Integral Controller Research Articles

Guiding Lunar Landers: Harnessing Neural Networks for Dynamic Flight Control with Adaptive Inertia and Mass Characteristics

The autonomous control of landing procedures can provide the efficiency and precision that are vital for the successful, safe completion of space operations missions. Controlling a lander with this precision is challenging because the propellants, which will be expended during the operations, represent a significant fraction of the lander’s mass. The mass variation of each tank profoundly influences the inertia and mass characteristics as thrust is generated and complicates the precise control of the lander state. This factor is a crucial consideration in our research and methodology. The dynamics model for our lander was developed where the mass, inertia, and center of mass (COM) vary with time. A feed-forward neural network (NN) is incorporated into the dynamics to capture the time-varying inertia tensor and COM. Moreover, the propellant takes time to travel through the feed lines from the storage tanks to the engine; also, the solenoid valves require time to open and close. Therefore, there are time delays between the actuator and the engine response. To take into account these sources of variations, a combined time delay is also included in the control loop to evaluate the effect of delays by fluid and mechanisms on the performance of the controller. The time delay is estimated numerically by a Computational Fluid Dynamics (CFD) model. As part of the lander’s control mechanism, a thrust vector control (TVC) with two rotational gimbals and a reaction control system (RCS) are incorporated into the dynamics. Simple proportional, integral, and derivative (PID) controllers are designed to control the thrust, the gimbal angles of the TVC, and the torque required by the RCS to manipulate the lander’s rotation and altitude. A complex mission with several numerical examples is presented to verify the hover and rotational motion control.

A decision-making approach on control techniques for an inverted pendulum based on, neuro-fuzzy, indirect adaptive and PID controllers

The operation of an inverted pendulum and its respective type of control are affected by the change of the values of its internal parameters. Changes with high uncertainty result in responses with undesirable outputs. In this work, a comparison is presented for the control of an inverted pendulum to determine the operation and characteristics of three types of control systems: Neuro-Fuzzy Control (NFC), Indirect Adaptive Control (IAC) and a Proportional Integral Derivative control (PID). The study considers several indices such as stabilization time, rise time, mean square error, overshoots, convergence, computational load, error, mathematical requirements, and performance indices for control systems. To demonstrate its operation, the controls are implemented in hardware, one for the NFC and another for the IAC under an Arduino UNO platform. The results indicate that the NFC and IAC controls do not generate a transient or impulse response, only a small delay and the rise and stabilization time are minimal. While PID presents a transient response and overshoot, as well as a stabilization time to reach the steady state response.

Qualitative performance improvement of a hybrid power supply at the DC common coupling point using a neuro-fuzzy method

Continuity of power supply service in production industries is of crucial importance, given the severity imposed in these sectors. A poor power supply leads to underproduction or degradation of equipment sensitive to electrical variations. A direct consequence of this is loss of earnings for industry. To ensure continuity of service for industrial power supplies, at least one secondary power source must be available. However, the switching system between sources may not be spontaneous due to the presence of electromechanical switches. The solution to this problem is to use electronic converters for control. The aim of this work is to enhance the parameters of an industrial photovoltaic-grid hybrid power supply to ensure uninterrupted power supply and balance between energy supply and demand. The sources are coupled to the DC bus via electronic converters. The photovoltaic system and the inverter are assumed to be unknown, and only the load terminal voltage is accessible and measurable. To address the lack of mathematical model of the system, we propose a mechanism that combines a fuzzy logic (FLC) centred mechanism with a radial basis function (RBF) neural network for the studied case. The FLC self-determines the image of the compensation power to be extracted through the current to the secondary energy source, ensuring continuity of service and balance between supply and demand. The FLC mechanism self-adjust the parameters of the RBF neural network and coordinates the energy at the DC link. The results obtained in the Matlab/Simulink environment are compared with the fuzzy logic and proportional integral (PI) control method. The quality of the results show that the proposed method is excellent compared with FLC and PI control. It improves response time between 24.223% and 82.9% for PI and between 22.292% and 63.694% for FLC. It guarantees an improvement in the mean square error on PI of between 5.865% and 57.994%, and on FLC of between 1.582% and 29.683%. The proposed method guarantees a high rationing coefficient, helping to maintain the balance between supply and demand, ensure continuity of service for the power supply, and improve the accuracy and self-compensation of the energy to be supplied, both qualitatively and quantitatively.

