Data-driven inherent strain prediction and inverse deformation control for butt welds

Application of recurrent neural networks in batch reactors: Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature

This paper presents a new predictive digital control method applied to Matrix Converters (MC) operating as Unified Power Flow Controllers (UPFC). This control method, based on the inverse dynamics model equations of the MC operating as UPFC, just needs to compute the optimal control vector once in each control cycle, in contrast to direct dynamics predictive methods that needs 27 vector calculations. The theoretical principles of the inverse dynamics power flow predictive control of the MC based UPFC with input filter are established. The proposed inverse dynamics predictive power control method is tested using Matlab/Simulink Power Systems toolbox and the obtained results show that the designed power controllers guarantees decoupled active and reactive power control, zero error tracking, fast response times and an overall good dynamic and steady-state response.

This paper presents a new approach to end-point trajectory control of flexible macro-micro manipulator based on nonlinear inversion and predictive control techniques. In this approach, precise control of the end-effector trajectory is accomplished by the inverse controller of the rigid micro manipulator, and the predictive controller steers the end point of the flexible macro manipulator with limited elastic oscillation. The predictive control law is obtained by minimizing a quadratic function of the tip tracking error, the elastic deflection, and the input torque of the flexible macro manipulator. The feedback parameters of the predictive controller are chosen such that zero dynamics are asymptotically stable. The combination of the inverse and the predictive controllers accomplishes precise end-effector trajectory tracking and elastic mode stabilization. These results are applied to a planar macro-micro manipulator system consisting of one flexible link and two micro rigid links. Simulation results are presented to show that in the closed-loop system time varying end point trajectory control and elastic mode stabilization are accomplished.

In this article, predictive torque control (PTC) of multi-level inversion (MLI) fed open-end winding induction motor (OEWIM) drive has been developed with predictive angle control technique. The classical PTC of induction motor drive involves direct control of torque and flux. The combination of direct torque control (DTC) with model predictive control (MPC) offers all the features of classical DTC and in addition it is easily implementable for MLI fed induction motor drives. This article introduces predictive angle control strategy for MLI fed OEWIM drive to reduce torque ripple, where as the classical PTC algorithm uses magnitudes of torque and flux. The proposed algorithm is developed by controlling the angle between stator flux and stator current, the voltage space vectors of MLI fed OEWIM drive are classified according to operating frequency range. The proposed PTC algorithm requires same computational time as that of classical PTC. The proposed algorithms are verified through simulation and experimental studies.

This paper develops a novel predictive position control (PPC) based on inverse system method for fast response and high-performance operation which is applied to the active magnetic bearing (AMB) flywheel system. Due to the simplicity and lower-order of decoupling model based on inverse system control (ISC) method, the proposed controller could combine deadbeat and direct predictive control (DPC) expediently. Its performance is compared to conventional PID position control and PID-ISC. Simulation results have demonstrated modified characteristics including faster position response, no parameter tuning and adjustable current constraints.

Using only two body flaps to control the attitude of the reentry vehicle, belonging to under-actuated system, probably lead to unstable internal dynamics in lateral movement. In order to solve the problem, based on using output-redefinition to stabilize internal dynamic, a predictive controller is put forward to make the tracking error between next states and next standard command to be minimum, so as to achieve the optimal controller. The simulation result shows that controller performances very well, and the precision of the predictive algorithm is superior to the inverse dynamic controller.

In order to improve the robustness of the traditional inverse predictive control, An inverse predictive control method of nonlinear least squares support vector machine (LS-SVM) based on model-free adaptive control(MFAC) compensation is proposed. Firstly, the LS-SVM inverse system method is combined with predictive control method, and the MFAC controller is designed to modify the inverse model online at the same time, so as to compensate the influence on the system when the model deviates from the controlled object. Simulation results show that the proposed method not only has good control performance for constant and slow time-varying parameter perturbations and external disturbances, but also has good robust control performance for fast time-varying parameter perturbations and external disturbances.

Nonlinear Inverse and Predictive End Point Trajectory Control of Flexible Macro-Micro Manipulators

A new Implicit Generalized Predictive Control (IGPC) algorithm based on online Least Square Support Vector Machines (LSSVM) inverse control is proposed. Firstly, an offline model of original nonlinear system is obtained. Online LSSVM is used to identify αth-order inverse dynamic model of nonlinear systems, which can compensate the errors of nonlinear systems caused by offline identification adaptively. Then the model of online αth-order inverse plant is cascaded before positive plant to create an αth-order delay pseudo-linear composite system, which can complete decoupling and linearization of multivariable systems. Then an implicit generalized predictive control is used to control the pseudo-linear composite system. Inputs are constrained in the whole of prediction horizon and control horizon. The simulation and experiment results for the typical nonlinear system and supercritical 600MW CFB process are shown that IGPC of multivariable systems based on online LSSVM have better tracking and strong anti-interference performance.

