Single Proportional-integral-derivative Research Articles

Nonlinear Kalman filter for gyroscopic and accelerometer noise rejection of an unmanned aerial vehicle control strategy

This study addresses timing issues inherent in traditional proportional-integral-derivative (PID) controllers for drone angle control and introduces an innovative solution, the adaptive PID flight controller, aimed at optimizing PID gains for improved performance in terms of speed, accuracy, and stability. To enhance the controller's robustness against noise and accurately estimate the system's state, a Kalman filter is incorporated. This filtering mechanism is designed to reject noise and provide precise state estimation, thereby contributing to the overall effectiveness of the adaptive PID flight controller in managing altitude dynamics for unmanned aerial vehicles (UAVs). The comparative methodology evaluates three configurations: a single PID controller for all three angles, two PID controllers dedicated to pitch/roll and yaw angles separately, and three PID sub-controllers for each angle (pitch, roll, and yaw). The study seeks to identify the most effective PID configuration in terms of stability, responsiveness, and accuracy while highlighting the added benefits of noise rejection and state estimation through the Kalman filter. This integrated approach showcases innovation and effectiveness, introducing a comprehensive solution not explored in previous research.

BLDC Motor Stability Management Using Adaptive PID (MRAC-PID)

BLDC motors have become popular in various industries such as automotive, consumer, healthcare, industrial automation, and instrumentation due to their optimal performance. To keep the BLDC motor in optimal condition, a control engineering system is required that serves as a controller. A single Proportional Integral Derivative (PID) control system is only suitable for linear conditions, so it cannot produce satisfactory output when there is a change in set point. To overcome this obstacle, an adaptive PID control system known as MRAC-PID control system is applied, which is able to control the stability of the BLDC motor as desired. Testing of this system is done under 4 different conditions using MATLAB software. After testing, the parameter values for the MRAC control system were obtained, namely ???????? = 0.4; ???????? = 50.75; ???????? = 0.0000867. Based on the test, the MRAC control system produces a system success rate of 81.2% to 98.9%.

Gain self-adjusting single neuron PID control method and experiments for longitudinal relative position of harvester and transport vehicle

To accurately control the longitudinal relative position of a harvester and transport vehicle, such that grain in the harvester can be accurately unloaded into the transport vehicle's granary, this study established a master–slave collaborative harvesting system. Furthermore, this study proposed a calculation method for the longitudinal deviation of two vehicles, analyzed the structure and mathematical model of the hydraulic stepless transmission, identified the proposed stem transfer function of the hydraulic stepless transmission speed, analyzed incremental proportional–integral–derivative (PID), and developed self-adjusting–single-neuron PID control methods. Simulations and field experiments were performed to test the applicability of the proposed system. The experimental results indicated that the maximum overshoot of the longitudinal deviation between the harvester and the transport vehicle did not exceed 0.25 m, the steady-state mean absolute deviation did not exceed 0.08 m, the steady-state maximum deviation did not exceed 0.26 m, and the steady-state standard deviation did not exceed 0.09 m, when the harvester speed was 0.6, 0.8, and 1 m/s. Field-harvesting application experiments showed that the settling time during the alignment process was 13.2 s, and the steady-state maximum deviation was 0.253 m. The grain can be accurately unloaded into the granary of the transport vehicle. These results indicate that the control method and the master–slave collaborative harvesting system proposed in this study can meet the needs of precise collaborative unloading between the harvester and transport vehicle.

Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

<span lang="EN-US">This paper presents an efficient interval type 2 fuzzy (IT2F) based on a single neuron proportional–integral–derivative (PID), also known as IT2FSNPID controller. The main purpose of the proposed control technique is to track the motion profile of the brushless DC (BLDC) motor. Also, a comparative study was investigated fuzzy type 1 (FT1) and IT2F. IT2F can treat the uncertainty and nonlinearity of the BLDC motor drive electric system in contrast to FT1. The parameters of each control technique were obtained using a new COVID-19 optimization algorithm according to an objective function. Moreover, several tests had been performed to ensure the ability of fuzzy type to absorb the system uncertainty and nonlinearity. All controllers were utilized to operate the BLDC motor sudden change in load and continuous load. The simulation results show that the IT2FSNPID can improve the dynamic response of linear and nonlinear of the same BLDC motor and accommodate the system uncertainty significantly.</span>