Research on the coordinated control of oxygen excess ratio and air pressure for PEMFC's air supply system

The proton exchange membrane fuel cell (PEMFC) air supply system is a complex nonlinear system with strong coupling of air flow and pressure. To avoid oxygen starvation and unstable output performance during rapid load changes, the oxygen excess ratio (OER) and air pressure need to be controlled effectively. To cope with the nonlinearity, coupling and uncertainty of the PEMFC air system, this paper puts forward a coordinated controller regulating OER and air pressure based on the input-output linearization and sliding mode methods. Firstly, a control-oriented third-order model is proposed to describe the dynamic behavior of the air supply system. Secondly, this paper decouples the flow and pressure of the PEMFC air system by using an input-output linearization method to obtain two independent linear subsystems. Moreover, a sliding mode method, effectively coping with the uncertainty of the system model, is designed to regulate the OER and air pressure of the PEMFC, which enhances the robustness of the control system. The overall simulation outcomes show that the proposed controller regulates OER and cathode pressure with smaller root mean square error (RMSE) and mean absolute percentage error (MAPE) compared with the combined proportional integral (PI) and feedforward controller.

Integral Backstepping Control of Two Compartment Microbial Fuel Cell

Integral Backstepping Control of Two Compartment Microbial Fuel Cell

Improving Power Quality in a Grid-Connected Hybrid Power System through the Implementation of a Fuzzy Logic Controller

The examination of modeling and designing grid-connected hybrid power systems with efficient control strategies is the focus of this study. Integration of wind and photovoltaic systems into the ac-bus bar system is a common practice in enhancing operational performance. Utilization of Matlab Simulink software is employed for the design of this hybrid system, incorporating efficient MPPT control algorithms to optimize power generation from solar and wind sources under varying climatic conditions. Analysis of the maximum power point tracking mechanism and control methods of the hybrid power system is conducted, particularly in response to changes in weather conditions such as wind speed and solar radiation. Maintaining grid voltages and Unity Power Factor (UPF) while feeding currents into the grid is essential for the hybrid system. The employment of Proportional Integral (PI) control instead of Fuzzy Logic Controller (FLC) in the voltage-oriented control approach is explored to enhance power quality.

Speed Control of Induction Motors using Neuro Fuzzy Expert Systems

Induction motors and their speed control is critical aspect in several real life applications. Often, the proportional integral derivative (PID) controlling mechanism is found in several applications in industries where speed of motors or induction motors is to be controlled. The PID controller is specifically useful since it tries to minimize the steady state error as well as increase the response or speed of the system, thereby incorporating the benefits of proportional derivative and proportional integral control. However, the real time operation of PID controllers is challenging due to its tuning. The controlling mechanism is critically important for the application of the PID. Previously, manual tuning was used which needed experts and was also prone to errors. With the advent of sophisticated optimization tools, automatic tuning has gained momentum. In this work, a combination of neural networks and fuzzy logic often called neuro fuzzy expert systems has been proposed for the speed control of induction motors. It has been shown that proposed system is capable to attain better results compared to conventional techniques. The system has been designed on Matlab/Simulink. Key Words: Induction Motors, Speed Control, PID Controllers, Fuzzy Logic, Neuro Fuzzy Expert Systems.

