Two-layer Control Scheme Research Articles

Design, Modeling, and Validation of Grid-Forming Inverters for Frequency Synchronization and Restoration

This paper focuses on the modeling, analysis, and design of grid-forming (GFM) inverter-based microgrids (MGs). It starts with the development of a mathematical model for three-phase voltage source inverters (VSI). The voltage and current controllers consist of two feedback loops: an outer feedback loop of the capacitance-voltage and an inner feedback loop of the output inductance current. The outer voltage loop is employed to enhance the controller’s response time. The inner current loop is used to provide active damping for the resonance created by the LCL filter. A two-layer control scheme is adopted for the GFM inverter control. The primary decentralized control uses droop control and virtual impedance loops to share active and reactive power. Simultaneously, the centralized secondary control addresses frequency and amplitude deviations induced by the droop control. Additionally, a synchronization loop is proposed for seamless reconnection of GFM inverters to the MG and to connect the GFM-controlled MG to the main grid. It has the advantage that the inverter operates in GFM mode even after the synchronization has occurred. The simulation results have shown that the voltage controller ensures a 0.005 s settling time and maintains the steady-state error at its minimum value of 0.1 V. Similarly, the current controller ensures a 0.006 s settling time with a 10−5 steady-state error. The system with the designed controller has a low total harmonic distortion (THD) of 1.46% and improved power quality of the output voltage. Furthermore, a quick restoration time is observed during load steps and tripping events, with a restoration time of 1 s with 10−10 steady-state error. Synchronization is achieved within 0.8 s for the incoming inverters and requires 3 s to synchronize the MG with the main grid, maintaining a steady-state error of 10−9.

Distributed Coordination of Networked Manipulators: A Two-Layer Control Scheme

This article studies the coordination control problem of networked manipulators, where manipulators are actuated to cooperatively transport an unknown object along a prescribed trajectory. It is assumed that the accurate position and velocity information of the trajectory is known only to a subset of manipulators. A two-layer control scheme consisting of an estimation layer and a control layer is developed. At the estimation layer, prescribed-time distributed estimators are designed to estimate the position and velocity of the trajectory. The estimation errors are shown to converge to zero in pre-specified time. At the control layer, an event-driven distributed controller with a dynamic triggering mechanism is proposed, where nonuniform time-varying communication delays are considered. A distinctive feature of the proposed controller is that both control updates and communication occur at certain event instants, and thus control update burden and communication costs are reduced. Moreover, two adaptive time-varying control gains are designed so that the controller can be implemented without requiring any inequality condition or global information. Finally, a simulation platform based on MATLAB Robotics Toolbox is designed and implemented to verify the effectiveness and superiority of the proposed two-layer control scheme.

Robust TS-ANFIS MPC of an Autonomous Racing Electrical Vehicle Considering the Battery State of Charge

In this work, the trajectory tracking problem of an autonomous racing electrical vehicle is addressed. Accordingly, a two-layer control scheme is developed, such that stability, recursive feasibility, and constraint satisfaction are guaranteed. The outer layer includes a zonotopic tube-based predictive control to ensure trajectory tracking while minimizing energy consumption considering the state of charge of the vehicle's battery. Meanwhile, the inner layer combines a linear quadratic zonotopic controller with a zonotopic Kalman filter to reduce the effect of exogenous disturbances and modeling errors. Moreover, for control and estimation purposes, a data-driven Takagi–Sugeno (TS) model trained by an adaptive neuro-fuzzy inference system (ANFIS) is employed. To illustrate the performance of the proposed control scheme, a simulated 1/10 Scale RC car is used.

Leader-Follower Formation Learning Control of Discrete-Time Nonlinear Multiagent Systems.