MPC-Based Process Control of Deep Drawing: An Industry 4.0 Case Study in Automotive

Deep drawing is a metalworking procedure aimed at getting a cold metal sheet plastically deformed in accordance with a pre-defined mould. Although this procedure is well-established in industry, it is still susceptible to several issues affecting the quality of the stamped metal products. In order to reduce defects of workpieces, process control approaches can be performed. Typically, process control employs simple proportional-integral-derivative (PID) regulators that steer the blank holder force (BHF) based on the error on the punch force. However, a single PID can only control single-input single-output systems and cannot handle constraints on the process variables. Differently from the state of the art, in this paper we propose a process control architecture based on Model Predictive Control (MPC), which considers a multi-variable system model. In particular, we represent the deep drawing process with a single-input multiple-output Hammerstein-Wiener model that relates the BHF with the draw-in of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> different critical points around the die. This allows the avoidance of workpiece defects that are due to the abnormal sliding of the metal sheet during the forming phase. The effectiveness of the proposed process controller is shown on a real case study in a digital twin framework, where the performance achieved by the MPC-based system is analyzed in detail and compared against the results obtained through an ad-hoc defined multiple PID-based control architecture. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the emerging need for the effective implementation of the zero-defect manufacturing paradigm in the Industry 4.0 framework. Especially in the deep drawing process, various quality issues in stamped parts can lead to significant product waste and manufacturing inefficiencies. This turns into considerable economic losses for companies, particularly in the automotive sector, where deep drawing is one of the most used cold sheet metal forming techniques. In most applications, only sample inspections are performed on batches of finished-product, with subsequent losses of time and resources. For the sake of improving the workpiece quality, innovative strategies for real-time process control represent a viable and promising solution. In this context, the proposed MPC-based process control approach allows the correct shaping of the metal sheet that is getting deformed during the forming stroke, thanks to the draw-in monitoring at various locations around the die. The draw-in is indeed one of the most effective forming variables to control in order to provide a correct BHF during the forming stroke. A useful and easy-to-implement non-linear metal sheet deep drawing process model is provided by this paper to perform an innovative process control strategy. A comprehensive methodology is applied in detail to an automotive case study, ranging from process modeling (model identification and validation based on experimental data acquisition) to MPC implementation (controller tuning and testing and software-in-the-loop system validation). The presented method can be easily implemented on any real deep drawing press, providing the multivariable constrained process with a suitable control system able to make the stamped parts well formed.

Numerical and experimental verification of the single neural adaptive PID real-time inverse method for solving inverse heat conduction problems

For the problems of difficult on-line tuning of PID parameters, weak anti-interference ability and poor self-adaptive ability in the classical proportional-integral-derivative (PID) inverse method, combining single artificial neuron with PID inverse algorithm, a single neural adaptive PID (SNA-PID) inverse method is established to estimate the thermal boundary condition. Compared with the classical PID inverse method, the main difference is that SNA-PID can adaptively adjust the weights of PID parameters. In the SNA-PID inverse method, firstly, the deviation signal between the output of the direct heat conduction model and the measured temperature and its first-order difference and second-order difference are taken as three inputs of a single neuron (dendritic input signal of neuron), and the connection strength of each input signal is determined according to the updated weight of the adaptive learning algorithm, The excitation value of the boundary heat flux increment of the heat conduction system is generated according to the linear truncation function. Finally, the real-time estimation of boundary heat flux of heat conduction system is realized. The unsteady boundary heat flux of one-dimensional heat conduction object is estimated through numerical and experimental tests, which verifies the effectiveness of the proposed SNA-PID inverse method.