Enhanced EV Battery Monitoring Using IoT with Improved SEPIC - ZETA Converter and Modified Lion Optimization for Photovoltaic Systems

The rising popularity of electric vehicles (EVs) and the increasing emphasis on sustainable transportation have highlighted the importance of advanced monitoring systems. These systems play a crucial role in providing real-time information about battery parameters to ensure optimal performance and longevity. Moreover, the integration of renewable energy sources, specifically photovoltaic (PV) systems, with EV charging infrastructure presents an exciting prospect to enhance energy efficiency and minimize environmental harm. As a consequence the proposed work develops an Internet of Things (IoT) based PV fed EV charging system. The system incorporates an improved SEPIC-Zeta converter together with Modified Lion Optimization algorithm (LOA) assisted Proportional Integral (PI) controller for optimizing PV system for charging EV battery. IoT connectivity monitors parameter such as PV voltage, PV current, and State of Charge (SOC) of battery. Real-time monitoring of these parameters is achieved by leveraging sensors. The collected data is transmitted through IoT networks to a central monitoring system, enabling operators to track the performance of PV system and EV battery. This facilitates optimized energy generation, efficient battery charging and discharging, and overall system reliability. IoT-enabled monitoring system provides remote accessibility, data analytics, and the generation of alerts and notifications for timely actions. Experimental validation demonstrates that, the proposed concept enhances the monitoring capabilities, improves energy efficiency, and ensures reliable operation of EV charging infrastructure.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

Robust Hybrid Visual Servoing of Omnidirectional Mobile Manipulator With Kinematic Uncertainties Using a Single Camera.

A robust hybrid visual servoing (HVS) method for a single camera-mounted omnidirectional mobile manipulator (OMM) with kinematic uncertainties caused by slipping is proposed herein. Most existing studies pertaining to the visual servoing of mobile manipulators do not consider the kinematic uncertainties and the singularity of the manipulator that can occur during actual operations; furthermore, they required external sensors other than a single camera. In this study, the kinematics of an OMM are modeled considering kinematic uncertainties. Accordingly, an integral sliding-mode observer (ISMO) is designed to estimate the kinematic uncertainties. Subsequently, an integral sliding-mode control (ISMC) law is proposed to achieve robust visual servoing using the estimates of the ISMO. Additionally, an ISMO-ISMC-based HVS method is proposed to address the singularity issue of the manipulator; this method guarantees both robustness and finite-time stability in the presence of kinematic uncertainties. Overall, the entire visual servoing task is performed using only a single camera attached to the end effector without any other external sensors, unlike in previous studies. The stability and performance of the proposed method are verified numerically and experimentally in a slippery environment that generates kinematic uncertainties.

Integral Concurrent Learning Control Lyapunov Functions and High Order Control Barrier Functions and Application to Adaptive Cruise Control

Integral Concurrent Learning Control Lyapunov Functions and High Order Control Barrier Functions and Application to Adaptive Cruise Control

Design method of multivariable PI controller for turboprop engine based on equivalent transfer function

Traditional centralized Proportional Integral (PI) controller design methods based on Equivalent Transfer Functions (ETFs) have poor decoupling effect in turboprop engines. In this paper, a centralized PI design method based on dynamic imaginary matrix and equivalent transfer function is proposed. Firstly, a method for solving equivalent transfer functions based on the dynamic imaginary matrix is proposed, which adopts dynamic imaginary matrix to describe the dynamic characteristics of the system, and obtains the equivalent transfer function based on the dynamic imaginary matrix characteristics. Secondly, for the equivalent transfer function, a centralized PI control gain is designed using the Taylor expansion method. Meanwhile, this paper further proves that the centralized PI design method proposed in this paper has integral stability. Considering the impact of altitude and Mach number on turboprop engines, a linear feedforward control method based on the transfer function matrix is further proposed based on the centralized PI controller, and the stability of the entire comprehensive control method is proved. Finally, to ensure the safe and effective operation of the turboprop engine, a temperature and torque limiting protection controller is designed for the turboprop engine. Simulation results show that the centralized PI controller design method and linear feedforward control method proposed can effectively improve the control quality of turboprop engine control systems.