To deal with the difficulty of nonlinear inverse model identification of directive inverse control and improve the ability of robustness and anti-interference of the open system, the paper studied the realization of inverse system identification and control using least squares support vector machine (LS-SVM), and proposed a new nonlinear predictive control algorithm based on inverse system method. The method cascades the αth-order inverse model approximated by LS-SVM with the original system to get the composite pseudo-linear system. Then the predictive control method is introduced to the pseudo-linear system. The simulation results show that the combined method does not depend on the accurate mathematical model and has the characteristics of better robustness stability, simpler design process and high tracking accuracy. And this approach is one of the applicable methods for the control of nonlinear systems.

Connected automated driving technologies have shown substantial benefits to improve the safety and efficiency of traffic. However, connected mixed traffic, which involves both connected automated vehicles and connected human-driven vehicles, is more foreseen for the realistic case in the near future. This brings new challenges because of the complexity of human elements in the system. In addition, the communication constraints in realistic connectivity such as random delays and packet losses bring even more challenges to the system. Therefore, this paper proposes a new anticipative and predictive automated vehicle control approach in connected mixed traffic. The approach first anticipates the states of surrounding vehicles including human-driven vehicles, and then integrates the anticipation into the predictive control of automated vehicles, which can help improve the control performance and also handle the communication constraints. An inverse model predictive control (IMPC) based anticipation approach has been proposed. The proposed approach, together with constant speed (CS), intelligent driver model (IDM) and artificial neural network (ANN) based anticipation methods are integrated with model predictive control (MPC) for automated vehicle control. The approaches have been tested in human-in-the-loop experiments and the results show that the integration with a newly proposed IMPC based anticipation has shown the best performance in terms of accuracy, efficiency and scalability in connected mixed traffic with both ideal and constrained communications.

Fully connected and automated vehicles have been envisioned to help improve the driving safety and efficiency of the transportation system. However, human-driven vehicles will still be present in the near future, which will lead to connected mixed traffic instead of fully connected and automated traffic. This is challenging because of the complexity of human-driving vehicles and the potential communication constraints in the connectivity. To address this issue, this paper models the connected mixed traffic and proposes model predictive control approaches with various prediction approaches including a new inverse model predictive control (IMPC) based approach to handle random communication delays and packet losses in connectivity. The human-in-the-loop experimental results for connected mixed traffic demonstrated the effectiveness and advantages of the proposed approaches, especially the predictive control with IMPC in handling communication constraints in mixed traffic.

Global nuclear security is threatened by nuclear accidents and the purposeful use of nuclear weapons. Accordingly, atmospheric pollution prediction and control for nuclear accidents, including identifying sources in nuclear or radiological incidents, predicting hazards to persons and environments, and optimally controlling accident hazards, are current areas of nuclear security research. Source inversion, hazard prediction, and optimal control are three interrelated key issues for nuclear accident emergencies. Although progress has been made in hazard prediction for nuclear accidents since the 1970s and some source inversion methods were presented after the Fukushima nuclear accident, optimal control methods are rarely reported, source inversion methods are less practical, and prediction accuracy remains unsatisfactory. Thus, novel theories are required for optimal control and source inversion for nuclear accidents, and to develop methods for simulating the influences of radioactive plume dispersion and deposition under complex meteorological and terrain conditions. This work reviews the current progress, uncertainties, and research needs in nuclear security. In addition, a rapid source inversion method based on the Lagrangian model is developed and implemented in a test case. To address future challenges, an innovative architecture for Atmospheric Pollution Prediction and Optimal Control System for nuclear accidents (APPOCS) is proposed, and the perspectives are generalized to promote future research on nuclear accident hazard prediction and optimal control. At this time, forward-looking ideas and revolutionary perspectives are required to foster nuclear security research in the academic community.

This paper presents the experimental evaluation of a flux linkage-based inverter loss model embedded in a deadbeat-direct torque and flux control (DB-DTFC) that achieves desired torque and minimizes losses over each switching period. The optimal flux commands are selected from those that will achieve the desired torque by using embedded dynamic flux linkage-based loss models of the machine and inverter. The proposed inverter loss model is implemented in real-time on a flux weakening interior permanent magnet synchronous machine (FW-IPMSM) test bench and evaluated experimentally. The actual inverter losses are obtained by measuring the electrical power at three different points in the test bench. In order to evaluate the loss model, comparative evaluation of the actual inverter losses versus the estimated losses have been performed under both constant stator flux and dynamic flux trajectories. Finally, losses resulting from using different widely used loss minimizing control strategies are compared.

Experimental verification and comparison of model predictive, PID and model inversion control in a Penicillium chrysogenum fed-batch process