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DC microgrid as a micro-scale power system, has drawn growing attention for its various applications which calls for good power quality, proper power sharing, and fast response property. However, the existing researches aren't yet able to achieve the purpose in a predefined time. In this paper, a new predefined-time secondary controller is proposed for DC microgrid to realize the objectives of both voltage regulation and current sharing among distributed generators within a predefined time. The controller is designed by employing a sign function and a $K^1$ function and by defining a new composite error, which does not need to sample the voltage of DC bus or the current of neighbors, but only the local current and voltage. The upper bound of the convergence time of both the voltage and current in the DC microgrid is just related to one parameter which can be predefined and is independent of initial error. The effectiveness of the proposed controller is verified by multiple simulations and experiments built on MATLAB/Simulink and a hardware-in-the-loop experimental platform. The conducted simulations and experiments include several typical scenarios together with a comparison, which illustrate the advantages of the proposed controller such as fast convergence rate and small overshoot.

This article investigates the synchronization problem of interconnected linear two-time-scale systems (TTSSs) with switching topology. By utilizing the Chang transformation, a distributed synchronization protocol is proposed with event-triggered communication. Static and dynamic event-triggered mechanisms are proposed successively, which both contain two separated event-triggering conditions corresponding to the slow and the fast subsystems. The existence of a strictly positive time period between any two successive transmissions is ensured regardless of the initial states. The main difficulty of this study lies in that the state jump and parametric uncertainty appear because of the system transformation. To overcome the difficulty, the system is first modeled as an uncertain hybrid system. Then, the control gain is properly designed by solving Riccati-like equations dependent on the rough bounds of the eigenvalues of communication graph Laplacians, and a piecewise quadratic Lyapunov function is proposed with which the jump caused by the switching topology is subtly evaluated. Sufficient conditions are thus established to achieve the event-triggered synchronization. Furthermore, the results are also extended to solve the synchronization problem of the interconnected impulsive linear TTSSs. Finally, three numerical examples are provided to demonstrate the effectiveness of the proposed theoretical results.

International audience; In this brief we investigate the fixed-time synchronization of competitive neural networks with multiple timescales. These neural networks play an important role in visual processing, pattern recognition, neural computing, etc. Our main contribution is the design of a novel synchronizing controller which does not depend on the ratio between the fast and slow time scales. This feature makes the controller easy to implement since it is designed through well-posed algebraic conditions (i.e. even when the ratio between the time scales goes to 0 the controller gain is well defined and does not go to infinity). Last but not least, the closed-loop dynamics is characterized by a high convergence speed with a settling time which is upper-bounded and the bound is independent of the initial conditions. A numerical simulation illustrates our results and emphasizes their effectiveness. Index Terms-Competitive neural network, multiple timescale feature, fixed-time synchronization, continuous control method.

Normally, achieving asymptotic convergence in the presence of unknown arbitrarily bounded external disturbance requires non-continuous control signals. In this paper, an adaptive backstepping controller for a class of incommensurate fractional-order nonlinear systems with parametric uncertainties and external time-varying disturbance is proposed to realize asymptotic perfect output tracking. Appropriate adaptive laws are designed to deal with the uncertainties and unknown bounded disturbance. In the designed adaptive backstepping control scheme, an auxiliary function is adopted to obtain a smooth control signal. It is theoretically shown that asymptotic output tracking to a given reference signal is achieved, while ensuring global system stability in the sense that all the closed-loop signals are bounded. Simulation and experimental studies demonstrate the effectiveness of the proposed backstepping control scheme.

In this letter, we address the design of a finite-dimensional control law based on sampled-data for a class of linear continuous-time fractional-order systems. Using the proposed finite-dimensional controller which only requires a finite number of previous control inputs to compute the current control signal, input-to-state stability (ISS) is established for the corresponding closed-loop systems by considering the hereditary and infinite memory properties of the fractional-order systems. A sufficient condition for determining the control signal storage space is also provided to guarantee certain control performance. Numerical simulations illustrate the effectiveness of the proposed design methods and validate the theoretical results.

