During the recovery process after the short fault at the grid side, the doubly-fed induction generator (DFIG) may suffer from overvoltage of the dc-link and over-modulation of the grid-side converter (GSC). Increasing...
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During the recovery process after the short fault at the grid side, the doubly-fed induction generator (DFIG) may suffer from overvoltage of the dc-link and over-modulation of the grid-side converter (GSC). Increasing...
During the recovery process after the short fault at the grid side, the doubly-fed induction generator (DFIG) may suffer from overvoltage of the dc-link and over-modulation of the grid-side converter (GSC). Increasing the dc voltage reference improves the modulation of the GSC, but increases the dc overvoltage. A larger dc capacitor suppresses the dc voltage, but deteriorates the dc modulation. The PI parameters of the DFIG may also be adjusted to suppress the dc overvoltage and improve the modulation. Coordinating the dc voltage reference, the dc capacitance, and/or the PI parameters may improve the high-voltage ride-through (HVRT) capability of the DFIG, which has not been studied yet. In this paper, the analytical expression of the trajectory sensitivities of the dc voltage with respect to above parameters is newly proposed, based on which an index of the dc voltage is newly defined to select the critical parameters. Change of dc voltage reference after the grid voltage recovery is included to damp the 2nd dc overvoltage, which adds the difficulty of solving the trajectory sensitivity. The objective function combining the areas about the dc overvoltage and over-modulation is newly proposed to optimize these parameters for the HVRT of the DFIG. The proposed model is solved with the interior point method. Numerical simulation validates the accuracy of the trajectory sensitivity. The proposed index selects the critical PI parameters which reduces the optimization time, and keeping desirable control effect. Coordination of dc capacitance and dc voltage reference is effective to suppress the dc overvoltage and improve dc modulation. Further including the PI parameters helps to damp the oscillation and reduce the recovery time of the dc voltage.
Dear Editor,This letter considers the formation control of multiple mobile robot systems (MMRS) that only relies on the local observation information. A new distributed finite-time observer is proposed for MMRS under ...
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Dear Editor,This letter considers the formation control of multiple mobile robot systems (MMRS) that only relies on the local observation information. A new distributed finite-time observer is proposed for MMRS under directed graph to estimate the relative information between each follower robot and the leader robot. Then the formation control problem is transformed into the tracking problem and a finite-time tracking controller is proposed based on the robot model feature.
In the paper, we investigate the optimization problem(OP) by applying the optimal control method. The optimization problem is reformulated as an optimal control problem(OCP) where the controller(iteration updating) is...
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In the paper, we investigate the optimization problem(OP) by applying the optimal control method. The optimization problem is reformulated as an optimal control problem(OCP) where the controller(iteration updating) is designed to minimize the sum of costs in the future time instant, which thus theoretically generates the “optimal algorithm”(fastest and most stable). By adopting the maximum principle and linearization with Taylor expansion, new algorithms are proposed. It is shown that the proposed algorithms have a superlinear convergence rate and thus converge more rapidly than the gradient descent;meanwhile, they are superior to Newton's method because they are not divergent in general and can be applied in the case of a singular or indefinite Hessian matrix. More importantly, the OCP method contains the gradient descent and the Newton's method as special cases, which discovers the theoretical basis of gradient descent and Newton's method and reveals how far these algorithms are from the optimal algorithm. The merits of the proposed optimization algorithm are illustrated by numerical experiments.
This study investigates the deterministic learning(DL)-based output-feedback neural control for a class of nonlinear sampled-data systems with prescribed performance(PP). Specifically, first, a sampleddata observer is...
