In this paper, a predetermined performance controller is proposed for a modular charging system for electric vehicles to solve the problem that the entering and leaving of electric vehicles will cause large voltage fluctuation, which will affect the service life of the battery. A barrier Lyapunov function is introduced and combined with the backstepping method to design the controller to ensure that the output voltage of the system can be limited to a set range. The entering and leaving of electric vehicles will result in changes in the system's load, however, this change is unknown, so a finite-time load estimator is designed to estimate the load variation. Then the estimated load is introduced into the controller to improve the adaptive ability of the controller.
The large-scale low-cost computing power is becoming the core competitiveness of countries,so China is building a national level computing power network.Large scale computing power consumes huge amounts of electricity.In the increasingly tense energy situation,in order to meet the continuously growing power supply of computing power,based on China's"West East Power Transmission"and"East West Computing"projects,a collaborative innovation framework for computing power is proposed to build in this article,and a collaborative innovation technology framework at three levels is established:computing power nodes and power nodes,computing power market and power market,and computing power grid and power grid.And establish a collaborative innovation technology framework for the hierarchical and partitioned integration of computing power and electricity,laying the foundation for the bidirectional collaborative optimization and scheduling of computing power and electricity in the future.On this basis,deduce the collaborative innovation application of future computing power.Through the bidirectional collaborative optimized scheduling of computing power,provide competitive low-carbon electricity for massive computing power and efficient computing power support for low-carbon development of electricity.
The vanilla fractional order gradient descent may oscillatively converge to a region around the global minimum instead of converging to the exact minimum point, or even diverge, in the case where the objective function is strongly convex. To address this problem, a novel adaptive fractional order gradient descent (AFOGD) method and a novel adaptive fractional order accelerated gradient descent (AFOAGD) method are proposed in this paper. Inspired by the quadratic constraints and Lyapunov stability analysis from robust control theory, we establish a linear matrix inequality to analyse the convergence of our proposed algorithms. We prove that the proposed algorithms can achieve R-linear convergence when the objective function is $\textbf{L-}$smooth and $\textbf{m-}$strongly-convex. Several numerical simulations are demonstrated to verify the effectiveness and superiority of our proposed algorithms.
Distributed two-stage hybrid flow shop scheduling problem (DTHFSP) is often considered; however, DTHFSP with more than one processing constraints is seldom studied in the heterogeneous factories. Feedback is also seldom integrated with meta-heuristic to obtain high quality solutions. In this study, DTHFSP with sequence-dependent setup times (SDST) and preventive maintenance (PM) is investigated in heterogeneous factories and a feedback-based artificial bee colony (FABC) is proposed to minimize makespan and total tardiness simultaneously. To produce high quality solutions, two dynamic combinations of global search and neighborhood search are adopted in employed bee phase and onlooker bee phase, a new effective feedback is implemented, which is applied to decide dynamically search operator and search times of reinforcement search. Extensive experiments are conducted and the computational results validated that new strategies of FABC are effective and efficient and FABC can provide better results than methods from literature on the considered problem.
In this paper, a position control method based on generalized disturbance estimation is proposed to solve the problem of position accuracy of magnetic levitation ball system under the influence of mismatched multiple disturbances. Firstly, an Luenberger observer is designed to estimate the state variables of the system. Considering the known disturbance information, a generalized disturbance estimator is designed to estimate the disturbance using the internal model principle. Then, the disturbance estimation and its derivative are introduced into the control law design to eliminate the influence of the mismatched multiple disturbances on the position output, and the disturbance compensation gain is designed for the control law. At the same time, the reference input compensation gain is designed to solve the problem of tracking the time-varying reference position under mismatched disturbance. Then, the stability and disturbance rejection performance of the proposed method are analyzed, and it is proved that the proposed method can achieve high precision position control of the magnetic levitation ball system under mismatched multiple disturbances. In order to verify the effectiveness of the proposed method, MATLAB/Simulink is used to simulate and verify the proposed method.
