Controller Gain Parameters Research Articles

Robust finite-time PID control for discrete-time large-scale interconnected uncertain system with discrete-delay

In this paper, we investigate the decentralized proportional–integral–derivative (PID) output-feedback control problem for a class of discrete-time uncertain large-scale systems with delayed interconnections. The robust finite time stabilization of the addressed uncertain large-scale system is attained via the feedback control with the prescribed extended passive performance index. Specifically, by using an appropriate Lyapunov–Krasovskii functional, a new set of sufficient conditions is derived on the basis of linear matrix inequality which ensures the finite-time boundedness of the augmented closed-loop uncertain large-scale systems. Furthermore, cone complementarity linearization (CCL) algorithm is developed to obtain the control gain parameters. Finally, a numerical example is provided to illustrate the effectiveness of proposed control design.

A semi-Markovian jumping system approach to secure DPC of nonlinear networked unmanned marine vehicle systems with DoS attack

In this paper, the problem of secure dynamic positioning control (DPC) for a network-based T-S fuzzy unmanned marine vehicle (UMV) systems is addressed. Firstly, a Takagi–Sugeno (T-S) fuzzy system model is adopted to model the nonlinear dynamics of UMV systems. Then, a semi-Markovian jumping system approach is introduced to describe the unknown DoS attack phenomenon. In order to reduce the communication burden, an event-based communication scheme is proposed. By means of the Lyapunov stability analysis method and semi-Markovian jumping system approach, the sufficient conditions are devised such that the closed-loop dynamic positioning system (DPS) is robust stochastically stable. Furthermore, by solving a few LMIs, the controller gain parameters can be derived. Finally, an illustrative example of networked T-S fuzzy UMV system is established to verify the proposed control algorithm.

Dynamics and active control of chemical oscillations modeled as a forced generalized Rayleigh oscillator with asymmetric potential

This paper focuses on the dynamics and active control of chemical oscillations governed by a forced generalized Rayleigh oscillator. The Melnikov method is used to analytically determine the critical parameters for the onset of chaotic motions. The analytical results are confirmed by numerical simulations. The bifurcation structures obtained show that the model displays a rich variety of dynamical behaviors and remarkable routes to chaos. The effects of the control gain parameters on the behavior of the system are analyzed and the results obtained have shown the control efficiency.

DSTATCOM implementation using Reduced Switch Single DC Source Cascaded H-bridge Multilevel Inverter

This paper describes the operation of reduced-switch single DC source cascaded H-bridge multilevel inverter (RSDCHBMLI). Compared to conventional multilevel inverters, RSDCHBMLI offers a number of advantages such as lower component count, inherent isolation and requirement of single DC source. The paper proposes application of RSDCHBMLI as DSTATCOM in a weak distribution system. The usage of single DC-link capacitor in this topology eliminates the problem of capacitor voltage balancing in DSTATCOM operation. The DSTATCOM is operated in current controlled mode (CCM) using Finite control set-Model predictive control (FCS-MPC). FCS-MPC is successful in independently performing load compensation in presence of feeder impedance along with maintaining sinusoidal voltages at the point of common coupling (PCC), without the need of any modulator or supplementary control techniques. This paper also explores different control schemes used for maintaining the DC-bus voltage. A current based proportional-integral (PI) DC-bus voltage controller is implemented in this paper, which allows easier calculation of the controller gain parameters. The performance of the current based controller is also compared with conventional power based DC-bus controllers in terms of steady state error and settling time. The results are presented for a 11-kV distribution system with RSDCHBMLI based DSTATCOM simulated in PSCAD/EMTDC environment.

