Controller Gain Parameters Research Articles

Complex dynamic behaviors of a congestion control system under a novel PD1n control law: Stability, bifurcation and periodic oscillations

Abstract In this paper, we consider the control of nonlinear dynamic in a congestion control system. A novel fractional-order proportional-derivative (PD) feedback law is firstly designed to control the Hopf bifurcation caused by the system. The proposed P D 1 n controller has the different order with the original congestion system. Meanwhile, the communication delay is elected as the bifurcation parameter to study the stability, bifurcations and periodic oscillations of the controlled congestion system. By the stability mechanism of fractional-order systems, we can obtain the criteria for satisfying the stability and Hopf bifurcation. It has been discovered that the Hopf bifurcation point can be delayed or advanced under the adjustment of appropriate control gain parameters and the order. Therefore, the congestion system becomes controllable and the desirable behaviors can be realized. Finally, numerical simulations are presented to demonstrate the validity of the main results and the efficiency of the control strategy in the designed fractional-order PD controller.

Output feedback robust H∞ control for spacecraft rendezvous system subject to input saturation: A gain scheduled approach

In this paper, the problem of output feedback robust H∞ control for spacecraft rendezvous system with parameter uncertainties, disturbances and input saturation is investigated. Firstly, a full-order state observer is designed to reconstruct the full state information, whose gain matrix can be obtained by solving the linear matrix inequality (LMI). Subsequently, by combining the parametric Riccati equation approach and gain scheduled technique, an observer-based robust output feedback gain scheduled control scheme is proposed, which can make full use of the limited control capacity and improve the control performance by scheduling the control gain parameter increasingly. Rigorous stability analyses are shown that the designed discrete gain scheduled controller has faster convergence performance and better robustness than static gain controller. Finally, the performance and advantage of the proposed gain scheduled control scheme are demonstrated by numerical simulation.

Optimal control strategies for a hybrid renewable energy system: an ALANN/RNN technique

In this paper, optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the parallel execution of both the ant lion optimization with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. Here, the HRES is made out of photovoltaic, wind turbine, fuel cell, and battery which is associated with the DC link and can adjust the real and reactive power. The proposed ALANN/RNN technique predicts the required control gain parameters of the HRES to maintain the power flow based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy accessibility and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. In the HRES unit, the power flow management of grid is accomplished while controlling the PI controller for producing the optimal control pulses of the DC/DC converter. The proposed method is actualized in MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques such as SPC, GA, PSO and BAT technique. The comparison results demonstrate that the occurrence of proposed approach confirms its ability for controlling the power flow in the HRES system. An optimal control strategy for the power flow management of the grid-connected hybrid renewable energy system (HRES) is proposed. The proposed control strategy is the joined execution of both the ant lion optimization (ALO) with artificial neural network (ANN) and recurrent neural network (RNN), and hence it is named as ALANN/RNN. ALO is utilized to minimizing the power variations of the HRES units. ANN is used for real power prediction, and RNN is utilized for reactive power prediction.

Adaptive Control Strategy for Ultracapacitor Based Bidirectional DC–DC Converters

Ultracapacitor (UC) based energy storage systems (ESS) are often operated as boost converters during discharging mode of operation. The control of such a system is typically performed using an outer voltage loop and inner current loop structure. Since UC stacks undergo wide voltage variation, it is desirable to have a control structure that accommodates system characteristics’ variations, which allow deep discharge of UC stack. This paper proposes an adaptive control method with reduced settling time where the control gain parameters for outer voltage loop are obtained online in a UC-based bidirectional dc–dc converter. For this, analytical expressions for controller gains for a required bandwidth (BW) and response characteristics are obtained. Using these, simplification in gain expressions that is required for adaptive implementation is achieved. In this regard, the design and drawbacks of using fixed controller gain parameters for voltage loop are presented. The proposed control incorporates the effect of right-hand plane (RHP) zero on the voltage loop design, which constraints the control BW especially during deep discharges. The adaptive control adjusts the BW of the voltage loop according to the RHP zero location, thereby ensuring best performance possible for wide range of operating UC stack voltages. Such a formulation of controller gains results in a voltage feedforward method, which improves the control performance of the ESS, with an improvement in the settling time ranging from ${\text{10}\%}$ to ${\text{50}\%}$ depending on the operating UC stack voltage level. The proposed control is also compatible with load feedforward method and is found to have better performance as compared to the case when fixed controller gains with load feedforward is utilized. As a proof of concept, the proposed control is verified on an experimental setup with power level $P_o$ = 62 W, voltage level $V_g$ = 26 V with the dc–dc converter switching at $f_{\text{sw}}$ = 100 kHz.

