Self-tuning Control Research Articles

Optimized Neural Networks-PID Controller with Wind Rejection Strategy for a Quad-Rotor

In this paper a full approach of modeling and intelligent control of a four rotor unmanned air vehicle (UAV) known as quad-rotor aircraft is presented. In fact, a PID on-line optimized Neural Networks Approach (PID-NN) is developed to be applied to angular trajectories control of a quad-rotor. Whereas, PID classical controllers are dedicated for the positions, altitude and speed control. The goal of this work is to concept a smart Self-Tuning PID controller, for attitude angles control, based on neural networks able to supervise the quad-rotor for an optimized behavior while tracking a desired trajectory. Many challenges could arise if the quad-rotor is navigating in hostile environments presenting irregular disturbances in the form of wind modeled and applied to the overall system. The quad-rotor has to quickly perform tasks while ensuring stability and accuracy and must behave rapidly with regards to decision making facing disturbances. This technique offers some advantages over conventional control methods such as PID controller. Simulation results are founded on a comparative study between PID and PID-NN controllers based on wind disturbances. These later are applied with several degrees of strength to test the quad-rotor behavior and stability. These simulation results are satisfactory and have demonstrated the effectiveness of the proposed PD-NN approach. In fact, the proposed controller has relatively smaller errors than the PD controller and has a better capability to reject disturbances. In addition, it has proven to be highly robust and efficient face to turbulences in the form of wind disturbances.

Optimization of DC motor speed control based on fuzzy logic-PID controller

In this paper the PID controller and the Fuzzy Logic Controller (FLC) are used to control the speed of separately excited DC motors. The proportional, integral and derivate (KP, KI, KD) gains of the PID controller are adjusted according to Fuzzy Logic rules. The FLC cotroller is designed according to fuzzy rules so that the system is fundamentally robust. Twenty-five fuzzy rules for self-tuning of each parameter of the PID controller are considered. The FLC has two inputs; the first one is the motor speed error (the difference between the reference and actual speed) and the second one is a change in the speed error (speed error derivative). The output of the FLC, i.e. the parameters of the PID controller, are used to control the speed of the separately excited DC Motor. This study shows that the precisiom feature of the PID controllers and the flexibllity feature of the fuzzy controller are presented in the fuzzy self-tuning PID controller. The fuzzy self – tuning approach implemented on the conventional PID structure improved the dynamic and static response of the system. The salient features of both conventional and fuzzy self-tuning controller outputs are explored by simulation using MATLAB. The simulation results demonstrate that the proposed self-tuned PID controller i.plementd a good dynamic behavior of the DC motor i.e. perfect speed tracking with a settling time, minimum overshoot and minimum steady state errorws.

A fuzzy-PID series feedback self-tuned adaptive control of reactor power using nonlinear multipoint kinetic model under reference tracking and disturbance rejection

In this work, a series feedback combination of fuzzy and PID controllers is designed to resolve the reactor power control problem for VVER-1000 PWR.A nonlinear multipoint kinetic model based on neutron diffusion, thermal hydraulics, spatial neutronics and poison effect developed for dynamic simulation with three groups of delayed neutrons. PID controller design is accompanied with the variable gains depending upon the reactor power level and error power concerning a defined reference value. Piece-wise linearity and curve fitting is used to find a linear variation of gainsand incorporated in the fuzzy control design.The optimum values of PID gains are based on the Matlab Tools,desired performance indices of response andthe critical limitations of control output for accommodating the actuator motorboundaries. The proposed fuzzy-PID adaptive control model found preferable in load tracking application and disturbance rejection conditions in comparison with PID and fuzzy controllers in standalone configuration.