Low Frequency Sampling in Model Predictive Path Integral Control

Low Frequency Sampling in Model Predictive Path Integral Control

Improved current control for PMSM via an active disturbance rejection controller

This article proposes an active disturbance rejection controller (ADRC) for regulating the dq axis currents of permanent magnet synchronous motors (PMSM). First, we establish actual PMSM model by considering ineluctable disturbances due to the variation of parameters, abrupt change of load, etc. Initially, we design dq axis current ADRCs incorporating an extended state observer (ESO). Compared to the commonly used proportional integral (PI) controller, the ADRC exhibits superior anti-interference capabilities in load and speed regulation. Secondly, we conduct a comprehensive analysis of ADRC’s key features, including ESO stability, dq-axis current tracking, and its anti-interference effectiveness in PMSM applications. Additionally, we offer methods for selecting parameters for both ADRC and ESO. Concurrently, we also examine the ADRC algorithm that utilizes a reduced-order extended state observer (RESO). The control performance of ADRC using RESO will be better than ESO. Finally, to verify the effectiveness of this method, we construct an experimental platform using the TMS320F28035. The results confirm the proposed method’s effectiveness.

Research on adaptive feedforward control method for Tiltrotor Aircraft/Turboshaft engine system based on radial basis function neural network

The paper proposes an adaptive feedforward control approach based on a radial basis function neural network to address the issue of poor disturbance rejection capability in conventional integrated control methods for tiltrotor aircraft propulsion systems. Firstly, a comprehensive simulation model of the tiltrotor aircraft and turboshaft engine is established, and its accuracy is validated against the results of the General Tiltrotor Integrated Simulation Code. Building upon this, a cascade Proportional-integral control parameter design method based on separation principles is proposed to tackle the problem of designing control parameters for the Proportional Integral main loop structure. Subsequently, an adaptive feedforward control method that takes into account both aircraft-induced disturbances and model uncertainties for turboshaft engines is presented using a Radial Basis Function neural network. This approach includes a nonlinear estimator capable of estimating load disturbances and a Radial Basis Function adaptive feedforward controller. Furthermore, stability proof of the closed-loop system is provided based on Lyapunov function analysis. Finally, digital simulations and hardware-in-the-loop experiments are conducted based on a typical tiltrotor flight mission. The results demonstrate that the proposed method effectively mitigates the power turbine drop compared to the conventional Proportional Integral controller with collective pitch feedforward.

Seamless Switching Control Strategy for a Power Conversion System in a Microgrid Based on Extended State Observer and Super-Twisting Algorithm

Microgrids can operate stably in both islanded and grid-connected modes, and the transition between these modes enhances system reliability and flexibility, enabling microgrids to adapt to diverse operational requirements and environmental conditions. The switching process, however, may introduce transient voltage and frequency fluctuations, causing voltage and current shocks to the grid and potentially damaging devices and systems connected to the microgrid. To address this issue, this study introduces a novel approach based on the Extended State Observer (ESO) and the Super-Twisting Algorithm (STA). Power conversion systems use Virtual Synchronous Generator (VSG) control and Power-Quality (PQ) control when they are connected to the grid or when the microgrid is not connected to the grid. VSG and PQ share a current loop. Transitioning the reference current generated by the outer loop achieves the switching of control strategies. A real-time observer is designed to estimate and compensate for current fluctuations, disturbances, and variations in id, iq, and system parameters during the switching process to facilitate a smooth transition of control strategies. Furthermore, to enhance the dynamic response and robustness of the system, the Proportional–Integral (PI) controller in the ESO is replaced with a novel super-twisting sliding mode controller based on a boundary layer. The Lyapunov stability principle is applied to ensure asymptotic stability under disturbances. The proposed control strategy is validated through simulation using a seamless switching model of the power conversion system developed on the Matlab/Simulink (R2021b) platform. Simulation results demonstrate that the optimized control strategy enables smooth microgrid transitions, thereby improving the overall reliability of grid operations.