Deep neural network based MPPT algorithm and PR controller based SMO for grid connected PV system

ABSTRACT In a grid connected photovoltaic (PV) system, the maximum power will be tracked by the conventional Perturb and Observe (P&O) algorithm. It generally produces high oscillations around maximum power point, and it fails to attain extreme power under prompt environmental conditions. To overcome these issues, this work proposes a deep neural network (DNN) based Maximum Power Point tracking (MPPT) algorithm and PR controller-based SMO for PV grid-connected system. The DNN consists of many algorithms in that here we use RNN in MPPT algorithm for tracking the maximum power. The Recurrent Neural Network (RNN) hidden layer has more number of layers. The deep network weight function is optimised by meta-heuristic-based Bald Eagle Search (BES) Optimisation. The proposed tracked RNN based Maximum Power Point (MPP) is passed through the DC/DC buck-boost converter. The inverter is tuned by Proportional Resonant (PR) controller, and it has two gain parameters like a proportional and integral gain. The PR controller gain parameters are tuned by the global optimisation algorithm of Spider Monkey Optimisation (SMO) to achieve better performance.

Probabilistic-constrained reliable [formula omitted] tracking control for a class of stochastic nonlinear systems: An outlier-resistant event-triggered scheme

In this study, the probabilistic-constrained outlier-resistant tracking control problem is investigated for a class of stochastic nonlinear systems subject to measurement outliers, randomly occurring faults and stochastic nonlinearities. The measurement outliers (e.g. the abnormal values of sensor measurements), which are mainly caused by sensor outages, faults or some unpredictable sudden but sharp disturbances, would inevitably bring certain negative effects on control performance if not tackled appropriately. Different from the commonly used saturation-function-based method, new type of event-trigger-based outlier-resistant communication scheme is proposed in this paper. Based on the proposed scheme, not only those “unnecessary” measurements are not permitted for transmission, but also the measurement outliers are discarded. The main purpose of the current study is to design a novel tracking control scheme such that both the H∞ control performance and specific probabilistic constraint (i.e., the probability of the tracking error falling in a prescribed set is larger than a preset value) are satisfied simultaneously. Furthermore, a finite-horizon probabilistic-constrained H∞ tracking control algorithm is developed to co-design the controller gain matrices and parameters of the event-triggered communication scheme. Finally, one example is utilized to illustrate the efficiency of the theoretical results.

A deep asynchronous actor‐critic learning‐based event‐triggered decentralized load frequency control of power systems with communication delays

AbstractThis article proposes a novel asynchronous advantage actor‐critic (A3C) learning‐based dynamic event‐triggered mechanism for the decentralized load frequency regulation to alleviate the local‐area communication burden and influence of the load fluctuations. The proposed dynamic event‐triggered mechanism applies the A3C algorithm to optimally adjust the threshold of the event‐triggered function in real time. In the A3C algorithm framework, the long short‐term memory (LSTM) network is used to estimate the policy function and value function. First, for each control area, a novel model of the decentralized load frequency control (LFC) system is established to design the event‐triggered communication mechanism and deal with the communication delay simultaneously. Then, based on the Lyapunov stability theory, the controller gain parameters of the decentralized LFC system and the margins of the even‐triggering thresholds are derived by solving a series of linear matrix inequalities (LMIs). Finally, a three‐area and four‐area power systems are used to evaluate the proposed decentralized LFC method. Simulation results show that the proposed method can greatly reduce the data transmission times and preserve a satisfactory system performance.

Study and analysis of voltage source converter control stability for HVDC system using different control techniques

A Stable and highly reliable DC link voltage represents an important factor for efficient power transfer in high voltage direct current (HVDC) networks. In this framework, this paper investigates the control and stability analysis of voltage source converter (VSC) for DC link voltage regulation. To separately achieve the independent active and reactive power control, the system voltages and currents are represented in the synchronous reference frame. In order to optimally design the parameters of proportional-integral (PI) controller, the inner and outer loops’ transfer functions are thoroughly derived/developed. In addition,/moreover, in order to attain satisfactory/certain system performance, modulus optimum, symmetrical optimum pole placement control approaches are studied and implemented for the purpose of tuning the voltage/current controller gain parameters. In particular for the symmetrical optimum control method, the gain parameters of the DC-bus voltage are determined under different values of network impedance. Furthermore, the impact/influence of changing gain parameters on the DC-link voltage and poles/zeros movement are investigated. MATLAB/Simulink model is built and simulation studies are carried out to verify the introduced concepts.