A flexible and collaborative approach to robotic box-filling and item sorting

In this paper, we introduce an adaptive robotic manipulation framework to respond to the flexibility needs of common industrial tasks such as box-filling and item sorting. The proposed framework consists of a vision module and a robot control module. The vision module is responsible for the detection and tracking of the environment (e.g., box and the items), which is also capable of creating an occupancy grid in real-time, to continuously update the robot trajectory planner with the occupied portions of the detected box and their coordinates. The robot control module includes a trajectory planner and a self-tuning Cartesian impedance controller, to implement an adaptive strategy for the picking, placement, and sorting of the items in the box. The item-sorting strategy is based on our preliminary observations on human motor behavior, implementing a trade-off between the task execution accuracy and environmental perception uncertainty. The efficacy of the framework in performing a flexible box-filling task using a robot, autonomously or in collaboration with a human, is evaluated through several experiments.

Does the Heart Want What It Wants? A Case for Self-Adapting, Mechano-Sensitive Therapies After Infarction

There is a critical need for interventions to control the development and remodeling of scar tissue after myocardial infarction. A significant hurdle to fibrosis-related therapy is presented by the complex spatial needs of the infarcted ventricle, namely that collagenous buildup is beneficial in the ischemic zone but detrimental in the border and remote zones. As a new, alternative approach, we present a case to develop self-adapting, mechano-sensitive drug targets in order to leverage local, microenvironmental mechanics to modulate a therapy's pharmacologic effect. Such approaches could provide self-tuning control to either promote fibrosis or reduce fibrosis only when and where it is beneficial to do so.

Can Deep Models Help a Robot to Tune Its Controller? A Step Closer to Self-Tuning Model Predictive Controllers

Motivated by the difficulty roboticists experience while tuning model predictive controllers (MPCs), we present an automated weight set tuning framework in this work. The enticing feature of the proposed methodology is the active exploration approach that adopts the exploration–exploitation concept at its core. Essentially, it extends the trial-and-error method by benefiting from the retrospective knowledge gained in previous trials, thereby resulting in a faster tuning procedure. Moreover, the tuning framework adopts a deep neural network (DNN)-based robot model to conduct the trials during the simulation tuning phase. Thanks to its high fidelity dynamics representation, a seamless sim-to-real transition is demonstrated. We compare the proposed approach with the customary manual tuning procedure through a user study wherein the users inadvertently apply various tuning methodologies based on their progressive experience with the robot. The results manifest that the proposed methodology provides a safe and time-saving framework over the manual tuning of MPC by resulting in flight-worthy weights in less than half the time. Moreover, this is the first work that presents a complete tuning framework extending from robot modeling to directly obtaining the flight-worthy weight sets to the best of the authors’ knowledge.

DRNN Robust DTC for Induction Motor Drive Systems Using FSTPI

In this paper, a self-tuning PI speed controller based on diagonal recurrent neural network is (DRNN) investigated and simulated to increase the robustness of the direct torque control (DTC) scheme for three-phase low-power IM drive system using a Four Switch Three-Phase Inverter (FSTPI). The drive is subjected to different system inputs and disturbances, step changes in speed under different load conditions, abrupt loading at high speed and speed reversal. Furthermore, the robustness of the controller is evaluated by varying motor parameter, stator resistance and moment of inertia. A comparison of classical and self-tuning PI speed controllers was presented to determine the effectiveness of the proposed controller. It is concluded based on simulation results using Matlab/Simulink. that the self-tuning PI speed controller provides the best performance by reacting rapidly and adaptively.

Optimization Design of Centrifugal Pump Flow Control System Based on Adaptive Control