To solve the permutation flow shop scheduling problem(PFSP), a mathematical model aiming at minimizing the maximum completion time is established, and a real-time scheduling method based on deep reinforcement learning is proposed. Firstly, the problem framework of PFSP is established by using the Markov decision process (MDP). According to the characteristics of the scheduling model and reinforcement learning algorithm framework, the environmental state of production scheduling based on PFSP is designed. This state extracts the completion process index of each machine in PFSP, which has a lower dimension and higher discrimination Aiming at the state and optimization objectives, an action space composed of three scheduling rules is proposed. Secondly, the double deep Q network (DDQN) is used as the agent, and the label generated in the interaction between the agent and the environment is used as the input to fit the nonlinear relationship between the state and the action value, so the agent can select the optimal behavior under different states for the system. The agent can be trained by small-scale problems to solve large-scale problems. Finally, through simulation experiments and comparison of algorithms for different test problems, it is verified that the algorithm can effectively solve the PFSP problem.
This study aimed to explore how autonomous vehicles can predict potential risks and efficiently pass through the dangerous interaction areas in the face of occluded scenes or limited visual scope. First, a Dynamic Bayesian Network based model for real-time assessment of potential risks is proposed, which enables autonomous vehicles to observe the surrounding risk factors, and infer and quantify the potential risks at the visually occluded areas. The risk distance coefficient is established to integrate the perception interaction ability of traffic participants into the model. Second, the predicted potential risk is applied to vehicle motion planning. The vehicle movement is improved by adjusting the speed and heading angle control. Finally, a dynamic simulation platform is built to verify the proposed model in two specific scenarios of view occlusion. The model has been compared with the existing methods, the autonomous vehicles can accurately assess the potential danger of the occluded areas in real-time and can safely, comfortably, and effectively pass through the dangerous interaction areas.
For a class of unknown dynamic nonlinear systems composed in discrete time,the traditional adaptive control method has the problem of poor control performance caused by low identification accuracy.To solve this problem,a new adaptive control method with unmodeled dynamic compensation based on global identification strategy is proposed.First,the equivalent correspondence between random vector function link(RVFL)network and low-order linear model and high-order unmodeled dynamic terms is mined by using the linear and enhanced structure characteristics.Then,the weight deviation penalty term is integrated to design the online updating algorithm of network model parameters to identify nonlinear system parameters.In addition,the one-step ahead optimal control strategy is used to design the linear controller and unmodeled dynamic compensator based on online identification of linear model parameters and unmodeled dynamic estimatorsl.Numerical experiments show that the proposed method is superior to the nonlinear adaptive control method based on alternating identification,and the industrial example verifies the industrial applicability of the proposed method.The potential problems of this control method in practical application and the relaxation of theoretical constraints are analyzed and prospected.
This paper proposed an adaptive super-twisting(ASTW)based sliding mode nonlinear control strategy for cooperative control of proton exchange membrane(PEM)fuel cell system.Firstly,the dynamic model of PEM fuel cell gas feeding system is developed,and the control problem of oxygen excess ratio,hydrogen excess ratio and pressure difference on both sides of membrane is formulated.Then the ASTW algorithm is proposed with the control gain regulated adaptively while suppressing the chattering.A Lyapunov function is built to prove the stability of this control strategy.Finally,simulation studies are conducted on a PEM fuel cell model built on Matlab/Simulink environment.The simulation results indicate that the developed ASTW control strategy has better dynamic control performance of oxygen excess ratio,hydrogen excess ratio and pressure difference on both sides of membrane,and strong chattering suppression ability,which is benefit to efficiency and stable operation of PEM fuel cell system.
The fixed-time stabilization control for a class of second order nonlinear system subjected to unknown ex-ternal disturbance is studied in this paper.First,a new fixed-time stabilization method is proposed,and the calculation of the convergence time and its upper bound estimation are given in details.Theoretical analysis shows that the upper bound of the convergence time can be totally determined by the system parameters independently on initial system states.Then,based on the proposed fixed-time stabilization method,a novel sliding mode reaching law and a novel terminal sliding mode surface are designed to make the system states first reaching to the sliding mode surface from any initial conditions and then converging to the original point along with the sliding mode surface in bounded finite-time,thus,the boundedness of the convergence time of the system states from anywhere to the original point is guaranteed.Meanwhile,the system state based auto switching method is employed to avoid the singular problem that caused by the terminal sliding mode surface.Finally,simulations are carried out to verify the effectiveness of the proposed method.
