Coordination of different distributed generation (DG) units is essential to meet the increasing demand for electricity. Many control strategies, such as droop control, master-slave control, and average current-sharing control, have been extensively implemented worldwide to operate parallel-connected inverters for load sharing in DG network. Among these methods, the droop control technique has been widely accepted in the scientific community because of the absence of critical communication links among parallel-connected inverters to coordinate the DG units within a microgrid. Thus, this study highlights the state-of-the-art review of droop control techniques applied currently to coordinate the DG units within a microgrid.

A review of droop control techniques for microgrid

Humanoid robots, unmanned rovers, entertainment pets, drones, and so on are great examples of mobile robots. They can be distinguished from other robots by their ability to move autonomously, with ...

https://journals.sagepub.com/doi/pdf/10.1177/1729881419839596

A review of mobile robots: Concepts, methods, theoretical framework, and applications

Introduction Part I: Preliminaries Linear Algebra and Preliminaries Disctrete-time Linear Systems Stochastic Processes Kalman Filter Part II: Realization Theory Realization of Deterministic Problems Stochastic Realization Theory I Stochastic Realization Theory II Part III: Subspace Identification Subspace Identification I: ORT Subspace Identification II: CCA Identification of Closed-loop System Appendices Least-squares Method Input Signals for System Identification Overlapping Parametrization Matlab(R) Programs Solutions to Problems

Subspace methods for system identification

Ieee transactions on control systems technology

In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cutting-edge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic.

/pdf/dc-microgrid-planning-operation-and-control-a-comprehensive-1jfwsuqahw.pdf

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

This paper investigates on the development and implementation of a high integrity navigation system based on the combined use of the Global Positioning System (GPS) and an inertial measurement unit (IMU) for land vehicle applications. The complementary properties of the GPS and the INS have motivated several works dealing with their fusion by using a Kalman Filter. The conventional kalman filter has a fix error covariance matrix in all times of processing. Multi-sensor based navigation system that is implemented in this paper is called data synchronization. Also, multi-rate operations that are compared with conventional Kalman filtering has fix error covariance matrix. Therefore, when GPS outage occurred we have improper treat by kalman filter. In this paper we present an Adaptive method instead of conventional methods. It is shown that proposed method has a better performance rather than conventional method. Experimental results show the effectiveness of the GPS/INS integrated system.

Adaptive Kalman filtering based navigation: An IMU/GPS integration approach

In this paper, trajectory tracking for a quadrotor unmanned aerial vehicle (UAV) is considered in the presence of a cable-suspended payload and wind as unknown disturbances. It is assumed that the ...

Trajectory tracking for quadrotor UAV transporting cable-suspended payload in wind presence

In this paper, a robust H2/H∞ control with regional Pole-Placement is considered for tool position control of a nonlinear uncertain flexible robot manipulator. The uncertain nonlinear system is first approximated by Takagi and Sugeno’s (T–S) fuzzy model. To achieve a better tracking, an extra state (error of tracking) is then augmented to the T–S model. Based on each local linear subsystem with augmented state, a regional pole-placement state feedback H2/H∞ controller is properly designed via linear matrix inequality (LMI) approach. Parallel Distributed Compensation (PDC) is also used to establish the whole controller for the overall system and the total linear system is obtained by using the weighted sum of the local linear systems. A fuzzy weighted online computation (FWOC) component is employed to update fuzzy weights in real time for different operating points of the system. Simulation results are presented to validate the effectiveness of the proposed controller like robustness and good load disturbance attenuation and accurate tracking, even in the presence of parameter variations and also load disturbances on the motor and the tool. The superiority of the proposed control scheme is finally highlighted in comparison with the Quantitative feedback theory (QFT) controller, the QFT controller of order 13, a polynomial controller and the so-called linear sliding-mode controller methods.

/pdf/tool-position-tracking-control-of-a-nonlinear-uncertain-18a46z28mo.pdf

Tool position tracking control of a nonlinear uncertain flexible robot manipulator by using robust H 2 /H ∞ controller via T–S fuzzy model

The aim of this research is to solve the Hamilton-Jacobi-Bellman equation (HJB) arising in nonlinear optimal problem using Adomian decomposition method. First Riccati equation with matrix variable coefficients, arising in linear optimal and robust control approach, is considered. By using the Adomian method, we consider an analytical approximation of the solution of nonlinear differential Riccati equation. An application in optimal control is presented. The solution in different order of approximations and different methods of approximation will be compared with respect to accuracy. Then the HJB equation, obtained in nonlinear optimal approach, is considered and an analytical approximation of the solution of it, using Adomian method, is presented.

Solving the Hamilton-Jacobi-Bellman equation using Adomian decomposition method

In this paper a robust H2/H∞ multi-objective state-feedback controller and tracking design are presented for a class of multiple input/multiple output nonlinear uncertain systems First, some state

Ahmad Fakharian

Papers

Adaptive Kalman filtering based navigation: An IMU/GPS integration approach

Trajectory tracking for quadrotor UAV transporting cable-suspended payload in wind presence

Tool position tracking control of a nonlinear uncertain flexible robot manipulator by using robust H 2 /H ∞ controller via T–S fuzzy model

Solving the Hamilton-Jacobi-Bellman equation using Adomian decomposition method

Robust multi-objective H2/H∞ tracking control based on the Takagi–Sugeno fuzzy model for a class of nonlinear uncertain drive systems