In this paper, in order to improve the control characteristics of the centrifugal pump flow control system, a mathematical model of the centrifugal pump flow control system was established based on an analysis of the basic structures, such as the frequency converter, motor, and centrifugal pump. Based on the adaptive control theory, the recursive least squares algorithm with a forgetting factor was used to estimate the real-time parameters of the centrifugal pump control system, and the self-tuning PID control method was used to optimize the mathematical model of the centrifugal pump flow control system. The simulation results showed that the adjustment time of the optimized system was shortened by 16.58%, and the maximum overshoot was reduced by 83.90%, which improved the rapidity and stability of the transient response of the system. This showed that adaptive control had a significant effect on improving the robustness and anti-interference ability of the centrifugal pump control system. In order to further verify the accuracy of the self-tuning PID control method, a flow adaptive control system test platform was built. The test results showed that under the conditions of constant frequency and variable frequency, the actual flow rate of the centrifugal pump was always kept near the set flow rate, the error was small, and it had better real-time followability. The research results showed that adaptive control could revise the parameters in real-time according to changes to the centrifugal pump control system, which improved the stability and robustness of the system. Therefore, adaptive PID control could effectively improve the adaptability of centrifugal pumps to various complex working conditions and improve the working efficiency of centrifugal pumps.

Optimization of Neural Network-Based Self-Tuning PID Controllers for Second Order Mechanical Systems

The feasibility of a neural network method was discussed in terms of a self-tuning proportional–integral–derivative (PID) controller. The proposed method was configured with two neural networks to dramatically reduce the number of tuning attempts with a practically achievable small amount of data acquisition. The first network identified the target system from response data, previous PID parameters, and response characteristics. The second network recommended PID parameters based on the results of the first network. The results showed that it could recommend PID parameters within 2 s of observing responses. When the number of trained data was as low as 1000, the performance efficiency of these methods was 92.9%, and the tuning was completed in an average of 2.94 attempts. Additionally, the robustness of these methods was determined by considering a system with noise or a situation when the target position was modified. These methods are also applicable for traditional PID controllers, thus enabling conservative industries to continue using PID controllers.

Methods of Pre-Identification of TITO Systems

The content of this article is the presentation of methods used to identify systems before actual control, namely decentralized control of systems with Two Inputs, Two Outputs (TITO) and with two interactions. First, theoretical assumptions and reasons for using these methods are given. Subsequently, two methods for systems identification are described. At the end of this article, these specific methods are presented as the pre-identification of the chosen example. The Introduction part of the paper deals with the description of decentralized control, adaptive control, decentralized control in robotics and problem formulation (fixing the identification time at the existing decentralized self-tuning controller at the beginning of control and at the beginning of any set-point change) with the goal of a new method of identification. The Materials and methods section describes the used decentralized control method, recursive identification using approximation polynomials and least-squares with directional forgetting, recursive instrumental variable, self-tuning controller and suboptimal quadratic tracking controller, so all methods described in the section are those ones that already exist. Another section, named Assumptions, newly formulates the necessary background information, such as decentralized controllability and the system model, for the new identification method formulated in Pre-identification section. This section is followed by a section showing the results obtained by simulations and in real-time on a Coupled Drives model in the laboratory.

Adaptive Voltage Regulation Control Strategy in a Stand-Alone Islanded DC Microgrid based on distributed Wind / Photovoltaic / Diesel / Energy Storage Hybrid Energy Conversion System

An adaptive approach for optimal tuning of a SMC for an automated voltage regulator system is displayed in this study. The approach is centered on hybrid of the GA and MOPSA. In addition, unique objective functions for the controller's parameter optimization are suggested. The performance of the resulting perfect sliding mode controller is confirmed by comparing it to controllers adjusted using various techniques that have been published in the literature. The simulation outcomes indicate that controllers tuned with the projected MOPSO and GA algorithms outperform controllers tuned with existing methods. In addition, a comparison study is performed to select the best controller for use in AVR systems. The suggested algorithm's major benefit is a considerable boost in convergence speed. With step changes and step load modifications in input wind power, the system model with built-in intelligent controller is generated in MATLAB/SIMULINK. The benefits of the recommended intelligent control algorithm are confirmed by comparing the outcomes of the sliding mode controller and the projected MOPSO self-tuned controller. The findings show that the hybrid Wind/PV system's reactive power adjustment capabilities. When used in conjunction with BES, it is extremely successful in optimising the voltage profile although providing active energy to local load.