Stabilization and tracking control of an x-z type inverted pendulum system using Lightning Search Algorithm tuned nonlinear PID controller

AbstractInverted pendulum systems (IPSs) are mostly used to demonstrate the control rules for keeping the pendulum at a balanced upright position due to a slight force applied to the cart system. This paper presents an application for nonlinear control of an x-z type IPS by using a proportional-integral-derivative (PID) controller with newly established evolutionary tuning method Lightning Search Algorithm (LSA). A single, double and triple PID controller system is tested with the conventional and the self-tuning controllers to get a better understanding of the performance of the given system. The mathematical modelling of the x-z type IPS, the theoretical explanation of the LSA and the simulation analysis of the x-z type IPS is put forward entirely. The LSA algorithm solves the optimization problem of PID controller in an evolutionary way. The most effective way of making comparisons is evaluating the performance results with a well-known optimization technique or with the previous accepted results. We have compared the system’s performance with particle swarm optimization and with a classical control study in the literature. According to the simulation results, LSA-tuned PID controller has the ability to decrease the overshoot better than the conventional-tuned controllers. Finally, it can be concluded that the LSA-supported PID can better stabilize the pendulum angle and track the reference better for non-disturbed and the slightly disturbed systems.

Submarine Multi-Model Switching Control Under Full Working Condition Based on Machine Learning

A continuous submarine depth control strategy based on multi-model and machine learning switching method under full working condition is proposed in this paper. A submarine motion model with six-degree-of-freedom is first built and decoupled according to the force analysis. The control set with corresponding precise model set is then optimized according to different working conditions. The multi-model switching strategy is studied using machine learning algorithm. The simulation experiments indicate that a multi-model controller comprised of the proportional-integral-derivative (PID), fuzzy PID (FPID) and model predictive controllers with support vector machine (SVM) switching strategy can realize the continuous submarine depth control under full working condition, showing a good control performance compared with a single PID controller.

Adaptive Single Neuron Anti-Windup PID Controller Based on the Extended Kalman Filter Algorithm

In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.

Practical Control of a Cold Milling Machine using an Adaptive PID Controller

This paper presents a supervised Hebb learning single neuron adaptive proportional-integral-derivative (PID) controller for the power control of a cold milling machine. The proposed controller aims to overcome the deficiency of the current power control algorithm, and to achieve as high an output power as possible for the cold milling machine. The control process and system model are established and presented to provide the insight and guidance to the controller design and analysis. The adaptive PID controller is developed using a supervised Hebb learning single neuron method with detailed algorithm and structure analysis. The field test is performed to validate the proposed single neuron adaptive PID control for the power control. In the test, the 8 cm-depth milling is conducted on a cement concrete pavement in which the cement is not well-distributed. The test results show that when the machine speed is adjusted by the machine itself or manually without the adaptive power control system, the machine is often overloaded or underloaded, and the average work speed is 2.4m/min. However, when the adaptive control system is implemented on the machine, it works very close to its rated work condition during its work process. With the developed controller, the machine work speed is adjusted in time to the load variation and uncertain dynamics. The average machine work speed can reach up to 2.766 m/min, which is 15.25% higher than the wok speed of the machine without an adaptive power control system.

Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine.

The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

Delay Margin of Low-Order Systems Achievable by PID Controllers

This paper concerns the delay margin achievable using proportional-integral-derivative (PID) controllers for linear time-invariant (LTI) systems subject to variable, unknown time delays. The basic issue under investigation addresses the question: What is the largest range of time delay so that there exists a single PID controller to stabilize the delay plants within the entire range? Delay margin is a fundamental measure of robust stabilization against uncertain time delays and poses a fundamental, longstanding problem that remains open except in simple, isolated cases. In this paper, we develop explicit expressions of the exact delay margin and its upper bounds achievable by a PID controller for low-order delay systems, notably the first- and second-order unstable systems with unknown constant and possibly time-varying delays. The effect of nonminimum phase zeros is also examined. PID controllers have been extensively used to control and regulate industrial processes that are typically modeled by first- and second-order dynamics. While furnishing the fundamental limits of delay within which a PID controller may robustly stabilize a delay process, our results should also provide useful guidelines in tuning PID parameters and in the analytical design of PID controllers.

An improved single neuron self-adaptive PID control scheme of superheated steam temperature control system

The control unit of superheated steam temperature of thermal power plants has poor quality, and easy over-temperature problem. In this paper, according to the single neuron adaptive controller with self-learning, strong adaptability, high robustness and fast response, an improved single neuron self-adaptive proportional-integral-derivative (PID) control scheme of superheated steam temperature control system has been presented. The proposed control scheme has two main characteristics: 1) Compared with the traditional PID control scheme, the three PID control parameters of proportional, integral, differential coefficients become a neuron adaptive control coefficient K; 2) This proposed control strategy has provided a new theoretical basis and research methods in the application of control system of superheated steam temperature. Simulation results show that the control scheme has improved the control performance of large time delay, multi-disturbance, and has reflected the strong robustness, high stability and good control quality.