Approaching hybridized GWO-SCA based type-II fuzzy controller in AGC of diverse energy source multi area power system

The present article addresses robust control of both frequency and tie line power of a two area six unit diverse energy source based non linear power network. In large interconnected network, deviation in frequency and tie line power occurs due to uncertainty in load variation. In order to keep both frequency and tie line power flow to their nominal values, Automatic Generation Control (AGC) is required. AGC aims proper balance between power generation and electrical demand. In AGC concern the present article proposes a robust type-II fuzzy PID controller to diminish all local error signals (change in frequency ‘Δf’ and change in tie line power flow ‘ΔPtie’) of each control area. Controller tries to keep network frequency and interline power flow within nominal values. To justify supremacy of proposed type-II fuzzy controller a comparative study with fuzzy type-I and conventional PID controllers has been carried out for this analysis. To obtain precious gain parameters of above implemented controllers, a novel hybridized Gray Wolf Optimization and Sine Cosine algorithm (hyGWO-SCA) has been proposed for the study. In fact of this to show supremacy of proposed hyGWO-SCA technique, its performances are compared with host GWO and SCA technique. To verify the robust nature of type-II fuzzy PID controller the work goes through different sensitive analysis. Overall critical assessment of this study concludes, proposed hyGWO-SCA tuned type-II fuzzy controller exhibits superior performance in regard of AGC analysis. Finally the work is extended to four area eight unit interconnected system.

Optimal power flow control of hybrid renewable energy system with energy storage: A WOANN strategy

This paper proposed an optimal control technique for power flow control of hybrid renewable energy systems (HRESs) like a combined photovoltaic and wind turbine system with energy storage. The proposed optimal control technique is the joined execution of both the whale optimization algorithm (WOA) and the artificial neural network (ANN). Here, the ANN learning process has been enhanced by utilizing the WOA optimization process with respect to the minimum error objective function and named as WOANN. The proposed WOANN predicts the required control gain parameters of the HRES to maintain the power flow, based on the active and reactive power variation in the load side. To predict the control gain parameters, the proposed technique considers power balance constraints like renewable energy source accessibility, storage element state of charge, and load side power demand. By using the proposed technique, power flow variations between the source side and the load side and the operational cost of HRES in light of weekly and daily prediction grid electricity prices have been minimized. The proposed technique is implemented in the MATLAB/Simulink working stage, and the effectiveness is analyzed via the comparison analysis using the existing techniques.

Performance analysis of dynamic voltage restorer using improved PSO technique

ABSTRACTThis paper has discussed the study of the performance of dynamic voltage restorer (DVR) taking different voltage sag conditions in the supply voltage of the distribution system for linear and non-linear load. DVR is employed to mitigate the voltage sag. Proportional and integral (PI) controller is used for the control of DVR to mitigate the voltage sag. Synchronous reference frame theorybased control algorithm has been implemented for generating reference voltages. In this paper, an efficient improved particle swarm optimisation technique for optimizing the gain parameters of the PI controller has been proposed. The performance and suitability of DVR is validated through MATLAB simulation results and use of Sim Power Systems Blocksets. Integral squared error is implemented to check the performance and suitability of DVR. Extensive results of the proposed method are presented and compared with conventional Ziegler Nichols and genetic algorithm methods of tuning gain parameters of PI controllers.