Distributed robust secondary voltage control for islanded microgrid with nonuniform time delays

This paper proposes a nonuniform delay-dependent robust secondary voltage control strategy with a finite-time voltage reference observer for an islanded microgrid. A discrete-time consensus algorithm is introduced to track the output voltage. By model transformation, a closed-loop microgrid control system is obtained. And then, the corresponding global stability and robust performance analysis theorems are derived. After implementing a series of trial studies, the impacts of secondary voltage control gains, sampling time on energy-to-peak robust performance are obtained. And the feasible optimal voltage control gains can be derived. Besides, a distributed cooperative secondary frequency control is introduced. Simulations of a four inverter-based microgrid test system are presented to verify the control performance. The simulation results indicate that our proposed method can maintain accurate voltage/frequency restoration and active power sharing. In addition, the voltage control gain parameters, arbitrary nonuniform time delays, and especially sampling time effects on the secondary voltage control performance are also verified.

Novel fuzzy-assisted nonlinear optimal power generation method for grid-connected SPV system with TLBO optimisation

This research article presents a novel fuzzy-assisted nonlinear proportional-integral (f-NPI) controller based optimal power generation method for a 100 kW grid-connected solar photovoltaic (SPV) system with boost converter topology. The primary objective is to optimise power generation when variations in irradiance and temperature are experienced. f-PI and novel f-NPI based PV array reference current predictor is implemented to adjust the duty cycle for the converter. The gain parameters of controllers are being fairly tuned using teaching-learning based optimisation (TLBO) technique. A comprehensive simulation analysis is carried out using MATLAB R2017a, which verifies that the utilisation of primitive parameters, i.e., irradiance and temperature for the proposed controller exhibits enhanced performance in comparison to extant P&O and fuzzy logic controller (using secondary/conventional parameters like voltage and current) in terms of settling time, efficiency and THD. Moreover, the operation of the novel f-NPI based method is found to comply with IEEE 929 standard.

Novel fuzzy-assisted nonlinear optimal power generation method for grid-connected SPV system with TLBO optimisation

This research article presents a novel fuzzy-assisted nonlinear proportional-integral (f-NPI) controller based optimal power generation method for a 100 kW grid-connected solar photovoltaic (SPV) system with boost converter topology. The primary objective is to optimise power generation when variations in irradiance and temperature are experienced. f-PI and novel f-NPI based PV array reference current predictor is implemented to adjust the duty cycle for the converter. The gain parameters of controllers are being fairly tuned using teaching-learning based optimisation (TLBO) technique. A comprehensive simulation analysis is carried out using MATLAB R2017a, which verifies that the utilisation of primitive parameters, i.e., irradiance and temperature for the proposed controller exhibits enhanced performance in comparison to extant P&O and fuzzy logic controller (using secondary/conventional parameters like voltage and current) in terms of settling time, efficiency and THD. Moreover, the operation of the novel f-NPI based method is found to comply with IEEE 929 standard.

A Robust Control Approach to Event-Triggered Networked Control Systems With Time-Varying Delays

A robust control approach is proposed to solve the asymptotic stabilization problem of networked control systems, where the data transmission is mediated by a triggering function, and is subject to time-varying transmission delays. We show that the triggering mechanism and the delay effects could be regarded as uncertain components interconnected with a stable linear time-invariant system. We then use two operators to describe the uncertainties, and find the corresponding integral quadratic constraints of the operators. The stability of the networked control system is analyzed from a robust control point of view. Beyond the stability analysis, a co-design algorithm is devised to design the control gain and detection parameters jointly to maximize the inter-event interval as well as the maximum allowable time-delay. Theoretically, the proposed scheme guarantees that out-of-order packet delivery does not happen. The robust control framework allows the algorithm developed for time-varying delays to be applied to the constant delay and the delay free cases. Finally, the theoretical results are demonstrated and verified by numerical simulations.