Adaptive tuning of network traffic policing mechanisms for DDoS attack mitigation systems

Distributed denial-of-service (DDoS) attacks are responsible for shutting down servers, denying access to critical sectors of the economy, and generating substantial downtime costs and reputation harm. Only in 2020, over ten million DDoS attacks were observed worldwide. Commonly, open-loop network traffic rate policing is used to mitigate them. That is how network devices are currently designed. This paper shows how to extend the state-of-the-art design by introducing the adaptive closed-loop tuning of policing mechanisms. As demonstrated experimentally, open-loop policing based on the celebrated token-bucket mechanism generates a steady-state control error. In contrast, the robust self-tuning controller eliminates that control error while adjusting the bitrate limiting operations to the severe and hardware-specific network operating conditions during a DDoS attack. The study shows the controller’s implementation details and discusses the critical difficulties encountered in its technical development. Furthermore, it illustrates how the control error variance depends on the commanded traffic rate limit and explains why the self-tuning controller’s anti-windup filter may fail to bring the control signal back to the set of admissible control values. All experiments presented in this paper were conducted using real data from the Polish nation-wide cybersecurity system FLDX managed by the NASK National Research Institute.

Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller.

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations.

Model-free MIMO self-tuning controller based on support vector regression for nonlinear systems

Model-free MIMO self-tuning controller based on support vector regression for nonlinear systems

Automatic Indoor Air Quality Control of COVID -19 Patient Facilities Using Type-2 Fuzzy Controller

Big facilities can be used to set up cohort wards for severe and critical COVID-19 patients. The purpose of carbon dioxide and humidity control is to provide healthy air for breathing by both diluting the pollutants originating in the building and removing the pollutants from it. In this paper, for proper mechanical ventilation to maintain indoor air quality (IAQ), a Self-tuned Interval Type-2 Fuzzy PI controller (SIT2FPIC) is proposed. 49 fuzzy if-then rules are used to tune the Interval Type-2 Fuzzy PI controller (IT2FPIC) for proper functioning and to reduce the parameter variations. Correlations of CO2 concentration and air humidity are studied to maintain the air quality in COVID-19 facilities. Mathematical models of CO2 concentration and air humidity in hospitals or temporary COVID-19 facilities are developed considering the presence of COVID-19 patients, doctors, nurses, etc in the room. The control and monitoring of room air quality are very much essential for any infectious diseases like coronavirus. To maintain the air quality in terms of humidity and carbon dioxide, the proposed SIT2FPIC is applied to the mathematical model of humidity and carbon dioxide for the simulation study. The effectiveness of the proposed controller in mechanical ventilation in the treatment facilities is shown by a comparative study of different transient parameters. Effects of load variations in the model are also studied to check the usefulness of the proposed scheme.

Modelling and analysis of inertia self‐tuning phase control strategy for a floating multi‐body wave energy converter

Wave energy converters (WECs) exhibit higher energy capture capacity when the wave frequency is close to the nature frequency of the device. A possible way to enhance energy capture efficiency is to modify the nature frequency of a WEC to match the incoming wave frequency. This paper presents an inertia self-tuning phase control strategy which can optimize the wave energy capture efficiency of a floating multi-body (FMB) WEC by regulating automatically the total mass of the capture energy part according to the incoming wave conditions. A variable-frequency inertia self-tuning control system is designed to conduct the proposed control strategy. The mathematical model of rotational motion of the FMB WEC is built and the dynamic performance of the control system is analysed in detail. Simulation results illustrate the proposed control strategy can achieve a phase optimization and hence significantly improve wave energy extraction of the FMB WEC in both regular and irregular waves. For regular waves, the maximum capture width ratio)CWR( under the control strategy is 7.3 times higher than that no control strategy is used. For irregular waves, the maximum CWR under the control strategy is 4.4 times that without the control strategy at the same wave frequency.

