Visual feedback control systems for the swirling-flow melting furnace

With the growing interest of integrating robotics into everyday life and industry, the requirements towards the quality and quantity of applications grows equally hard. This trend is profoundly recognized in applications involving visual perception. Whereas visual sensing in home environments tend to be mainly used for recognition and localization, safety becomes the driving factor for developing intelligent visual control algorithms. More specifically, a robot operating in a human environment should not collide with obstacles and executed motion should be as smooth as possible. Furthermore, as the environment is not known on beforehand, a high demand on the robustness of visual processing is a necessity. On the other hand, in an industrial setting, the environment is known on beforehand and safety is mainly guaranteed by excluding a human operator. Despite these reasons, and the fact that visual servoing has gained much attention from industry to become a standard solution for robotic automation tasks, applications are highly simplified. For example, methods such as visual fault detection are already a mature technique in industrial manufacturing, where a fixed camera observes a product (e.g., on a conveyor belt) and checks whether it meets certain requirements. These operations can be executed at a fairly high rate due to the simplicity of the system (e.g., static camera) and the simplification of the processing task (e.g., binary images). For both areas the identified difficulties are similar. Foremost, this is the slow nature of (robust) visual processing, in respect to the ever growing demand of increasing speed and reducing delay. These two application areas with analogous limitations motivate the design of more direct approaches of vision in visual control systems. Therefore, in order to meet the requirements for next generation visual control systems, this thesis presents approaches which employ visual measurements as a direct feedback to design constrained motion. First, for industrial robotics, in order to obtain the required positioning accuracy, the measurement and fixation system have to be highly rigid and welldesigned, implying high cost and long design time. By measuring the position of objects directly with a camera, instead of indirectly by motor encoders, the requirements of the measurement and fixation system are less demanding. Moreover, this motivates the miniaturization of the complete control system. The approach is validated in experiments on a simplified 2D planar stage (i.e., considerable friction, poor fixation), which attains similar performance compared to encoder-based positioning systems. Secondly, in a human-centred environment, this direct sensing can improve traditional visual control systems, when subject to certain disturbances. More specifically, a method is proposed that uses an image-based feedforward controller on top of traditional position-based visual servo control to overcome disturbances such as friction or poorly designed local motor controllers. This visual feedforward control action is only active when an image-based error is present and vanishes when that error goes to zero. The method is validated on an anthropomorphic robotic manipulator with 7 degrees of freedom, intended for operation in the human care environment. Third, sensing the product directly gives rise to designing motion directly. Whereas in traditional approaches the motion trajectory is designed offline and can not be changed at runtime, direct trajectory generation computes the motion of the next step based on current state and events. This means that at any instance in time, the trajectory of a motion system can be altered with respect to certain desired kinematic or dynamic constraints. For industrial applications this makes manufacturing on near-repetitive or non-rigid structures (e.g. flexible displays) possible. When applied to a robotic manipulator, this enables obstacle avoidance to no longer be on path planning level, but on trajectory planning level, where kinematic or dynamic constraints can be taken into account. This results in a motion that is smoother than when obstacle avoidance with path planning is employed. For both application areas this direct trajectory generation method is implemented and shows high flexibility in constrained motion trajectory design.

Autonomous High Speed Road Vehicle Guidance by Computer Vision 1

In this paper, the autonomous navigation of six-crawler machine is studied, and a visual tracking control method based on machine vision for fuzzy proportional–integral–derivative control of six-crawler machine is proposed. The steering principle of the six-crawler machine and the matching relationship between the steering angle and the speed of each crawler are introduced, and the control system is described in detail. Besides, the mathematical model for the unsteady steering is introduced to analyze the influence of deflection angle on the steering trajectory of the six-crawler machine. The image processing algorithm is programmed by LabVIEW software. After the image is fitted by graying, binary, filtering, edge detection, and least square method, the navigation line-fitting curve is obtained. The fuzzy proportional–integral–derivative control algorithm is programmed in the control system to control the six-crawler machine to drive along the navigation line. In order to obtain reasonable control parameters, a virtual prototype model of a six-crawler machine is established. In the CoLink module, the control algorithm of a six-crawler machine is established, and the co-simulation is carried out. By analyzing the simulation results, the control parameters of the fuzzy proportional–integral–derivative controller of the six-crawler machine are established. In order to verify the control effect of the visual tracking control system of the six-crawler machine, a physical prototype of the six-crawler machine is constructed and tested. The results show that the visual tracking control system of the six-crawler machine can complete the preset functions.

