. Copyright © 2010 John Wiley & Sons, Ltd.     

Path-based approach to integrated planning and control for robotic systems: A path-based approach provides an analytical integration of robot planning and control. As a result, the task level control can be achieved, and the system is capable of coping with unexpected or uncertain events

Our newly developed event-based planning and control theory is applied to robotic systems. It Introduces a suitable action or motion reference variable other than time, but directly related to the desired and measurable systems output, called event. Here the event is the length of the path tracked by a robot. It enables the construction of an integrated planning and control system where planning becomes a real-time closed-loop process. The path-based integration planning and control scheme is exemplified by a single-arm tracking problem. Time and energy optimal motion plans combined with nonlinear feedback control are derived in closed form. To the best of our knowledge, this closed-form solution was not obtained before. The equivalence of path-based and time-based representations of nonlinear feedback control is shown, and an overall system stability criterion has also been obtained. The application of event-based integrated planning and control provides the robotic systems the capability to cope with unexpected and uncertain events in real time, without the need for replanning. The theoretical results are illustrated and verified by experiments.     

Observer‐based model reference control of Takagi–Sugeno–Lipschitz systems affected by disturbances using quadratic boundedness

In this paper, a model reference control strategy is proposed in order to perform trajectory tracking in Takagi–Sugeno–Lipschitz (TSL) systems. Since the state vector is assumed not to be completely available for measurement, a proportional observer is added to the control scheme in order to apply an estimate‐feedback control action instead of a state‐feedback one. The overall design of both the controller and the observer gains are performed using a Lyapunov‐based quadratic boundedness specification, in order to improve the robustness against unknown exogenous disturbances. It is shown that the conditions that ensure convergence within ellipsoidal regions of the tracking and estimation errors can be expressed in the form of a linear matrix inequality (LMI) formulation. The effectiveness of the developed control strategy is demonstrated by means of simulation results.     

Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster–Shafer’s theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.     

Robust observer–controller compensator design using the loop shaping design procedure and the algebraic method

This paper investigates robust observer‐controller compensator design using Vidyasagar's structure (VS). VS has a unit matrix parameter H similar to the Q parameter for the Youla–Kucera parameterization. VS can be designed based on the left coprimeness of the central controller in the H_∞‐loop shaping design procedure (H_∞‐LSDP) and therefore can preserve the intrinsic properties of the H_∞‐LSDP. This paper introduces algebraic methods to simplify the design of H in the VS controller by solving specific algebraic equations. In particular, the algebraic design of H can achieve two things. First, a dynamic H adjusts the tracking performance and yields the integral action. Second, a dynamic H rejects the input and output sinusoidal disturbances with known frequencies. These attributes are indications of the flexibility of the proposed method since the output‐feedback controller design of the H_∞‐LSDP cannot easily deal with such conditions. This paper discusses the achieved loop and the closed‐loop behavior of the system with VS, and also gives two numerical examples. The first example shows that the proposed method results in a better design in many aspects than the resulting from H_∞‐LSDP. The second example shows the application of the proposed method to rejecting input and output step disturbances, and input and output multiple sinusoidal disturbances, for which the H_∞‐LSDP can hardly be used. Copyright © 2009 John Wiley & Sons, Ltd.     

Neural network based switching control of AC–AC converter with DC–AC inverter for voltage sags, harmonics and EMI reduction using hybrid filter topology

A neural network based AC–AC voltage restorer is designed for voltage sags and PWM type active power filter with compound trap passive filter as a new hybrid filter are simultaneously used for voltage harmonics compensation and electromagnetic interference (EMI) reduction. First objective is to apply the neural network based switching control technique for the AC–AC voltage restorer to reduce time delays during the switching conditions and switching losses. The aim of the IGBTs used in the AC–AC voltage restorer is to test and to find the best switching frequency–power combination in the steps of the simulation. Thus, the proposed AC–AC voltage restorer has important advantages such as fast switching response, simplicity and more intelligent structure, better output waveform. The transient condition of the AC–AC voltage restorer is improved via the neural network based control technique. The second objective is the proposed strategy for elimination of voltage harmonics using PWM type DC–AC inverter part of the system as an active power filter. The last objective of the system is EMI reduction with using hybrid filter and voltage restorer together. Three problems which are voltage sags, harmonics and EMI are solved with the proposed system simultaneously.     

A sensitivity formula for risk-sensitive cost and the actor–critic algorithm

We propose for risk sensitive control of finite Markov chains a counterpart of the popular ‘actor–critic’ algorithm for classical Markov decision processes. The algorithm is based on a ‘sensitivity formula’ for the risk sensitive cost and is shown to converge with probability one to the desired solution. The proof technique is an adaptation of the ordinary differential equations approach for the analysis of two time-scale stochastic approximation algorithms.     

Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography

The expansion of urban development into wildland areas can have significant consequences, including an increase in the risk of structural damage from wildfire. Land-use and land-cover maps can assist decision-makers in targeting and prioritizing risk mitigation activities, and remote sensing techniques provide effective and efficient methods to create such maps. However, some image processing approaches may be more appropriate than others in distinguishing land-use and land-cover categories, particularly when classifying high spatial resolution imagery for urbanizing environments. Here we explore the accuracy of pixel-based and object-based classification methods used for mapping in the wildland–urban interface (WUI) with free, readily available, high spatial resolution urban imagery, which is available in many places to municipal and local fire management agencies. Results indicate that an object-based classification approach provides a higher accuracy than a pixel-based classification approach when distinguishing between the selected land-use and land-cover categories. For example, an object-based approach resulted in a 41.73% greater accuracy for the built area category, which is of particular importance to WUI wildfire mitigation.     

A tale of two coalitions – marginalising the users while successfully implementing an enterprise resource planning system

Classic analyses of system implementations view user participation as a key element for successful implementation. However, under some conditions, avoiding user participation offers an alternative route to a successful implementation; this is advisable especially when the user network is weak and aligning user needs with the technological capabilities will take too much resource. To illustrate such situation, we analyse how a successful implementation outcome of an enterprise resource planning (ERP) system emerged in a recently established conglomeration of two previously independent universities. The ERP was used to replace several legacy student administration systems for both political and functional reasons. It was deemed successful by both project consultants and the new university's management while the users were marginalised (‘black boxed’) and left to ‘pick up the pieces’ of an incomplete system using traditional methods such as shadow systems and work‐a‐rounds. Using a process approach and an actor–network theory ‘reading’ of related socio‐technical events, we demonstrate how three networks of actors – management, the project team and the administrative users – collided and influenced the implementation outcome and how the management and project network established the ERP as a reliable ally while at the same time the users – while being enrolled in the network – were betrayed through marginalisation. Our analysis also suggests a useful way to conduct a ‘follow the network’ analysis explaining and accounting for the observed implementation outcome. We illustrate the benefits of using a socio‐technical processual analysis and show how stable actor networks must be constructed during large‐scale information technology change and how different actor groups perceive and influence differently the implementation outcome.     

A scheduling quasi–min-max model predictive control algorithm for nonlinear systems

In this paper, a model predictive control algorithm is designed for nonlinear systems. Combination of a linear model with a linear parameter varying model approximates the nonlinear behavior. The linear model is used to express the current nonlinear dynamics, and the linear parameter varying model is used to cover the future nonlinear behavior. In the algorithm, a “quasi-worst-case” value of an infinite horizon objective function is minimized. Closed-loop stability is guaranteed when the algorithm is implemented in a receding horizon fashion by including a Lyapunov constraint in the formulation. The proposed approach is applied to control a jacketed styrene polymerization reactor.