Design of Neutrosophic Self-Tuning PID Controller for AC Permanent Magnet Synchronous Motor Based on Neutrosophic Theory

In practical control applications, AC permanent magnet synchronous motors need to work in different response characteristics. In order to meet this demand, a controller which can independently realize the different response characteristics of the motor is designed based on neutrosophic theory and genetic algorithm. According to different response characteristics, neutrosophic membership functions are constructed. Then, combined with the cosine measure theorem and genetic algorithm, the neutrosophic self-tuning PID controller is designed. It can adjust the parameters of the controller according to response requirements. Finally, three kinds of controllers with typical system response characteristics are designed by using Simulink. The effectiveness of the designed controller is verified by simulation results.

Enhancing the performance of a deregulated nonlinear integrated power system utilizing a redox flow battery with a self-tuning fractional-order fuzzy controller

Load frequency regulation is one of the most vital and complex ancillary services in a deregulated power system. Increasing penetration from renewable energy sources in an integrated power system (IPS) further escalates the related control complexity due to a considerable decrement in IPS’s effective inertia. This may incur additional costs and can even lead to the destabilization of IPS. To overcome these problems in frequency regulation, this work proposes and investigates the use of an intelligent, direct adaptive control scheme, i.e., self-tuning fractional order fuzzy PID (STFOFPID) controller with and without the presence of a recently devised energy storage unit, i.e., the redox flow battery. The IPS’ efficacy with the STFOFPID controller is validated for various contracts in a deregulated operation mode for considered three area IPS. Extensive simulation studies are carried out, and detailed comparative studies have been drawn with conventional PID and fractional order PID controllers for load frequency regulation in Poolco, bilateral, and contract-violation mode of operation. Robustness analysis in terms of parametric variations in different nonlinearities present in a reheated thermal power plant is also carried out, and the efficacy of the STFOFPID controller is established using a thorough quantitative comparative analysis. The real-time digital simulation validation of the investigated control structure has been carried out on OPAL-RT 4150 based on Xilinx Kintex-7 FPGA board with INTEL multi-core processor.

Development of an Intelligent Control System for Spacecraft Vacuum Thermal Test

This article develops a spacecraft component-level vacuum thermal test intelligent control system, which can control the thermal vacuum chamber to automatically complete the entire test process including establishing a vacuum environment, setting temperature control parameters, switching test conditions, and executing shutdown procedures. It is the first to complete the one-key boot function in similar domestic laboratories. This system combines thermostat, parameter identification and self-tuning control technology to complete the temperature control of the test piece. It solves the technical problems of complex thermal characteristics of test piece, large time delay, and overshoot in temperature control. The system also has the function of automatic conversion of test condition and provides a guarantee for the smooth development of the vacuum thermal test of the spacecraft and its components.

An adaptive CGPC based anti-windup PI controller with stability constraints for the intermittent power penetrated system

With the increasing penetration of the renewable energy, the modern power system is facing greater challenges to maintain the frequency stability, which can be studied with load frequency control (LFC). Since the conventional proportional integral (PI) controller can’t necessarily satisfy the ever-growing robustness requirement, an adaptive constrained generalized predictive control (CGPC) based anti-windup PI controller with stability constraints is proposed, which is referred to as CGPC-PI in this article. Wherein, the CGPC logic in the upper hierarchy acts as a real-time optimizer for the PI parameters in the lower hierarchy. The proposed control scheme inherits the self-tuning and adaptive control characteristics from CGPC and can maintain the facilities and control logic from PI. Furthermore, simpler stability constraints and performance improvement could be gained considering that PI is the actual implementing control method. Simulation experiments are conducted to reveal the frequency regulation performances in a Generation Rate Constraint (GRC) and Governor Deadband (GDB) involved interconnected power system with intermittent power penetration. The results can testify the superiority of CGPC-PI over the comparative methods in terms of the overshoot and damping performance, which includes:(1) CGPC-PI outperforms PI and CGPC. (2) the superiority is more obvious when working with varying disturbances which is big enough can trigger the anti-windup scheme. (3) CGPC-PI can enhance the frequency regulation performance of the intermittent power penetrated systems.