BackgroundControl of reaching movements for manual work, vehicle operation, or interactions with manual interfaces requires concurrent gaze control for visual guidance of the hand. We hypothesize that reaching movements are based on negotiated strategies to resolve possible conflicting demands placed on body segments shared by the visual (gaze) and manual (hand) control systems. Further, we hypothesize that a multiplicity of possible spatial configurations (redundancy) in a movement system enables a resolution of conflicting demands that does not require sacrificing the goals of the two systems.MethodsThe simultaneous control of manual reach and gaze during seated reaching movements was simulated by solving an inverse kinematics model wherein joint trajectories were estimated from a set of recorded hand and head movements. A secondary objective function, termed negotiation function, was introduced to describe a means for the manual reach and gaze directing systems to balance independent goals against (possibly competing) demands for shared resources, namely the torso movement. For both systems, the trade-off may be resolved without sacrificing goal achievement by taking advantage of redundant degrees of freedom. Estimated joint trajectories were then compared to joint movement recordings from ten participants. Joint angles were predicted with and without the negotiation function in place, and model accuracy was determined using the root-mean-square errors (RMSEs) and differences between estimated and recorded joint angles.ResultsThe prediction accuracy was generally improved when negotiation was included: the negotiated control reduced RMSE by 16% and 30% on average when compared to the systems with only manual or visual control, respectively. Furthermore, the RMSE in the negotiated control system tended to improve with torso movement amplitude.ConclusionsThe proposed model describes how multiple systems cooperate to perform goal-directed human movements when those movements draw upon shared resources. Allocation of shared resources can be undertaken by a negotiation process that is aware of redundancies and the existence of multiple solutions within the individual systems.

This paper reports the outcome of an exhaustive analytical and numerical investigation of stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances. The class of adaptive algorithms considered are those commonly referred to as model-reference adaptive control algorithms, self-tuning controllers, and dead-beat adaptive controllers; they have been developed for both continuous-time systems and discrete-time systems. The existing adaptive control algorithms have been proven to be globally assymptotically stable under certain assumptions, the key ones being (a) that the number of poles and zeroes of the unknown plant are known, and (b) that the primary performance criterion is related to good command following. These theoretical assumptions are too restrictive from an engineering point of view. Real plants always contain unmodeled high-frequency dynamics and small delays, and hence no upper bound on the number of the plant poles and zeroes exists. Also real plants are always subject to unmeasurable output additive disturbances, although these may be quite small. Hence, it is important to critically examine the stability robustness properties of the existing adaptive algorithms when some of the theoretical assumptions are removed; in particular, their stability and performance properties in the presence of unmodeled dynamics and output disturbances. A unified analytical approach has been developed and documented in the recently completed Ph.D. thesis by Rohrs [15] that can be used to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite-gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. The practical engineering consequences of the existence of the infinite-gain operator are disastrous. Analytical and simulation results demonstrate that sinusoidal reference inputs at specific frequencies and/or sinusoidal output disturbances at any frequency (including d.c.) cause the loop gain of the adaptive control system to increase without bound, thereby exciting the (unmodeled) plant dynamics, and yielding an unstable control system. Hence, it is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

Due to the development of high technologies, there is a need to use computer systems to support the increasingly complex types of human activities. It provides high accuracy and speed of various research and medical examinations. Taking into account the daily workload of doctors in the Intensive Care Unit, remote medical diagnostics systems are being implemented in many referral hospitals to predict the course of diseases. Decision-making is the result of processing certain information, the medical history patients based on the use of accumulated knowledge. Therefore, it can be concluded that automatic and visual control systems make the diagnosis process more accurate and easier. The aim of the work is to develop an optoelectronic system for visual monitoring and data transmission for patients in a coma by Li-Fi technology.

The incomplete and untrue data caused by cyber attacks (e.g., image information leakage and tampering) will affect control performance and even lead to system instability. To address this problem, a novel co-design secure control method based on image attack detection and data compensation for networked visual control systems (NVCSs) is proposed. Firstly, the existing problems of NVCSs under image attacks are analysed, and a co-design secure control method including image encryption, watermarking-based attack detection and online data compensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarking is designed for real-time, integrity and authenticity discrimination of the images. Furthermore, according to the detection results, an online compensation scheme based on cubic spline interpolation and post-prediction update is proposed to reduce the effect of cumulative errors and improve control performance. Finally, the online compensation scheme is optimized by considering the characters of networked inverted pendulum visual control systems, and experimental results demonstrate the feasibility and effectiveness of the proposed detection and control method.