Output‐based event‐triggered control for networked control systems: tradeoffs between resource utilisation and robustness

The additional threshold of a mixed event-triggering mechanism (ETM) makes it impossible to guarantee the requirement of asymptotic stability. As a consequence, it is necessary to investigate input-to-state practically stable (ISpS) which is a mild stability property. In this study, the authors firstly model the closed-loop event-triggered control system as a delayed linear parameter-varying (LPV) systems with a mixed ETM and parameter uncertainty. Then, a co-design condition in a sense of ISpS of controller gain and trigger parameters for the delayed LPV systems is obtained by employing the parameter-dependent Lyapunov–Krasovskii functionals and congruent transformation method. Moreover, based on the obtained co-design condition in a sense of ISpS, an optimisation algorithm for the tradeoffs between resource utilisation and robustness of the closed-loop system is further proposed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.

Robust stochastic stabilization of attitude motion

This study proposes robust stochastic stabilization of rigid body attitude motion within the framework of geometric mechanics, which can globally represent the attitude dynamics model. The system is subject to a stochastic input torque with an unknown variance parameter and an unknown nonlinear diffusion coefficient matrix. Our development starts with introducing a general notion of the stochastic stability in probability within the framework of geometric mechanics. Then, the Morse–Lyapunov (M–L) technique is employed to design a nonlinear continuous stochastic feedback control law. Finally, the asymptotic stability of the system is guaranteed in probability and the control gain parameters are obtained via solving a linear matrix inequality feasibility problem. An estimate of the region of attraction of the system is calculated to provide a better insight for tuning the control gain parameters. Two illustrative examples are performed based on the discretized model of the closed-loop system to demonstrate the effectiveness of the proposed control scheme.

Optimal design of shunt active power filter for power quality enhancement using predator-prey based firefly optimization

The nonlinear behavior of present day power electronic based equipments generates harmonics that lead to overheating, efficiency reduction and malfunctioning of the utility equipments. Shunt active power filters (SAPF), comprising of a three phase voltage source inverter (VSI) and a dc capacitor, are commonly used to eliminate the harmonics at source side. The design of these filters is very important in obtaining satisfactory performances. The classical design methods and the tuning of controller gain parameters may not yield optimal performances. This paper formulates the design of SAPF as an optimization problem with an objective of minimizing the total harmonic distortion (THD) and solves it using predator-prey based firefly optimization (PPFO). The PPFO inherits the features of predator-prey concept and firefly optimization (FO) and helps to get away from the suboptimal traps, thereby ensuring global optimal design. The paper also includes the simulation results of a few test cases representing different types of harmonic loads and illustrates the superior performances of the proposed design philosophy. • An optimal design method for shunt active power filter is proposed. • It attempts to minimize the harmonics in source current. • It employs predator-prey based firefly optimization. • The performance is exhibited with six test cases.

Connected cruise control of a car platoon: A time-domain stability analysis

A compact representation of a vehicle platoon following model considering the headway, velocity, and vehicle-to-vehicle communication delay is presented. Then, a connectivity structure is selected, which includes vehicles equipped with connected cruise control and a conventional vehicle as the leader. The platoon exponential stability and its robust stability with respect to time-varying matrix perturbations are studied. The analysis is carried out in the time domain, via Lyapunov–Krasovskii functionals depending on the delay Lyapunov matrix. The stability results are illustrated by stability charts of the plane of control gain parameters. A total of three platoon illustrative vehicle scenarios show the interest of the approach.