Hybrid Control Synthesis for Turing Instability and Hopf Bifurcation of Marine Planktonic Ecosystems With Diffusion

Great progress has been made in bifurcation control of systems described by ordinary differential equations. However, the control of Hopf bifurcation and Turing patterns is seldom reported in reaction-diffusion systems, which is formed by partial differential equations. In this paper, a hybrid control synthesis combining state feedback is firstly devised in the reaction-diffusion marine planktonic ecosystem. The Turing instability condition and Hopf bifurcation criterion are derived through carrying out the eigenvalue analysis of the controlled system. The numerical simulations show that the hybrid control strategy can not only suppress the formation of Turing patterns, but also delay or advance the Hopf bifurcation point. Therefore, the desired spatial dynamics behaviors can be generated by manipulate the control gain parameters, so as to achieve the purpose of maintaining the marine ecological balance.

Multi-Objective Digital PID Controller Design in Parameter Space and its Application to Automated Path Following

This paper presents a multi-objective digital PID controller design method using the parameter space approach of robust control. Absolute stability is treated first by finding the digital PID controller gain parameter space corresponding to closed loop poles being inside the unit circle. Additionally, phase margin, gain margin and a mixed sensitivity bound are treated as frequency domain constraints. Determination of digital PID controller parameter space regions satisfying these constraints is presented. All of these regions are superimposed to obtain a multi-objective digital PID controller gain parameter space solution region. The path following controller design of an automated driving vehicle is used as an example to illustrate the method. This multi-objective parameter space design approach can be extended to other digital controller forms also.

A Self-Tuning Congestion Tracking Control for TCP/AQM Network for Single and Multiple Bottleneck Topology

In this work a self-tuning rate and queue based proportional and integral controller called SRQ-PI is proposed to efficiently control the queue length with small overshoot and faster settling time. SRQ-PI proposes a new control tracking function that maps level of congestion to the packet drop probability dynamically. In SRQ-PI, the incoming traffic rate is estimated and used with the proportional and integral controller. The SRQ-PI tunes itself and stabilizes the system with internal feedback without requiring any external feedback. Furthermore, the stability of the SRQ-PI is analyzed using control theory and presents systematic guidelines to select the control gain parameters. NS2 is used to carry out the simulation work. The simulation result demonstrates that SRQ-PI is stable and gets faster transient response due to lower average delay jitter and robust against dynamic network parameters. The SRQ-PI outperforms proportional integral (PI), Intelligent adaptive PI (IAPI) and Random exponential marking (REM) algorithm.

A Novel Hybridized Fuzzy PI-LADRC Based Improved Frequency Regulation for Restructured Power System Integrating Renewable Energy and Electric Vehicles

In this article, an Improved Frequency Regulation (IFR) for an interconnected hybrid power system under a deregulated scenario is proposed. The analyzed test system comprises of a thermal power system, biogas plant, and Distributed Generation (DG). The impact of the integration of Renewable Energy Sources (RES) has been examined by considering DG system with solar and wind power generation. In order to make the system more realistic, appropriate non-linearities are incorporated into the thermal and biogas system. Electric Vehicle (EV) is also employed to take care of some portion of uncontracted demand. A maiden hybridized fuzzy-Proportional Integral (FPI)-Linear Active Disturbance Rejection Control (LADRC) is proposed and successfully implemented for IFR. Also, a new Quasi-Opposition-based Artificial Electric Field Algorithm (QO-AEFA) is proposed to acquire the optimal controller gain parameters of the tested system. A pertinent performance analysis is also examined for other employed algorithm in this study, to figure out the eminence of the proposed control algorithm. A comprehensive examination and comparison of the proposed controller with other controllers shows its effectiveness for the proposed test system. In order to ensure the reliability of the proposed controller, sensitivity analysis is also carried out for system parameters that reveal its robust nature. Further, case studies for random load variations and comparison with the works in previous literature also manifest the puissance of the current research work. Moreover, the improved results under random weather conditions illustrate the effectiveness of the proposed IFR under deregulation.