In this article, we propose an economic storage sharing framework for prosumers and energy storage providers (ESPs) to promote renewable energy utilization cooperatively. The optimal shared capacities of ESPs and the energy sharing profiles of prosumers are first derived via minimizing social energy costs. Then the storage sharing profits of ESPs and the energy sharing payments of prosumers are successively determined by the asymmetric bargaining-based benefits sharing model. Specifically, the prosumer group bargains with the ESPs with the nominal required capacity and the shared capacities as their bargaining power to share the storage sharing benefits. Then prosumers bargain with each other to share the energy sharing benefits with their bargaining power quantified by a nonlinear energy sharing mapping method. Therefore, the benefits sharing model based on the contributions of prosumers and ESPs is fair enough for the participants. Numerical simulation tests verify the efficiency of the proposed framework.

In this paper, a dynamic average consensus-based distributed fixed-time secondary control scheme is proposed for an islanded DC microgrid, to achieve both current sharing among the converters and voltage regulation of the DC bus within a fixed settling time. The secondary controller is designed using sign functions and fraction-based feedback signals for accelerating the convergence. To release the requirement on the piror knowledge of the communication topology of DC microgrid, an adaptive law is proposed to update the control gain autonomously. The proposed secondary controllers do not need to deliver the output current data to the neighbors and sample the voltage of the DC bus, making them easier to be implemented in practice. To demonstrate the effectiveness of the proposed secondary controllers, five simulation cases and three experiment tests are carried out. It can be illustrated that the proposed secondary control schemes are resilient to the constant power loads and sources, the failure of communication links, and the scenario of plug-and-play.

This paper proposes a resilient controller for DC microgrid to achieve current sharing and voltage restoration under discrete-time false data injection (FDI) and denial-of-service (DoS) attacks. Switching and impulsive signals are used to model the dynamic system of DC microgrid under DoS and FDI. To deal with the cyber attacks, a combined error of current and voltage is proposed and a switching secondary controller is designed. Based on the stability analysis method on hybrid systems, we establish a sufficient condition for selecting control parameters in relation to the average dwell time of FDI attack and the normal communication rate under DoS attack. Furthermore, an adaptive gain based control scheme is proposed to relax the requirement on knowledge of the cyber attacks in control parameter design. The utility of the results is illustrated through case studies on a tested DC microgrid.

This article investigates the current sharing and voltage regulation problem of a DC microgrid by a distributed supervisory control method. In the existing secondary control methods, current sharing feedback control loop and voltage regulation feedback control loop are linearly integrated to shift the droop equation. However, the linear combination of two loops may easily offset the control effect of each other, as the control dimension is less than the objective dimension. This fact motivates us to design a distributed supervisory secondary control (DSSC) scheme to achieve a more flexible configuring of each converter. The DSSC consists of two candidate controllers and a supervisor. The two controllers refer to current sharing configuring and DC bus voltage regulation respectively, and the supervisor is to orchestrate the switching between candidate controllers based on the system environment of DC microgrid. Under the proposed DSSC, each converter selects to configure the current sharing or regulates the DC bus voltage according to its need. Theoretical stability analysis is rigorously conducted. Finally, both simulation and experiment results are presented to demonstrate the effectiveness of DSSC.