This article investigates the leader-follower formation learning control (FLC) problem for discrete-time strict-feedback multiagent systems (MASs). The objective is to acquire the experience knowledge from the stable leader-follower adaptive formation control process and improve the control performance by reusing the experiential knowledge. First, a two-layer control scheme is proposed to solve the leader-follower formation control problem. In the first layer, by combining adaptive distributed observers and constructed in -step predictors, the leader's future state is predicted by the followers in a distributed manner. In the second layer, the adaptive neural network (NN) controllers are constructed for the followers to ensure that all the followers track the predicted output of the leader. In the stable formation control process, the NN weights are verified to exponentially converge to their optimal values by developing an extended stability corollary of linear time-varying (LTV) system. Second, by constructing some specific "learning rules," the NN weights with convergent sequences are synthetically acquired and stored in the followers as experience knowledge. Then, the stored knowledge is reused to construct the FLC. The proposed FLC method not only solves the leader-follower formation problem but also improves the transient control performance. Finally, the validity of the presented FLC scheme is illustrated by simulations.

Handling model uncertainty in control of a pressure swing adsorption unit for syngas purification: A multi-model zone control scheme-based robust model predictive control strategy

The application of a robustly stabilizing model predictive control (MPC) strategy to a pressure swing adsorption (PSA) unit, hitherto unexplored in the literature, is addressed in this work. Here, a set of linear models to handle model uncertainties describes the PSA nonlinear dynamic behaviour. Each model represents a different operating condition of the PSA, generating a multi-model uncertainty description. In addition to incorporating the multi-model uncertainty, the proposed MPC control law deals with a controlled output zone tracking scheme to systematically accommodate typical cyclic steady states of a PSA process. From the industrial standpoint, economic targets on process variables (controlled outputs, manipulated inputs, or both) are also incorporated into the robust control method. A Real-Time Optimizer usually defines these targets, making up a two-layer control scheme. The effectiveness of the robust MPC is evaluated in a syngas purification process via a single bed, six-step PSA unit composed of a porous amino-functionalized titanium terephthalate MIL-125-type MOF adsorbent.

Application of Distributed Linear Multi-Agent Containment Control to Robotic Systems

This article proposes the application of a distributed containment control algorithm to a team of mobile robots. This paper builds on the containment controller developed by Yuan et al. (2019) for generic linear multi-agent system and tested in simulation only. In this article, we particularize the controller for the case of multiple mobile robots by including it into a two-layer control scheme. The high-level controller computes a desired position for the mobile robots, that is then used as reference trajectory for the low-level controller. The resulting control system is implemented as a fully distributed system on a team of mobile robots and validated in simulations and experiments.

A Two-Layer Control Scheme Based on P – V Droop Characteristic for Accurate Power Sharing and Voltage Regulation in DC Microgrids

The increasing penetration of dc distributed energy resources (DERs) and dc loads has motivated the utilization of dc microgrids (MGs). In this paper, a new two-level control scheme is proposed for accurate power sharing and appropriate voltage regulation in dc MGs during islanded operation mode. In the primary control level, a P-· V droop method is proposed to eliminate the dependency of power sharing among DERs on line resistances. Since the P-· V droop deviates the voltage derivative to a nonzero value, a voltage derivative restoration mechanism is adopted in the secondary control level to provide voltage stability. The secondary control level also compensates the voltage deviations by cascading an outer voltage control loop with the inner voltage derivative restoration loop. The secondary control signal is broadcasted to each DER via a unidirectional low-bandwidth communication link. Small signal stability of the proposed scheme is analyzed by studying the locus of the system eigenvalues. To verify the efficacy of the proposed method and compare it with the conventional scheme, real-time simulations in OPAL-RT lab setup are provided.

Vision-based neural predictive tracking control for multi-manipulator systems with parametric uncertainty

To deal with the coordination problem for multi-manipulator trajectory tracking systems with parametric uncertainties, this paper proposes a two-layer control scheme incorporating a model predictive strategy and an extended state observer. In the kinematic layer, the visual information is implemented and a visual servoing error model is derived by the image-based visual servoing strategy. A recurrent neural network model predictive control approach is proposed to obtain velocities which are regarded as the reference signals for the dynamic layer. For dynamics, a linear time-varying dynamic model of the multi-manipulator system coupled with the object is established, where the parametric uncertainty is recognized as an added disturbance. An extended state observer is sequentially designed to estimate the disturbance by using pole placement method. The input-to-state practical stability of the system is further analyzed with a bounded disturbance. Finally, simulations and comparison are given to verify the effectiveness and robustness of the proposed algorithm.