Design of MI fuzzy PID controller optimized by Modified Group Hunting Search algorithm for interconnected power system

This paper is validated the multi inputs (two inputs) fuzzy PID (MIFPID) controller as automatic generation control (AGC) over two disparate consolidation of single input FPID (SIFPID-1 and SIFPID-2) controller for a two area interconnected power system. The objective function is formulated by concerning undershoot, overshoot, and settling time of frequency and tie-line power deviation of the power system by implementing two different SIFPID and MIFPID controllers individually as AGC in each area. Modification of Group Hunting Search optimization (MGHS) is proposed to optimize the gain parameters of controllers to minimize the multi-objective problem with constraint. All the performances of these controllers as AGC are examined by implementing a load disturbance of 1% (0.01 p.u.) in area-1. Finally, MIFPID controller optimized by MGHS algorithm contributes better performance in the proposed system.

Event-Triggered Based Reliable Control of Vehicle Active Suspension System Under Actuator Faults

This paper addresses the problem of reliable control of a vehicle active suspension system under actuator faults.The methodology based on event-triggering scheme is employed to design the controller and the proposed results are expressed in the form of linear matrix inequalities (LMIs). The motivation for introducing event-triggering control is to reduce the amount of data transmission in the system. The problem is contrived as a co-design problem to find both controller gain and event-triggering parameters. Finally, the efficacy of the proposed results is demonstrated for the system under fault through a simulation example. Also, a comparison is drawn between the sampled-data control and the proposed event-triggered control strategy.

RQ-AQM: a rate and queue-based active queue management using feedback control theory

This work proposes a rate and queue based active queue management (RQ-AQM) algorithm to improve the stability of the network system supporting TCP flows. Classical control theory is employed to analyse the stability of TCP/RQ-AQM system and provide systematic guidelines to select control gain parameters of the RQ-AQM. The performance of the proposed algorithm is evaluated and compared with existing algorithms using network simulator-2 (NS2). Simulation results demonstrate that RQ-AQM achieves stability with faster responsive to the dynamic network conditions. The proposed algorithm obtains better performance with respect to throughput, link utilisation, average delay and achieves fastest settling time than others.

RQ-AQM: a rate and queue-based active queue management using feedback control theory

This work proposes a rate and queue based active queue management (RQ-AQM) algorithm to improve the stability of the network system supporting TCP flows. Classical control theory is employed to analyse the stability of TCP/RQ-AQM system and provide systematic guidelines to select control gain parameters of the RQ-AQM. The performance of the proposed algorithm is evaluated and compared with existing algorithms using network simulator-2 (NS2). Simulation results demonstrate that RQ-AQM achieves stability with faster responsive to the dynamic network conditions. The proposed algorithm obtains better performance with respect to throughput, link utilisation, average delay and achieves fastest settling time than others.

Fractional order disturbance observer based adaptive sliding mode hybrid projective synchronization of fractional order Newton–Leipnik chaotic system

This manuscript, presents the fractional order disturbances observer based adaptive sliding mode hybrid projective synchronization of commensurate and incommensurate fractional order Newton–Leipnik chaotic system with disturbance. Firstly, a fractional order disturbance observer (FODO) based on adaptive sliding mode control laws for a fractional order Newton–Leipnik system with unknown bounded disturbance is studied. The appropriate control gain parameters are chosen such that the disturbance observer can approximate the unknown disturbance well. Further, we design a simple sliding mode surface based on sliding mode control technique incorporating with the designed fractional order disturbance observer for developing a bounded hybrid projective synchronization control scheme. Numerical simulation results are given to demonstrate the validity and effectiveness of the proposed sliding mode control scheme in the presence of external bounded unknown disturbance. To analyse the superiority of proposed methodology we have compared our results with earlier published results as well. Moreover, as an application, the proposed scheme is then applied to a secure communication system. Simulation results verify the proposed schemes success in the communication application.