Traditionally, the practice of the tibial component placement in total knee arthroplasty has focused on achieving maximum coverage without malrotation. However, the concept of maximizing coverage has not been well defined or researched and yet biased results are often produced. This study aimed to evaluate the effect of a prioritizing maximum coverage positioning strategy on the rotational alignment by using a strict computer algorithm. Computed tomographic scans of 103 tibial specimens were used to reconstruct three-dimensional tibia models. A virtual surgery was performed to generate the resection plane with a posterior slope of 7° on the proximal tibia. Symmetrical and anatomical tibial components were placed and analyzed with an automated program designed for approximating the maximum coverage based on the coherent point drift algorithm. We found that the average tibial coverage achieved across all specimens and implants was 85.62 ± 3.65%, ranging from 83.64 ± 4.10% to 86.69 ± 3.07%. When placed for maximal tibial coverage, the mean degree of rotation related to the Insall line was − 0.73° ± 4.53° for all subjects, 23% of the tibial components were malrotated. The average percentage position of the baseplate anteroposterior axis over the patellar tendon was 26.95 ± 14.71% from the medial edge. These results suggest that with specific design and proper placement of the component, approximating the maximum tibial coverage in total knee arthroplasty does not necessarily result in implant malrotation. The current tibial baseplates have shown good performance on the coverage when aligned parallel to the Insall line with the anteroposterior axis positioned between the medial 1/3 and medial 1/6 of the patella tendon.

To demonstrate the discrepancies of the international standards on compatibility evaluation for energetic additives, a comparative study on compatibility of three typical graphene-based catalysts with common energetic ingredients has been done by using DSC and VST methods. Based on some unusual findings, further suggestions are proposed on improvement in the compatibility evaluation standard. The compatibility results from DSC and VST methods for HMX/GT-Ni, HMX/GC-Cu, AP/GC-Cu, AP/GT-Ni and RDX/GC-Cu systems are contradictory. The major discrepancies between DSC and VST results are caused by combination of contradictory nature of stabilization and catalytic effects of these additives on the corresponding energetic ingredients. It has been shown that the HMX/GC-Ni, HMX/GT-Ni, AP/GT-Ni, NC/GC-Ni, NC/GT-Ni, NC/GT-Cu and NC/GA-Cu mixtures have good compatibility, and the RDX/GC-Ni, AP/GC-Ni and NC/GC-Cu mixtures have moderate compatibility. In general, the copper-based additives even under the stabilization of graphene oxide are not so compatible with nitramine oxidizers and AP. These results indicate that each of the evaluation standards on compatibility has its own feature and limitation. The DSC standard is probably not suitable for the evaluation of the compatibility of GC-Ni, GC-Cu, GT-Ni, GT-Cu and GA-Cu with energetic ingredients. The compatibility assessment cannot simply rely on the available standards, and more comprehensive evaluation procedures need to be established according to the nature of the materials evaluated.

With the penetration of computation intelligence, an increasing number of learning methods are developed into power engineering, such as dc microgrid applications. This paper establishes a distributed iterative learning framework to solve the current sharing and voltage regulation problem in an islanded dc microgrid from the perspective of game theory. The control objectives of dc microgrid include not only achieving the desired output current dispatch, but also regulating the voltage of dc bus to its rated value. Based on the two objectives, local performance indexes are established and an N -player game is formulated. Each source aims to minimize its own performance index and to achieve the current sharing objective simultaneously. Under the framework of game theory, a distributed iterative learning algorithm is designed based on the Bellman optimality principle and subsequently carried out using the approximate dynamic programming technique. The proposed algorithm is data based where it does not require to have the accurate model parameters of the dc microgrid and it ensures that the dc microgrid falls into a Nash equilibrium. Furthermore, a rigorous convergence analysis of the proposed algorithm is given. To demonstrate the effectiveness of the proposed method, simulation examples are presented on a tested dc microgrid.

Reinforcement learning (RL) has attracted great interest from researchers in recent years. RL performs as human or better in many fields such as games and robot control. Although this technology is booming in computer science, it has not been practically applied in industrial process control. Up to now, proportional–integral–derivative (PID) control is still the most dominating and popular control method in industrial control. In this paper, we propose a combination of deep reinforcement learning (DRL) and PID control for better process control performance. The idea is generated by the following observations: for PID controller, its transient performance is not usually well enough to meet a strict requirement or in complex signal tracking tasks; For RL technology, a perfectly designed reward function is required for training. However, in practice, the reward function needs to be tested through trial and error, which will lead to a waste of computational power and time. By combining these two strategies, PID controller can help to improve the steady-state performance of RL control by its integral term, while the trained RL agent is able to improve the transient performance of PID controller. Several case studies with the water tank system are presented to demonstrate the effectiveness of the combined PID + RL control strategy.