The spatial-temporal complementarity of wind-photovoltaic power generation can be realized in the wind-photovoltaic energy storage cluster,and the system fluctuation through energy storage can be stabilized,which make the cluster to be integrated into grid increasingly.However,there are many challenges about the reliable and economic operation of clusters,due to the fluctuation from wind-photovoltaic power energy inside cluster as well as the uncertainty of market prices and load demand outside cluster.To this end,a wind-photovoltaic-liquid air energy storage cluster optimization method based on the cooperative game considering multi-uncertainty is proposed in this paper.Firstly,by designing a cluster allocation model based on the cooperative game,the various cooperation modes and income distribution strategies between cluster participants are designed,and afterwards rationality of the model is discussed.Secondly,based on the information gap decision theory(IGDT),the optimization operation models for two type cluster market operators,i.e.,risk aversion vs.opportunity seeking,are established,taking full consideration of the risk constraints of wind energy,photovoltaic energy,day-ahead market electricity price and load uncertainty.Finally,the simulation results show that the cluster model based on the cooperative game improves the total and individual income of the cluster,compared with the cluster independent operation.The proposed optimal power purchase and energy scheduling strategies,which can adapt to the two preferences,takes into account the robustness and economy,and provides a reference for cluster operators to manage cluster energy.
For the dynamic systems with mismatched disturbances,it has an important significance to design anti-disturbance control algorithm with high resource utilization.Under the frame of double event-triggered mechanism,this paper discusses integral sliding mode anti-disturbance control and dynamic performance analysis for the unmatched dis-turbance systems.Firstly,based on the augmented model,the disturbance observer is constructed to realize the dynami-cal estimation of unknown mismatched disturbances.In order to reduce data redundancy and ensure synchronization of transmission,based on the single trigger condition of feedback state and disturbance estimation,a double event-triggered framework is proposed with the principle of first-arrival and same-trigger.Based on this,both the integral sliding surface and the corresponding double event-triggered anti-disturbance controller are designed to guarantee the controlled system convergence to the pre-designed sliding surface.Based on the Lyapunov analysis method,the gains of controller and ob-server are calculated,which ensures the favorable stability and dynamic tracking performance of the augmented system.Further,it is analyzed that the Zeno phenomenon caused by the trigger will not occur.Finally,a typical A4D model is used for the simulation verification,and the simulation results show that the proposed method has satisfactory anti-disturbance performance.
Identifiability is the property that whether a model can be uniquely determined by observational data,which is systematic studied in economics,biology,chemistry and control.In the past two decades,with the complexity of dynamical systems increasing dramatically,it is becoming more popular to model a system as a dynamical network,and identifiability of dynamical networks is attracting much attention from the academic community.Identifiability is not only a theoretical guarantee for system identification,but also a theoretical guide for experimental design and data collection in modeling.This work reviews identifiability problems of dynamical systems.Firstly,the problems of identifiability and some related definitions are given.Then classical conclusions on the identifiability of linear time-invariant systems.General approaches,such as input-output method and output-equality method,to study the identifiability for nonlinear systems are presented.The problem of identifiability and representative research of dynamical network are introduced for four different cases of excitation and observation matrices.The survey ends with discussions on related problems to be solved in future.
For the delay reason of grid-side information acquisition in the switching process of single inverter from off-grid to grid-connected,it's difficult to realize the function of self-synchronizing grid information of inverter for the classical virtual impedance method.This paper designs a finite-time full-order sliding mode(FOSM)DC bus voltage/reactive power controller.Compared with PQ controller,the proposed controller can reduce the complex phase-locked loop(PLL),can realize self-synchronous tracking of grid information including the grid-side frequency and voltage informations compared with virtual synchronous generator(VSG)control,finite-time FOSM can realize faster response speed and stronger robust-ness compared with the traditional proportional-integral control.The simulation and experimental results show that the proposed control strategies can quickly track the grid voltage information,and effectively improve the dynamic regulation ability of the inverter output voltage.