Periodic Response and Stability of a Maglev System with Delayed Feedback Control Under Aerodynamic Lift

In this research, the periodic response and stability of a nonlinear maglev system under the combined effects of steady and unsteady aerodynamic lifts is investigated, considering time delay in the feedback control loop. First, a nonlinear maglev system with a single levitation point that accounts for the nonlinearity of the electromagnetic force, time delay in the feedback control loop, and effect of aerodynamic lift is established. Then the periodic solutions of the maglev system with aerodynamic lift and time delays are obtained by an incremental harmonic balance analysis, in which the explicit time-delay action matrices used indicate that the effect of time delay on the response of the maglev system is periodic. The stability of the periodic solutions based on a finite difference continuous time approximation method and Floquet theory is studied, from which the critical time delay is obtained. Also, the relationship between the periodic vibration amplitude and the time delay is examined, along with the steady aerodynamic lift coefficient, and frequency of the unsteady aerodynamic lift, as well as the variation of critical delay with respect to the position feedback and velocity feedback with the control gain parameters. In addition, the stability boundary for the simultaneous time-delayed position and velocity feedback is obtained.

Unified Real Power Sharing of Generator and Storage in Islanded Microgrid via Distributed Dynamic Event-Triggered Control

The distributed generators (DGs) and energy storages systems (ESSs) have different droop characteristics for power sharing in autonomous microgrids (MGs). In previous research, the distributed secondary control is proposed for only one specific type of units, and relies on periodic communications among neighbors. In this paper, a unified distributed real power sharing control scheme based on the dynamic event-triggered mechanism is proposed for droop-governed DGs and ESSs. A unified power sharing strategy is proposed where the power are fairly shared among all DGs and ESSs considering their different power ratings and capacities, while state-of-charges among ESSs are balanced. In the meantime, a distributed event-triggered control scheme with a novel dynamic triggering mechanism is designed to reduce the communications among controllers. Besides, the proposed event-triggered mechanism is sample-based and only uses the largest and the second smallest eigenvalues of the Laplacian matrix to design the controller gain and event-triggered parameters. To validate the proposed control design, a MG test system with detailed component models is built in the OPAL-RT real-time simulator. The results demonstrate the effectiveness and performance of the proposed controller.

Neural Network-Based Nonlinear Fixed-Time Adaptive Practical Tracking Control for Quadrotor Unmanned Aerial Vehicles

This brief addresses the position and attitude tracking fixed-time practical control for quadrotor unmanned aerial vehicles (UAVs) subject to nonlinear dynamics. First, by combining the radial basis function neural networks (NNs) with virtual parameter estimating algorithms, a NN adaptive control scheme is developed for UAVs. Then, a fixed-time adaptive law is proposed for neural networks to achieve fixed-time stability, and convergence time is dependent only on control gain parameters. Based on Lyapunov analyses and fixed-time stability theory, it is proved that the fixed-time adaptive neural network control is finite-time stable and convergence time is dependent with control parameters without initial conditions. The effectiveness of the NN fixed-time control is given through a simulation of the UAV system.

Decentralized Adaptive Fault-Tolerant Control for a Class of Strong Interconnected Nonlinear Systems via Graph Theory

This article addresses the decentralized tracking control problem for a class of strong interconnected nonlinear systems with actuator faults. The considered interconnections are allowed to be dominated by some bounding functions, which are linear growth in the status of all subsystems. First, an adaptive high-gain technique is introduced to deal with the unknown strong interconnections. Then, a group of fault-tolerant controllers is designed to adaptively compensate for the effects of the actuator failures, in which the controller gain parameters are adjusted online only according to local available information. Furthermore, with the aid of an algebraic graph theory result, it is proved that all signals of the closed-loop system are globally uniformly bounded, and the tracking errors of all subsystems converge to zero asymptotically. The effectiveness of the proposed control algorithm is demonstrated by a numerical simulation.