A neural-network-based terminal sliding-mode control (SMC) scheme is proposed for robotic manipulators including actuator dynamics. The proposed terminal SMC (TSMC) alleviates some main drawbacks ...(such as contradiction between control efforts in the transient and tracking errors in the steady state) in the linear SMC while maintains its robustness to the uncertainties. Moreover, an indirect method is developed to avoid the singularity problem in the initial TSMC. In the proposed control scheme, a radial basis function neural network (NN) is adopted to approximate the nonlinear dynamics of the robotic manipulator. Meanwhile, a robust control term is added to suppress the modeling error and estimate the error of the NN. Finite time convergence and stability of the closed loop system can be guaranteed by Lyapunov theory. Finally, the proposed control scheme is applied to a robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.
In this paper, global adaptive finite-time stabilization is investigated by logic-based switching control for a class of uncertain nonlinear systems with the powers of positive odd rational numbers. ...Parametric uncertainties entering the state equations nonlinearly can be fast time-varying or jumping at unknown time instants, and the control coefficient appearing in the control channel can be unknown. The bounds of the parametric uncertainties and the unknown control coefficient are not required to know a priori. Our proposed controller is a switching-type one, in which a nonlinear controller with two parameters to be tuned is first designed by adding a power integrator, and then a switching mechanism is proposed to tune the parameters online to finite-time stabilize the system. An example is provided to demonstrate the effectiveness of the proposed result.
Research in light field reconstruction focuses on synthesizing novel views with the assistance of depth information. In this paper, we present a learning-based light field reconstruction approach by ...fusing a set of sheared epipolar plane images (EPIs). We start by showing that a patch in a sheared EPI will exhibit a clear structure when the sheared value equals the depth of that patch. By taking advantage of this pattern, a convolutional neural network (CNN) is then trained to evaluate the sheared EPIs, and output a reference score for fusing the sheared EPIs. The proposed CNN is elaborately designed to learn the similarity degree between the input sheared EPI and the ground truth EPI. Therefore, no depth information is required for network training and reasoning. We demonstrate the high performance of the proposed method through evaluations on synthetic scenes, real-world scenes, and challenging microscope light fields. We also show a further application of our proposed network for depth inference.
Light Field Image Processing: An Overview Gaochang Wu; Masia, Belen; Jarabo, Adrian ...
IEEE journal of selected topics in signal processing,
10/2017, Volume:
11, Issue:
7
Journal Article
Peer reviewed
Open access
Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the ...scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data.
Regarding two-player zero-sum games of continuous-time nonlinear systems with completely unknown dynamics, this paper presents an online adaptive algorithm for learning the Nash equilibrium solution, ...i.e., the optimal policy pair. First, for known systems, the simultaneous policy updating algorithm (SPUA) is reviewed. A new analytical method to prove the convergence is presented. Then, based on the SPUA, without using a priori knowledge of any system dynamics, an online algorithm is proposed to simultaneously learn in real time either the minimal nonnegative solution of the Hamilton-Jacobi-Isaacs (HJI) equation or the generalized algebraic Riccati equation for linear systems as a special case, along with the optimal policy pair. The approximate solution to the HJI equation and the admissible policy pair is reexpressed by the approximation theorem. The unknown constants or weights of each are identified simultaneously by resorting to the recursive least square method. The convergence of the online algorithm to the optimal solutions is provided. A practical online algorithm is also developed. Simulation results illustrate the effectiveness of the proposed method.
Blast furnace (BF) in ironmaking is a nonlinear dynamic process with complicated physical-chemical reactions, where multiphases and multifields interactions with long time delay phenomena take place ...during its operation. In BF operation, the molten iron temperature as well as the Si content (Si), the phosphorus content (P), and the sulfur content (S) is the most essential quality (MIQ) indices. The measurement, modeling, and control of these indices have always been important issues in metallurgic engineering and automation. This paper proposes a novel data-driven nonlinear state-space modeling method for the prediction and control of multivariate MIQ indices by integrating hybrid modeling and control techniques together. First, to improve modeling efficiency, a data-driven hybrid method that combines canonical correlation analysis and correlation analysis is established to identify the most influential controllable variables as the modeling inputs from multitudinous factors. Then, a Hammerstein model for the prediction of MIQ indices is established using the Least squares support vector machine-based nonlinear subspace identification method. Such a model is further simplified by using piecewise cubic Hermite interpolating polynomial method. Compared with the original Hammerstein model, it has been shown that this simplified model can not only significantly reduce the computational complexity, but can also exhibit a good reliability and accuracy for a stable prediction of MIQ indices. In order to verify the practicability of the developed model, it is applied to the design of a genetic algorithm based nonlinear predictive controller for the multivariate MIQ indices by directly taking the established model as a predictor. Industrial experiments show the advantages and effectiveness of the proposed approaches.
