Robust multi-input multi-output integral-proportional controller for interconnected microgrid operation

This paper proposes a modeling and controller design approach for a hybrid wind power generation system that considers a fixed wind-turbine and a dump load. Since operating conditions are kept changing, it is challenge to design a control for reliable operation of the overall system. To consider variable operating conditions, Takagi-Sugeno (TS) fuzzy model is taken into account to represent time-varying system by expressing the local dynamics of a nonlinear system through sub-systems, partitioned by linguistic rules. Also, each fuzzy model has uncertainty. Thus, in this paper, a modern nonlinear control design technique, the sliding mode nonlinear control design, is utilized for robust control mechanism. In the simulation study, the proposed controller is compared with a proportional-integral (PI) controller. Simulation results show that the proposed controller is more effective against disturbances caused by wind speed and load variation than the PI controller, and thus it contributes to a better quality wind-hybrid power generation system.

Robust Model-Following control of a three-level Neutral Point Clamped shunt active filter in the medium voltage range

Parallel robots are nowadays used in many high-precision tasks. The dynamics of parallel robots is naturally more complex than the dynamics of serial robots, due to their kinematic structure composed by closed chains. In addition, their current high-precision applications demand the innovation of more effective and robust motion controllers. This has motivated researchers to propose novel and more robust controllers that can perform the motion control tasks of these manipulators. In this article, a two-loop proportional–proportional integral controller for trajectory tracking control of parallel robots is proposed. In the proposed scheme, the gains of the proportional integral control loop are constant, while the gains of the proportional control loop are online tuned by a novel self-organizing fuzzy algorithm. This algorithm generates a performance index of the overall controller based on the past and the current tracking error. Such a performance index is then used to modify some parameters of fuzzy membership functions, which are part of a fuzzy inference engine. This fuzzy engine receives, in turn, the tracking error as input and produces an increment (positive or negative) to the current gain. The stability analysis of the closed-loop system of the proposed controller applied to the model of a parallel manipulator is carried on, which results in the uniform ultimate boundedness of the solutions of the closed-loop system. Moreover, the stability analysis developed for proportional–proportional integral variable gains schemes is valid not only when using a self-organizing fuzzy algorithm for gain-tuning but also with other gain-tuning algorithms, only providing that the produced gains meet the criterion for boundedness of the solutions. Furthermore, the superior performance of the proposed controller is validated by numerical simulations of its application to the model of a planar three-degree-of-freedom parallel robot. The results of numerical simulations of a proportional integral derivative controller and a fuzzy-tuned proportional derivative controller applied to the model of the robot are also obtained for comparison purposes.

Regulations on environmental effects due to such issues as nitrogen oxide (NOx) and carbon monoxide (CO) emissions from thermal power plants have become stricter[1]; hence the need for compliance with these regulations has been increasing. To meet this need, several technologies with respect to fuel combustion, exhaust gas treatment and operational control have been developed[2-4]. The technologies for the fuel combustion and the exhaust gas treatment include a low NOx burner and an air quality control system, and they are capable of reducing impact on the environment as physical and chemical implementation methods. The operational control technology for the thermal power plants is constantly required to receive changes in operational conditions. It is difficult to realize operational control which responds to combustion properties. To overcome this issue, the operational control must be able to reduce NOx and CO emissions flexibly in accordance with such changes. Robustness is also required in such control because the measured NOx and CO data often include noise. Therefore, a robust and flexible plant control system is strongly desired to reduce environmental effects from thermal power plants efficiently. Several studies have proposed plant control technologies to reduce the environmental effects[4-10]. These technologies are classified into two types of methods: model based and non-model based methods. The former methods include an optimization algorithm and a numerical model to estimate plant properties using neural networks (NNs)[11,12] and multivariable model predictive control[13]. The optimization algorithm searches for optimal control signals to reduce NOx and CO emissions using the numerical model. The latter methods have no models and they generates the optimal control signals by fuzzy logic[14]. A fuzzy logic controller outputs the optimal control signals for multivariable inputs using fuzzy rule bases. The fuzzy rule bases are based on a priori knowledge of plant control, and they can be tuned by parameters. These technologies require the measured plant data for initial tuning of the model properties and the parameters of rules when the technologies are installed in plants. It usually takes some time to collect enough plant data. In addition, the search for control

The paper considers a train operation which minimizes energy consumption for leading the train from one station to the next station at the specified instant and stopping times. The minimization of the energy consumption is defined as an optimal trajectory used as a reference signal for the robust optimal control to regulate an AGT train in Automatic People Movers (APM) system. In this case, we consider the optimal control system where the performance index is of general form containing a final (terminal) cost function in addition to the integral cost function. Since the final time and the final state are fixed or specified, there are no extra boundary conditions to be used other than those given in the problem formulation. Full state feedback control law is selected to design Linear Quadratic Regulator (LQR) and PID controller. Here we also consider the parameter selection problems of the PID controller within the framework of robust control theory which provides an alternative method to optimize the setting of the PID controller. To handle uncertainty of the train parameter such as mass, robust design is applied to find a controller, for a given system, such that the closed loop system is robust. By considering the uncertain passenger arrival flow, the robust optimal control law is designed to suppress the effect of this uncertainty and the uncertain disturbances to the nominal mass of the train system. Simulations show the robustness of the proposed train control algorithms against disturbances occurring on a train parameter.