Hybrid Sliding Mode Control with Gain Scheduling Proportional Integral Derivative Controller for Water Tank System

Hybrid Sliding Mode Control with Gain Scheduling Proportional Integral Derivative Controller for Water Tank System

Analysis of interval type-2 fuzzy controller based interleaved sepic PFC converter for PQ enhancement in power solutions

The Power solutions have become indispensable for all the devices in recent years with an appropriate power conversion circuitries and control methods to ensure good dynamic response, improved stability, reliability and efficiency. The main intent of this article is to impart the designing of interval type-2 fuzzy logic controller (IT2FLC) based interleaved Sepic power factor correction (PFC) converter. This work also involves the careful design of the robust controller with enhanced precision and good power quality (PQ) performance at the AC mains. In addition, the development of IT2FLC based power solution improves the overall power conversion with stabilized output in the perspective of its quick rise time, less overshoot and fast settling time in comparison to other traditional controllers. Further, the uncertainties and issues associated with the conventional proportional integral (PI) and fuzzy logic controllers (FLCs) are handled effectively by the proposed IT2FLC controller. Moreover, this preferred converter is modeled with an internal parasitics and its performances are evaluated and compared with other conventional Zeigler Nicholas (ZN) tuned PI controller and FLC by dint of MATLAB/Simulink platform. Finally, the experimental test bench set up of 250 W, 48 V power circuitry is devised and the test outcomes confirm the excellent transient behavior and PQ performances of the modeled power solution.

A novel scheme of load frequency control for a multi-microgrids power system utilizing electric vehicles and supercapacitors

Incorporation of renewable energy sources (RESs) into power systems has recently experienced a rapid surge, primarily driven by environmental concerns and the desire for more sustainable power generation. However, the replacement of traditional generating units with RESs lowers the system inertia significantly and causes intermittent generation. These issues make frequency stability a greater concern, particularly in microgrids (MGs), and produce mechanical stress on the conventional generation units. Moreover, in interconnected MGs, the complexity is further magnified owing to interaction between microgrids through the tie power which may increase the disturbance propagation among microgrids. This paper suggests a novel control scheme based on a centralized fractional order proportional integral controller cascaded with a virtual inertia emulator (C.FOPI+VI). The virtual inertia emulation is provided using a supercapacitor (SC) along with electric vehicles (EVs) controlled through a virtual inertia controller. However, the EV is suffering from uncertainty, hence the supercapacitor can mitigate this uncertainty. The control system is applied on three interconnected microgrids, each has a thermal plant, a supercapacitor, and an electric vehicle aggregator. The performance of the system incorporating the proposed control technique is compared with five different inertia emulation topologies known as virtual inertia (VI), virtual synchronous generator (VSG), distributed fractional order proportional integral controller cascaded with virtual inertia (D.FOPI+VI), distributed fractional order proportional integral controller cascaded with virtual synchronous generator (D.FOPI+VSG), and without synthetic inertia. To optimize the parameters of different controllers, the Transit Search (TS) optimization algorithm is employed considering the frequency deviation, the restoration time, and the tie power. The proposed C.FOPI+VI has proven its superiority in providing fast frequency restoration, less stress on the conventional generation units, and minimizing the spreading of the disturbance within the system. Furthermore, the proposed controller enhances the Gain Margin (G.M) to 21.1 dB compared to operation without inertia support (0.322 dB), VI (3.38 dB), VSG (5.23 dB), DFOPI+VI (8.25), and DFOPI+VSG (8.32).

An Attitude Adaptive Integral Sliding Mode Control Algorithm with Disturbance Observer for Microsatellites to Track High-Speed Moving Targets

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution, narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.03°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.