Principal component analysis (PCA) and independent component analysis (ICA) have been widely used for process monitoring in process industry. Since the operation data of blast furnace (BF) ironmaking ...process contain both non-Gaussian distribution data and Gaussian distribution data, the above single PCA or ICA method hardly describes the data distribution information of the BF process completely, which makes the monitoring and diagnosis of abnormal working-conditions only with a single method prone to false positives and false negatives. In this article, a novel integrated PCA-ICA method is proposed for monitoring and diagnosing the abnormal furnace conditions in BF ironmaking by comprehensively considering and combining the characteristics of PCA and ICA. First, the process monitoring models of PCA and ICA are, respectively, established using the actual industrial BF data, while both them are using T 2 and squared prediction error statistics to monitor whether the process is abnormal. Based on this, in order to fully reveal the internal structure of actual BF ironmaking data, an integrated PCA-ICA strategy and algorithm is proposed for comprehensively monitoring and diagnosing the abnormal furnace conditions. The corresponding unified contribution charts indices and control limits for fault identification were also presented. Finally, data experiments using actual industrial BF data show that the proposed method can obtain good results in both monitoring and diagnosing the abnormal furnace conditions of BF ironmaking.
This paper investigates the state observer design of a class of unknown input switched linear systems via mode-dependent dwell time switchings. The distinguishing feature of the proposed method is ...that strong detectability condition of subsystems of the switched systems is unnecessarily required. First, a time-varying coordinate transformation is introduced to design a suitable reduced-order observer for each subsystem. Then, computable sufficient conditions on the synthesis of the observers are proposed in the framework of a mode-dependent dwell time technique. Since the observer of an individual subsystem cannot be designed due to unavailability of strong detectability condition of the subsystem, the state of the switched system is estimated under the condition of confining the dwell time by a pair of upper and lower bounds, restricting the growth of the Lyapunov function of the active subsystem and forcing "energy" of the overall switched system to decrease at switching instants. Finally, an example is presented to demonstrate the effectiveness of the proposed method.
In this paper, a data-driven multiblock concurrent projection to latent structures (CPLS) method is proposed for monitoring large-scale manufacturing lines, particularly for cold rolling continuous ...annealing processes (CAPs) fault diagnosis. The proposed method provides decentralized process monitoring and helps localize faults in both input variables and output variables concurrently. First, the CPLS-based process monitoring method is briefly reviewed. Second, a multiblock CPLS algorithm, which incorporates process block partition, is proposed to diagnose faults relevant to process inputs or outputs with a decentralized structure. For the CAP line application, tension-specific variations, roll-specific variations, and tension-roll covariations are analyzed in each partitioned block. Furthermore, within the roll-specific subspace of an abnormal block, a delay-alignment scheme based on strip transportation delay is proposed to diagnose defective processing materials.
To perform power augmentation tasks of a robotic exoskeleton, this paper utilizes fuzzy approximation and designed disturbance observers to compensate for the disturbance torques caused by unknown ...input saturation, fuzzy approximation errors, viscous friction, gravity, and payloads. The proposed adaptive fuzzy control with updated parameters' mechanism and additional torque inputs by using the disturbance observers are exerted into the robotic exoskeleton via feedforward loops to counteract to the disturbances. Through such an approach, the system does not need any requirement of built-in torque sensing units. In order to validate the proposed framework, extensive experiments are conducted on the upper limb exoskeleton using the state feedback and output feedback control to illustrate the performance of the proposed approaches.
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