This paper presents the development of an airport bipolar DC microgrid and its interconnected operations with the utility grid, electric vehicle (EV), and more electric aircraft (MEA). The microgrid DC-bus voltage is established by the main sources, photovoltaic (PV) and fuel cell (FC), via unidirectional three-level (3L) boost converters. The proposed one-cycle control (OCC)-based current control scheme and quantitative and robust voltage control scheme are proposed to yield satisfactory responses. Moreover, the PV maximum power point tracking (MPPT) with FC energy-supporting approach is developed to have improved renewable energy extraction characteristics. The equipped hybrid energy storage system (HESS) consists of an energy-type battery and a power-type flywheel; each device is interfaced to the common DC bus via its own 3L bidirectional interface converter. The energy-coordinated operation is achieved by the proposed droop control. A dump load leg is added to avoid overvoltage due to an energy surplus. The grid-connected energy complementary operation is conducted using a neutral point clamped (NPC) 3L three-phase inverter. In addition to the energy support from grid-to-microgrid (G2M), the reverse mcrogrid-to-grid (M2G) operation is also conductible. Moreover, microgrid-to-vehicle (M2V) and vehicle-to-microgrid (V2M) bidirectional operations can also be applicable. The droop control is also applied to perform these interconnected operations. For the grounded aircraft, bidirectional microgrid-to-aircraft (M2A)/aircraft-to-microgrid (A2M) operations can be performed. The aircraft ground power unit (GPU) function can be preserved by the developed microgrid. The MEA on-board facilities can be powered by the microgrid, including the 115 V/400 Hz AC bus, the 270 V DC bus, the switched-reluctance motor (SRM) drive, etc.

Approach to robust multivariable combustion control design

This paper presents a robust control technique for three-phase chargers under unbalanced grid conditions. The control method consists of inner-loop robust grid-current control and outer-loop proportional integral control for constant current (CC) and constant voltage (CV) control. A dual-current control for the inner-loop positive and negative sequence is employed to eliminate the unbalanced current caused by the grid so that a constant current and voltage can be provided to the batteries. The inner-loop robust controllers utilize state feedback with integral action in the dq-synchronous frame. A linear matrix inequality-based optimization scheme is used to determine stabilizing gains of the controllers to maximize the convergence rate to steady state in the presence of uncertainties. The uncertainties of the system are described as the potential variation range of the inductance and resistance in the L-filter.

The paper presents a dq-model used to analyze a brushless doubly fed induction machines’ closed loop control-ability. A simplified vector control algorithm is proposed by reducing the controllers dependence on cross compensation in the power-winding current control loop. Simulink is used to show the viability of the controller in operation. It is found that this simplification provides a robust and responsive controller with good response times and acceptable disturbance rejection. Finally simulation results are used to show the models’ response to variations in speed, reactive power and torque whilst operating in motoring mode.

The paper presents a robust adaptive speed controller for AC drives. A minimum control synthesis (MCS) algorithm based on model reference adaptive control (MRAC) is employed in the robust controller. This MCS controller requires no prior knowledge of the drive dynamic parameters and is guaranteed to provide global asymptotic stability of the closed loop system. The paper describes many practical aspects of the implementation, such as adaptive gain analysis, adaptive rate selection, the gain variation limits, gain wind-up prevention measure and initial values. The MCS controller has been successfully implemented and outperforms the PI counterpart with respect to speed overshoot in dynamic tracking, torque response and speed recovery when subject to sudden load torque disturbance. The quantitative comparison of various experimental test results for both MCS and PI controllers are shown as support proof. This new MCS controller has considerable potential for industrial applications as is demonstrated in the paper.

Design of a Robust Fractional Order Controller for Burning Zone Temperature Control in an Industrial Cement Rotary Kiln

In this paper, several design methods are proposed to deal with the constant turning force feedback control system against the nonlinear time-varying gain perturbation in the cutting process. The proposed methods include minimum weighted sensitivity robust control, robust-linear quadratic sub-optimal PI control and robust linear quadratic optimal control. The simulation results show that these design methods can achieve satisfactory system stability and performance and achieve superior performance responses to the classical PI control.

The design of a robust controller for the deaerator of the Experimental Breeder Reactor-II (EBR-II) that uses the linear quadratic Gaussian with loop transfer recovery (LQG/LTR) procedure is described. At present, classical proportional-integral (PI) controllers are used to control the deaerator. When the operating condition changes, the system is disturbed, or a fault occurs, and the PI controllers may fail to maintain the desired performance. A robust controller that can accommodate system faults and obtain a reasonable behavior for a wide range of model uncertainty was designed. The controller provides the desired performance despite a considerable change in the operating condition, accommodates some of the failures that can occur, and provides the choice of penalizing one variable over another. The design is tested for robustness by varying the system operating conditions and simulating a steam valve failure. The set of nonlinear simulations using the modular modeling system and the advanced continuous simulation language is included.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Sliding Mode – PI controller is recognized as robust controller with a high stability in a wide range of operating conditions, although it suffers from chattering problem. In addition, it cannot be directly applied to multi switches power converters. This paper concerns a sliding controller design with the proportional-integral (PI) control form. First, a theoretical analysis of the considered sliding PI controller is provided. Our analysis reveals that the proportional control term of the sliding PI controller, i.e., sliding proportional controller, can reduce the chattering problem and thus, the robustness can be established. Second, the chattering problem is eliminated by smoothing the control law in a narrow boundary layer, and a pulse width modulator produces the fixed frequency switching law for the inverter. The smoothing procedure is based on limitation of pulse width modulator. Therefore, the chattering problem of the proportional gain is resolved and the valuable robust control property of the sliding integral controller is illustrated again. The simulation model has been developed and tested using MATLAB software.

Efficient adaptive controllers and recursive filters for centralized control scheme of islanded microgrid with linear and non-linear loads