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This study investigates the deterministic learning(DL)-based output-feedback neural control for a class of nonlinear sampled-data systems with prescribed performance(PP). Specifically, first, a sampleddata observer is employed to estimate the unavailable system states for the Euler discretization model of the transformed system dynamics. Then, based on the observations and backstepping method, a discrete neural network(NN) controller is constructed to ensure system stability and achieve the desired tracking performance. The noncausal problem encountered during the controller deduction process is resolved using a command filter. Moreover, the regression characteristics of the NN input signals are demonstrated with the observed states. This ensures that the radial basis function NN, based on DL theory, meets the partial persistent excitation condition. Subsequently, a class of discrete linear time-varying systems is proven to be exponentially stable, achieving partial convergence of neural weights to their optimal/actual values. Consequently, accurate modeling of unknown closed-loop dynamics is achieved along the system trajectory from the output-feedback control. Finally, a knowledge-based controller is developed using the modeling *** controller not only enhances the control performance but also ensures the PP of the tracking error. The effectiveness of the scheme is illustrated through simulation results.
The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadrati...
The maximum principle has bridged mathematical optimization to optimal control,ushering in significant developments and refinements in optimal control theory,notably during the 1960s with the advent of linear quadratic (LQ)control and linear quadratic estimation (LQE).This progression propelled optimal control theory into further advancements,encompassing stochastic control,robust/H-infinity control,model predictive control (MPC),networked control,and reinforcement learning *** control,established upon a rigorous mathematical foundation,extends static optimization theory to dynamic systems,exhibiting scientific essence,unity,and ***,since its inception,optimal control theory has served as an indispensable core role across all control-related domains,including communication-constrained control in networked systems,consensus control,cooperative control,and reinforcement learning control.
Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and *** this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic cell to ...
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Inflatable deployable structures inspired by origami have significant applications in space missions such as solar arrays and *** this paper,a generalized Miura-ori tubular cell(GMTC)is presented as the basic cell to design a family of inflatable origami tubular structures with the targeted ***,the classification of rigid foldable degree-4 vertices is studied *** the proposed GMTC is comprised of forming units(FU)and linking units(LU),types of FUs and LUs are investigated based on the classification of degree-4 vertices,*** rigid foldability of the GMTC is presented by studying the kinematics of the FUs and *** of the GMTC is analyzed to investigate multistable configurations of the basic *** variations in volume of the GMTC offer great potential for developing the inflatable tubular *** method and parametric optimization of the tubular structure with targeted configuration are *** feasibility of the approach is validated by the approximation of four different cases,namely parabolic,semicircular,trapezoidal,and straight-arc hybrid tubular structures.
In recent years,there has been a surge of interest in air-ground collaborative robotics *** research group designs a novel combination-separation air-ground robot(CSAGR),which exhibits rapid automatic combination and ...
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In recent years,there has been a surge of interest in air-ground collaborative robotics *** research group designs a novel combination-separation air-ground robot(CSAGR),which exhibits rapid automatic combination and separation *** the combination process,contact effects between robots,as well as between robots and the environment,are ***,it is essential to conduct detailed and accurate modeling and analysis of the collision impact intensity and transmission pathways within the robotic system to ensure the successful execution of the combination *** paper addresses the intricate surface geometries and multi-point contact challenges present in the contact regions of dual robots by making appropriate modifications to the traditional continuous contact force model and applying equivalent processing *** validity of the developed model is confirmed through comparisons with results obtained from finite element analysis(FEA),which demonstrates its high ***,the impact of this model on control performance is analyzed within the flight control system,thereby further ensuring the successful completion of the combination *** research represents a pioneering application and validation of continuous contact theory in the dynamics of collisions within dual robot systems.
Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity *** existing second-or...
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Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity *** existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization *** paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation *** cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage *** DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC *** results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.
Recently, multirobot systems(MRSs) have found extensive applications across various domains, including industrial manufacturing, collaborative formation of unmanned equipment, emergency disaster relief, and war scenar...
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Recently, multirobot systems(MRSs) have found extensive applications across various domains, including industrial manufacturing, collaborative formation of unmanned equipment, emergency disaster relief, and war scenarios [1]. These advancements are largely supported by the development of consistency control theory. However, traditional dynamicsfree models may cause instability in complex robotic systems. Lagrangian dynamics offers a better approach for modeling these systems, as it facilitates controller design and optimization analysis. Despite this, challenges persist with unknown parameters and nonlinear friction within the systems.
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