In manufacturing flow-shop,the buffer space is finite.If there exists reentrant process in products producing process,a serious production blocking,namely the deadlock,will probably occur,which will seriously affect the entire production process.To the end,a mathematical mode for reentrant flexible flow-shop with limited buffer is established,then,proposing a dynamic buffer reservation method based on Markov chain(DBRMMC)to solve the previous production blocking and deadlock problems by reserving buffer space for these reentrant jobs automatically,which can reduce the buffer space competition pressure from other jobs,thus to reduce the probability of deadlock occurrence.Besides,bringing self-adaptive threshold binarization algorithm into the DBRMMC to strengthen the ability of buffer dynamic reservation and propose an improved DBRMMC(IDBRMMC).Furthermore,combining the IDBRMMC and local dispatching rule based on the HRRN(highest response ratio next),IDBRMMC with local dispatching rule based on the HRRN(IDBRMMC-HRRN)is generated.Finally,the comprehensive simulation experiments have been conducted to verify the effectiveness of the DBRM-RFFLBS.Results show that the DBRM-RFFLBS can effectively decrease the deadlock probability in RFFLBS and give smooth and feasible scheduling results.
This paper focus on the prescribed performance platoon control of connected vehicles subject to unknown disturbances and model uncertainties,and a novel platoon control scheme based on improved sliding mode control is proposed in this paper.First,to satisfy the prescribed performance of the vehicular platoon,a novel finite-time prescribed performance function is designed,with which the tracking errors can converge to the given region in settling time.Then,an improved sliding mode control scheme is proposed,which speeds up the convergence speed of the system.The given scheme is proved to be capable of guaranteeing the individual vehicle stability and string stability in finite-time of the platoon.At the same time,the effects of unknown disturbances and model uncertainties are dealt with by introducing a set of adaptive estimation laws.Finally,the effectiveness of the proposed algorithm is verified by the simulation of platoon control of six connected vehicles in MATLAB.
The flocculation and sedimentation process of water plant has the characteristics of strong nonlinearity,uncertainty and time-varying parameters,and disturbances of sudden changes in raw water quality and water flow are easy to adversely affect the flocculation and sedimentation process.This paper proposes a control design method of second-order sliding mode based on the finite-time disturbance observer for alum dosing system.First,the feedback control of alum dosing system is designed by second-order sliding mode control method with non-smooth terms.Then,a finite-time disturbance observer is designed to estimate disturbances of sudden changes in raw water quality and water flow,as well as model mismatch caused by strong nonlinearity,uncertainty,and time-varying parameters in the flocculation and sedimentation process.The estimation result is combined with feedback control as feedforward compensation.Finally,the theoretical analysis proves the stability of second-order sliding mode control method based on the finite-time disturbance observer.The simulation results show that the composite control method proposed in this paper effectively improves the robustness and anti-disturbance performance of alum dosing system.
In order to improve the control performance of permanent magnet synchronous motor(PMSM),such as the tracking accuracy of the speed and the current,a high-order sliding mode observer based on the deadbeat predictive control method for PMSM is proposed in this paper.The mathematical model of the interior permanent magnet synchronous motor is established considering the parameter uncertainties and external load as lumped disturbances.To improve the robustness and the tracking accuracy of the proposed control algorithm,two third-order super twisting sliding mode observers are constructed to estimate and attenuate the lumped disturbances in the speed and current loops,respectively.An improved sliding mode reaching law is employed in the speed loop to reduce the chattering and improve the convergence rate.Finally,the proposed method is validated on the interior PMSM experiment platform,and compared with the conventional deadbeat predictive current control and exponential reaching law sliding mode speed controller.The experimental results show the effectiveness and superiority of the proposed method.
The observation accuracy of traditional Luenberger observer is easily affected by unknown external distur-bance.To solve this problem,an adaptive proportional-integral H2 sliding mode observer is designed in this paper,which achieves robust exact estimation of nonlinear systems with parameter uncertainties and external disturbances.Firstly,the radial basis function neural network is used to approach the complex nonlinear terms of the system model.Secondly,a linear sliding mode surface based on error is designed,and the proportional integral sliding mode term is injected into the observer,so that the sliding mode dynamic converges to the sliding mode surface in finite time,and the nonlinear compen-sation of external disturbance and system model is realized completely.Finally,an observer parameter self-tuning method is proposed based on the H2 suboptimal control and regional pole assignment.The simulation results of a single-link robot verify the proposed method can ensure the robustness and adaptability of the nonlinear system.
