Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning
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Model Predictive Control
Machine Vision
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Almost all industrial processes exhibit nonlinear dynamics, however most model predictive control (MPC) applications are based on linear models. Linear models do not always give a sufficiently adequate representation of the system and therefore Nonlinear Model Predictive Control (NMPC) techniques have to be used. In this article, two techniques of NMPC, namely successive linearization nonlinear model predictive control (SLNMPC) and wiener nonlinear model predictive control (WNMPC) are applied to nonlinear process systems. The major advantage of the two methods being that the NMPC problem is reduced to a linear model predictive control (LMPC) problem at each time step which thereafter allows the optimization problem to be solved using quadratic programming (QP) techniques. Another advantage of these methods is the reduced computational time in calculating the control effort which makes them suitable for online implementation. Both simulation and experimental results show the superiority of the SLNMPC over WNMPC in handling process nonlinearity. The work also shows the favourable performance of the NMPC over LMPC, as expected.
Model Predictive Control
Linearization
Nonlinear model
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In this paper, the model predictive control (MPC) technique is applied to control a multi-degree-of-freedom industrial robot. Different from the conventional MPC, the predictive model used in this paper is non-linear. In this paper, the properties of the MPC method using a non-linear predictive model to control an industrial robot are studied.
Model Predictive Control
Industrial robot
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This paper overviews the major achievement in the nonlinear model predictive control and its application.As two great branches of predictive control techniques,robust predictive control and adaptive predictive control techniques are presented in more detail.
Model Predictive Control
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Model predictive control (MPC) is one of the main process control techniques explored in the recent past; it is the amalgamation of different technologies used to predict future control action and future control trajectories knowing the current input and output variables and the future control signals. It can be said that the MPC scheme is based on the explicit use of a process model and process measurements to generate values for process input as a solution of an on-line (real-time) optimization problem to predict future process behavior. There have been a number of contributions in the field of nonlinear model–based predictive control dealing with issues like stability, efficient computation, optimization, constraints, and others. New developments in nonlinear MPC (NMPC) approaches come from resolving various issues, from faster optimization methods to different process models. This article specifically deals with chemical engineering systems ranging from reactors to distillation columns where MPC plays a role in the enhancement of the systems’ performance.
Model Predictive Control
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This chapter introduces some typical predictive control algorithms based on the basic principles, with different model types, aiming at illustrating how the predictive control algorithm can be developed by concretizing these principles. Dynamic matrix control (DMC) is one of the most widely used predictive control algorithms in industrial processes. Generalized predictive control (GPC) is another kind of predictive control algorithms rising from the research area of adaptive control. The early predictive control algorithms generally adopted an input-output model, either nonparametric such as model predictive heuristic control or DMC, or parametric such as GPC. However, since the 1990s, the state space model has been widely adopted in theoretical research of model predictive control because such research needs to take the mature theoretical results of modern control theory as reference. The prediction model aims at providing the basis for solving the optimization problem.
Model Predictive Control
Predictive Analytics
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Abstract: Deep fake is a rapidly growing concern in society, and it has become a significant challenge to detect such manipulated media. Deep fake detection involves identifying whether a media file is authentic or generated using deep learning algorithms. In this project, we propose a deep learning-based approach for detecting deep fakes in videos. We use the Deep fake Detection Challenge dataset, which consists of real and Deep fake videos, to train and evaluate our deep learning model. We employ a Convolutional Neural Network (CNN) architecture for our implementation, which has shown great potential in previous studies. We pre-process the dataset using several techniques such as resizing, normalization, and data augmentation to enhance the quality of the input data. Our proposed model achieves high detection accuracy of 97.5% on the Deep fake Detection Challenge dataset, demonstrating the effectiveness of the proposed approach for deep fake detection. Our approach has the potential to be used in real-world scenarios to detect deep fakes, helping to mitigate the risks posed by deep fakes to individuals and society. The proposed methodology can also be extended to detect in other types of media, such as images and audio, providing a comprehensive solution for deep fake detection.
Deep Neural Networks
Normalization
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Due to model predictive control can better deal with the constraints problem of nonlinear systems and improve the dynamic performance of the controlled system, therefore, this technology has attracted much attention in the field of motor drive. This article first introduced the basic principle of model predictive control, continuous control set model predictive control and finite control set model predictive control. Secondly, summarized the research status of generalized predictive control, explicit model predictive control, model predictive current control, model predictive torque control and commonly used improved model predictive control in motor drive systems. Thirdly, prospected the future development trend based on the current research status of model predictive control in motor drive system. Finally, the advantages and disadvantages of the continuous control set model predictive control and the finite control set model predictive control were comprehensively compared, and the ways in which the two algorithms act on the motor drive system were summarized.
Model Predictive Control
Motor Control
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This paper investigates the usage of Discrete-time Linear Model Predictive Control in controlling a nonlinear Coupled Tanks System. Two different schemes of Model Predictive control are employed. To begin with, a basic Model Predictive Control based on Generalized Predictive Control is used and then a Model Predictive Control approach based on Laguerre functions. Simulation results have been included which demonstrate the performance of both controllers when used to control Single-Input Single-Output Coupled Tanks System and the performance when Laguerre based Model Predictive Control is applied to Multi-Input Multi-Output Coupled Tanks System.
Model Predictive Control
Laguerre polynomials
Nonlinear model
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With the wide application of predictive control in industrial processes, the theoretical research on predictive control also achieves great development in recent years. In this paper, recent advance in research on predictive control performance is surveyed, which includes the stability, robustness, feasibility of predictive control systems, as well as nonlinear predictive control.
Model Predictive Control
Robustness
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Introduction Common Linear Models Used in Model Predictive Control Prediction in Model Predictive Control Predictive Control-The Basic Algorithm Examples - Tuning Predictive Control and Numerical Conditioning Stability Guarantees and Optimising Performance Closed-Loop Paradigm Constraint Handling and Feasibility Issues in MPC Improving Robustness-The Constraint Free Case The Relationship Between Modelling and the Robustness of MPC Robustness of MPC During Constraint Handling and Invariant Sets Optimisation and Computational Efficiency in Predictive Control Predictive Functional Control Multirate Systems Modelling for Predictive Control Appendices Conclusion
Model Predictive Control
Robustness
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