Fractional order disturbance observer based adaptive sliding mode synchronization of commensurate fractional order Genesio-Tesi system

In this manuscript, we have studied the adaptive sliding mode synchronization of commensurate fractional order Genesio-Tesi system with disturbance. Firstly, we have discussed the fractional order disturbance observer (FODO) which is based on adaptive sliding mode control laws for a commensurate fractional order Genesio-Tesi system with unknown bounded disturbance. Appropriate control gain parameters have been chosen in such a manner, so that the disturbance observer can approximate the unknown disturbance well. Further, we have designed a simple sliding mode surface based on sliding mode control technique, incorporating the designed fractional order disturbance observer for developing a bounded synchronization control scheme. Finally, the viability and the efficacy of the proposed sliding mode control scheme in the presence of external bounded unknown disturbance for fractional order Genesio-Tesi system is established by the numerical simulation results. We have observed that for different parameters and fractional orders of Genesio-Tesi system the synchronization time varies, which is also demonstrated in numerical simulations. To visualize the robustness and effectiveness of proposed methodology we have compared our results with earlier published results as well. Moreover, as an application, the proposed scheme is then applied to a secure communication scheme. Simulation results verify the proposed schemes success in the communication application.

Data-Driven Adaptive LQR for Completely Unknown LTI Systems

In this paper, a data-driven on-policy optimal control design is proposed for continuous-time linear time invariant (LTI) systems with completely unknown dynamics. An online system identifier and control gain parameter estimator, which use past and current data together with standard gradient descent update laws, facilitate the design of an adaptive optimal controller that guarantees parameter convergence without the need of persistence of excitation (PE). Unlike the classical approach of enforcing the restrictive PE condition on the regressor, the data-driven approach is verifiable online and establishes parameter convergence from information rich past stored data simultaneously with the current data. A state feedback controller is designed using a dynamic gain parameter which is shown to converge to the neighborhood of the optimal LQR gain. Semi-global uniformly ultimately bounded (UUB) stability of the overall system is established using Lyapunov-based analysis. Simulation results further validate the developed result.

Active Transient Acousto-Structural Response Control of a Smart Cavity-Coupled Circular Plate System

Abstract The transient vibroacoustic response suppression of a piezo-coupled sandwich circular plate backed by a rigid-walled cylindrical acoustic enclosure is investigated. Problem formulation is based on the linear acoustic wave theory, Kirchhoff thin plate model, fluid/structure compatibility relations, Rayleigh integra formula, and active damping control (ADC) strategy. Matlab’s Genetic Algorithm (GA) is utilized to identify and optimize the feedback controller gain parameter based on a multi-objective performance index function. Durbin’s numerical Laplace inversion scheme is then used to calculate the key acousto-structural response parameters due to a transverse impulsive shock force for selected cavity depths. Numerical simulations demonstrate satisfactory performance of adopted control methodology in effective suppression of panel displacement response and radiated external sound pressure for enclosures of shallow and moderate depths. Limiting cases are considered and accuracy of the proposed model is rigorously verified.

Hybrid BFPSO Approach for Effective Tuning of PID Controller for Load Frequency Control Application in an Interconnected Power System

Penetration of renewable energy sources makes the modern interconnected power systems to have more intelligence and flexibility in the control. Hence, it is essential to maintain the system frequency and tie-line power exchange at nominal values using Load Frequency Control (LFC) for efficient, economic and reliable operation of power systems. In this paper, intelligent tuning of the Proportional Integral Derivative (PID) controller for LFC in an interconnected power system is considered as a main objective. The chosen problem is formulated as an optimization problem and the optimal gain parameters of PID controllers are computed with three innovative swarm intelligent algorithms named Particle Swarm Optimization (PSO), Bacterial Foraging Optimization Algorithm (BFOA) and hybrid Bacterial Foraging Particle Swarm Optimization (BFPSO) and a comparative study is made between them. A new objective function designed with necessary time domain specifications using weighted sum approach is also offered in this report and compared with conventional objective functions. All the simulation results clearly reveal that, the hybrid BFPSO tuned PID controller with proposed objective function has better control performances over other optimization methodologies.