This paper deals with the large category of convex optimization problems on the framework of second-order multi-agent systems, where each distinct agent is assigned with a local objective function, and the overall optimization problem is defined as minimizing the sum of all the local objective functions. To solve this problem, two distributed optimization algorithms are proposed, namely, a time-triggered algorithm and an event-triggered algorithm, to make all agents converge to the optimal solution of the optimization problem cooperatively. The main advantage of our algorithms is to remove unnecessary communications, and hence reduce communication costs and energy consumptions in real-time applications. Moreover, in the proposed algorithms, each agent uses only the position information from its neighbors. With the design of the Lyapunov function, the criteria about the controller parameters are derived to ensure the algorithms converge to the optimal solution. Finally, numerical examples are given to illustrate the effectiveness of the proposed algorithms.

The problem of singularly perturbed Lur’e systems is investigated in this brief. Two new storage functions are constructed to explore the dynamics of both the fast and slow models, as well as their interaction more efficiently. By using the merit of the proposed storage functions, some new $\varepsilon $ -uniformly strict dissipativity criteria in terms of well-conditioned linear matrix inequalities are derived. Moreover, a differential evolution algorithm is proposed to obtain the upper bound of singular perturbation parameter. The obtained results are theoretically and numerically less conservative than the existing results.

This paper addresses a one-dimensional optimal persistent monitoring problem using second-order agents. The goal is to control the movements of agents to minimize a performance metric associated with the environment (targets) over a finite time horizon. In contrast to earlier results limited to first-order dynamics for agents, we control their accelerations rather than velocities, thus leading to a better approximation of agent behavior in practice and to smoother trajectories. Bounds on both velocities and accelerations are also taken into consideration. Despite these added complications to agent dynamics, we derive a necessary condition for optimality and show that the optimal agent trajectories can be fully characterized by two parameter vectors. A gradient-based algorithm is proposed to optimize these parameters and yield a minimal performance metric. In addition, a collision avoidance algorithm is proposed to solve potential collision and boundary-crossing problems, thus extending the gradient-based algorithm solutions. Finally, simulation examples are included to demonstrate the effectiveness of our results.

This paper studies the current sharing problem of a dc microgrid using the hybrid dynamic control method. The hybrid dynamic controller framework is established including a continuous-time part and a discrete-time part, where the former part eliminates the voltage deviation of the dc bus and the latter part ensures the current sharing accuracy of the dc microgrid. The proposed distributed hybrid secondary controller not only guarantees a high accuracy of current sharing but also maintains the voltage regulation at the dc bus. Different from most existing methods, it only utilizes the sampling output current information of neighbors at the discrete time instants, which greatly reduces the communication burden. Under the framework of stability analysis on the closed-loop system, the proposed hybrid dynamic controller achieves both current sharing and voltage regulation if the average interacted interval of the discrete time interaction satisfies a bounded constraint. Besides, a detailed parameter design of the controller is provided. Finally, simulation and experimental tests are presented to demonstrate the effectiveness of the proposed method.

This paper proposes a model reference control (MRC) scheme for general discrete-time linear time-invariant (LTI) systems subject to output quantization and saturation. In such a scheme, the quantized-output feedback MRC law is analytically specified only by using external reference input and saturated-and-quantized output. It is proven that, by appropriately designing the sensitivity of the quantizer, the MRC law can ensure that all closed-loop signals are bounded and the output tracking error converges to a certain small residual set in a certain finite time only under the minimum-phase condition. Particularly, the proposed MRC scheme does not rely on the coprimeness of zero and pole polynomials or initial conditions that are commonly used in the related literature. A representative example is given to demonstrate the design procedure and verify the validity of the proposed MRC scheme.