Robust Performance Improvement of Lateral Motion in Four-Wheel Independent-Drive Electric Vehicles

In this article a robust two-layer control scheme is proposed to improve the performance of Four-Wheel Independent-Drive (4-WID) electric vehicles. The main objective is to enhance the lateral motion performance of electric vehicle under various manoeuvres and in the existence of parameter uncertainties and exterior disturbances. The upper-layer control law is proposed according to a novel terminal sliding mode approach formulated using the longitudinal velocity and yaw rate dynamics of the electric vehicle. It aims at generating the desired driving forces of each tire along with the vehicle's yaw momentum while guaranteeing chattering-free motion and finite-time stability in the existence of parametric uncertainties and disturbances. The lower-layer control law is formulated in the light of a constrained optimization problem to generate the optimal forces and torques for the in-wheel electric motors and achieve the desired longitudinal force and yaw momentum. The efficacy of the suggested approach is confirmed using CARSIM; a high-fidelity simulation package. Its performance analysis considered three standard driving manoeuvres and included a comparative analysis of other designs. The obtained results show that the offered methodology ensures the finite-time stability of the vehicle and improves its manoeuvrability and tracking performance under various driving conditions.

Two-Layer Control Scheme for Maintaining the Frequency and the Optimal Economic Operation of Hybrid AC/DC Microgrids

In this paper, a two-layer control scheme is proposed to improve the optimal economic operation of hybrid ac/dc microgrids. The proposed scheme consists of the lower layer representing a continuous dynamic control model with a solution that is analogous to the iterative solution of a decentralized economic dispatch problem for ac and dc sections. The upper layer, which includes primary, secondary, and tertiary coordinations in interlinking converters (ICs), regulates power exchanges between the two sections. Local controllers in ac and dc sections are coordinated to regulate the microgrid frequency and voltages while maintaining the economic dispatch in each section. The microgrid master control implemented in the tertiary IC coordination will optimize power exchanges between the two sections. The difference in incremental costs of ac and dc sections is utilized in the tertiary IC coordination for reducing computation and communication requirements of the optimal solution. The effectiveness of the proposed control scheme is verified by applying time-domain simulations in PSCAD/EMTDC to the optimal operation of a hybrid ac/dc microgrid.

A Two-Layer Stochastic Model Predictive Control Scheme for Microgrids

A two-layer control scheme based on model predictive control (MPC) operating at two different timescales is proposed for the energy management of a grid-connected microgrid (MG), including a battery, a microturbine, a photovoltaic (PV) system, a partially non predictable load, and the input from the electrical network. The high-level optimizer runs at a slow timescale, relies on a simplified model of the system, and is in charge of computing the nominal operating conditions for each MG component over a long time horizon, typically one day, with sampling period of 15 min, so as to optimize an economic performance index on the basis of available predictions for the PV generation and load request. The low-level controller runs at higher frequency, typically 1 min, relies on a stochastic MPC (sMPC) algorithm, and adjusts the MG operation to minimize the difference, over each interval of 15 min, between the planned energy exchange and the real one, so avoiding penalties. The sMPC method is implemented according to a shrinking horizon strategy and ensures probabilistic constraints satisfaction. Detailed models and simulations of the overall control system are presented.