To implement a position-based visual feedback controller for a manipulator, it is necessary to calibrate the homogeneous transformation matrix between its base frame and the vision frame besides intrinsic parameters of the vision system. The accuracy of such a calibration greatly affects the control performance. Substantial efforts must be made to obtain a highly accurate transformation matrix. In this paper, we propose an adaptive visual feedback controller for manipulators when the homogeneous transformation matrix is not calibrated. It is assumed that the vision system can measure the 3D position and orientation of the manipulator in real-time. Based on an important observation that the unknown transformation matrix can be separated from the visual Jacobian matrix, we propose an adaptive algorithm, similar to the model-based adaptive algorithm, to estimate the unknown matrix online. The use of the proposed visual feedback controller greatly simplifies the implementation of a manipulator-vision workcell. This controller is especially useful when such a pre-calibration is not possible. It is proved by Lyapunov approach that the motion of the manipulator approaches asymptotically to the desired trajectory. Simulations and experimental results are included to demonstrate performance of this adaptive visual feedback controller.

According to electric energy meter sealing in electrical metrology industry, the automatic screw holes alignment visual servo motion control technology is studied. Identification and positioning of the screw hole is conducted with digital image processing technology, the visual servo motion control procedure is designed, and finally the control software is operated in actual platform. The operating results verify that the visual servo motion control system has good progressiveness and reliability.

In this paper, we propose a visual tracking control method of a hand-eye robot for a moving target object with multiple feature points. The hand-eye robot is constructed from a three-DoF planar manipulator and a single CCD camera that is mounted on the manipulator's end-effector. The control objective is to keep all feature points of the target object around their desired coordinates on the image plane. In many conventional visual servo methods, it is assumed that the target object is static. Consequently, the visual tracking error arises in the case of a moving target object. Although we have already proposed the visual tracking control systems in consideration of the target object motion, this method can deal only with a single feature point. Therefore, this paper extends such a visual tracking control method to multiple feature points. The validity of our control method is evaluated by an experiment.

In the humanoid robot system, we used two computers responsible for visual information processing system and motion control system respectively. The visual platform of the humanoid robot is designed the same as the human head, which can search for the target object in a wide range. The target object tracking process consist of four cycle steps: search, match, refreshing and forecasting; the grasping object process contains five steps: find and coarse positioning target object, walking close to the target object, based on the visual feedforward coarse alignment, fine alignment based on visual feedback, grasp. Each step requires a different control strategy. In experiments, we use the motion capture system to measure the three-dimensional position of the target object, and as a benchmark to measure the measurement accuracy of the humanoid robot vision platform. Experiments show that based on the visual platform, the visual feedforward and the visual feedback control strategy, the target object tracking accuracy has been improved.

In order to realize the visualization,spatial analysis,automatic modeling and dynamic updating of model,the mine spatial environment and traditional 3D spatial data models were analyzed.Then an entity-oriented spatio-temporal hybrid data model was designed.It integrated the 3D parameter skeleton model,Topology-concerned and Entity-oriented 3D Vector Data Model(TEVDM) and Octree-Block Model(OBM).The TEVDM and OBM were used to express the boundary and internal property of orebodys.The Parametric Entity-Network Data Model(PENDM) was introduced into the model to describe the skeleton of the roadway engineering and production systems.Particularly,a behavior model was introduced into the model to describe the personnel and devices with behavior characteristics.The model also included the complete spatial feature which could describe the set of semantically related entities.Finally,some samples of using this model were given to describe the mines' spatial phenomenon and carry out spatial analysis.The samples show this model has better practicability in mining than traditional models.

The model-free adaptive control (MFAC) which is based on compact from format dynamic linearization (CFDL) is applied in the speed control system of automatic door. The design of the adaptive controller is model free. It is directly based on the estimation and prediction of pseudo partial derivative (PPD). It analyzed and compared the effects of speed control which displayed by model free adaptive control and PID algorithm. And it also adaptive analyzed adaptive the effects when changing the parameters adaptive of pseudo partial derivative. The results of the adaptive experiments showed that the model-free adaptive control algorithm in the control of Auto-Door servo system is better than that of PID control. And the results also highlight the model free adaptive control algorithm for fast motion system stability and robustness.

Parametric study of nonlinear adaptive control algorithm with magneto-rheological suspension systems

The aim of this paper is to answer the question whether "perception-action" dissociation, which is well documented in vision, may also be found in auditory information processing. Trained singers were asked to produce vowel sounds into a microphone. The sound that each singer produced was fed back to their ears via headphones. Two seconds after the sound production had begun, the auditory feedback was shifted in pitch by a certain degree (9, 19, 50, or 99 cents in either direction). In every set of sounds, instances without any pitch shifts also appeared. After each trial, participants reported whether they were aware of a pitch change or not. It was found that even though the participants were unaware of subtle pitch changes, the fundamental frequency of their vowel production was found to shift slightly in the opposite direction to the pitch shift. These results show that auditory information is processed by two separate systems: one for perception and one for action. They also show that the function of the auditory control system differs from the visual control system. The latter is used to control bodily movements while the function of the former is a nonconscious, instant control of vocalization.