Non-linear economic model predictive control of water distribution networks

This paper addresses a non-linear economic model predictive control (EMPC) strategy for water distribution networks (WDNs). A WDN could be considered as a non-linear system described by differential-algebraic equations (DAEs) when flow and hydraulic head equations are considered. As in other process industries, the main operational goal of WDNs is the minimisation of the economic costs associated to pumping and water treatment, while guaranteeing water supply with required flows and pressures at all the control/demand nodes in the network. Other operational goals related to safety and reliability are usually sought. From a control point of view, EMPC is a suitable control strategy for WDNs since the optimal operation of the network cannot be established a priori by fixing reference volumes in the tanks. Alternatively, the EMPC strategy should determine the optimal filling/emptying sequence of the tanks taking into account that electricity price varies between day and night and that the demand also follows a 24-hour repetitive pattern. On the other hand, as a result of the ON/OFF operation of parallel pumps in pumping stations, a two-layer control scheme has been used: a non-linear EMPC strategy with hourly control interval is chosen in the upper layer and a pump scheduling approach with one-minute sampling time in the lower layer. Finally, closed-loop simulation results of applying the proposed control strategy to the D-Town water network are shown.

Cooperative Output Regulation for Uncertain Nonlinear Multi-Agent Systems with Unknown Control Directions

Abstract: This paper concerns the global cooperative output regulation problem for a class of nonlinear multi-agent systems with unknown control directions and unknown exosystem. The communication among agents makes it difficult to implement the Nussbaum gain technique to handle the non-identical unknown control directions. Moreover, the unknown parameter in the exosystem, which cannot be easily handled by the estimators in existing results, makes our problem more intricate. To overcome these challenges, we employ a novel internal model candidate to develop a two-layer control scheme for an adaptive distributed controller design. Theoretic stability analysis is presented and a numerical example is given to show the effectiveness of the proposed controller.

A gradient optimization scheme for solution purification process

This paper presents a two-layer control scheme to address the difficulties in the modeling and control of solution purification process. Two concepts are extracted from the characteristics of solution purification process: additive utilization efficiency (AUE) and impurity removal ratio (IRR). The idea of gradient optimization of solution purification process, which transforms the economical optimization problem of solution purification process into finding an optimal decline gradient of the impurity ion concentration along the reactors, is proposed. A robust adaptive controller is designed to track the optimized impurity ion concentration in the presence of process uncertainties, disturbance and saturation. Oxidation reduction potential (ORP), which is a significant parameter of solution purification process, is also used in the scheme. The ability of the gradient optimization scheme is illustrated with a simulated case study of a cobalt removal process.

Two-layer control scheme for a Supercapacitor Energy Storage System coupled to a Doubly Fed Induction Generator

The intermittent nature of the wind represents a challenge to the quality of power of wind generators (WGs). For this reason, many topologies of WGs have been developed to overcome wind fluctuations. Nowadays, the most prevalent topology is the Doubly Fed Induction Generator (DFIG). This technology can compensate for up to ±30% of the total WG rated power in an attempt to maintain power output constant. In order to extend its operating range, we propose a two-layer control scheme for a supercapacitor energy storage system coupled to a DFIG. The first layer is a decoupled control developed to have the supercapacitor act as a flywheel, thereby reducing output power oscillations. The second layer is an optimization algorithm developed to use the wind forecast data to set the reference power to a small number of levels. The combined scheme is able to effectively command the DFIG to supply optimal, smooth power output. The proposed control scheme is implemented in Matlab-Simulink and simulation results demonstrate its effectiveness.

Advanced Concepts in Control of Communication Traffic

The paper presents traffic control schemes for telephone networks. It is indicated that traffic nonhoinogeneity manifests itself on different time scales. This observation underlies the functional decomposition of traffic control problem into layers. For each layer, the network status information patterns are discussed and relevant modelling techniques are proposed. Then, drawbacks of fully decentralized traffic routing are analysed. They provide a starting point for investigatioi of complex control rules. In particular, problems of two-layer control are discussed. They are aggregation of information on network status and lower layer specification of upper layer decisions. Possible approaches to these problems are illustrated and classified. Finally, an example of two-layer control scheme is presented and evaluated by means of simulations.