Thermal Management-Oriented Multivariable Robust Control of a kW-Scale Solid Oxide Fuel Cell Stand-Alone System

The aim of this paper is to develop an effective control system for a kW-scale solid oxide fuel cell (SOFC) stand-alone system based on our previous studies, where the dynamic model and the steady-state analysis and optimization of the system have been completed. The open-loop dynamic responses of the system are first conducted to gain insight on the system dynamics, and the results demonstrate that the system is a complicated multivariable system with strong coupling and nonlinear characteristics. Considering the complicated dynamics, a multivariable robust proportional-integral derivative (PID) control system is proposed with a multiloop feedforward/feedback control structure. The feedforward controllers are designed using the stack current based on the steady-state optimization results determined in the previous studies, and the feedback controllers are implemented by developing a single neuron adaptive PID algorithm. The adaptive PID combines the advantages of robust control and PID control, which can automatically adjust control parameters when system encounters uncertainties and disturbances, and is very easy for engineering practices because of its simple structure. Test results demonstrate that the multivariable robust PID control system has well robustness and stability for the SOFC system when operating point changes within the full operating range.

Closed-Loop Control of Chemical Injection Rate for a Direct Nozzle Injection System.

To realize site-specific and variable-rate application of agricultural pesticides, accurately metering and controlling the chemical injection rate is necessary. This study presents a prototype of a direct nozzle injection system (DNIS) by which chemical concentration transport lag was greatly reduced. In this system, a rapid-reacting solenoid valve (RRV) was utilized for injecting chemicals, driven by a pulse-width modulation (PWM) signal at 100 Hz, so with varying pulse width the chemical injection rate could be adjusted. Meanwhile, a closed-loop control strategy, proportional-integral-derivative (PID) method, was applied for metering and stabilizing the chemical injection rate. In order to measure chemical flow rates and input them into the controller as a feedback in real-time, a thermodynamic flowmeter that was independent of chemical viscosity was used. Laboratory tests were conducted to assess the performance of DNIS and PID control strategy. Due to the nonlinear input–output characteristics of the RRV, a two-phase PID control process obtained better effects as compared with single PID control strategy. Test results also indicated that the set-point chemical flow rate could be achieved within less than 4 s, and the output stability was improved compared to the case without control strategy.

Model predictive control of the solid oxide fuel cell stack temperature with models based on experimental data

Generalized predictive control (GPC) is applied to control the maximum temperature in a solid oxide fuel cell (SOFC) stack and the temperature difference over the stack. GPC is a model predictive control method and the models utilized in this work are ARX-type (autoregressive with extra input), multiple input-multiple output, polynomial models that were identified from experimental data obtained from experiments with a complete SOFC system. The proposed control is evaluated by simulation with various input–output combinations, with and without constraints. A comparison with conventional proportional-integral-derivative (PID) control is also made. It is shown that if only the stack maximum temperature is controlled, a standard PID controller can be used to obtain output performance comparable to that obtained with the significantly more complex model predictive controller. However, in order to control the temperature difference over the stack, both the stack minimum and the maximum temperature need to be controlled and this cannot be done with a single PID controller. In such a case the model predictive controller provides a feasible and effective solution.

Input-Rate Based Adaptive Fuzzy Neuron PID Control for AQM

Internet routers play an important role during network congestion. All the routers have buffers at input and output ports to hold the packets at congestion. Various congestion control algorithms have been proposed to control the congestion. Recently, some proportional-integral-derivative (PID) controller based algorithms have been proposed as Active Queue Management (AQM) schemes to address performance degradations of end-to-end TCP congestion control. However, most of the proposed PID-controllers for AQM are validated for their performance and stability via intuitive explanation and simulation studies instead of theoretic analysis and performance evaluation. But there are a few drawbacks of PID-controller based AQM algorithms leading to poor performance like causing data retention dropping and oscillation when the time delay is large, which means that the existing PID-controller can not meet the Quality of Service (QoS) requirements. To overcome the drawbacks of traditional PID, we analyze and enhance the PID-controller based AQM algorithm by regarding the TCP congestion control mechanism as an input-rate based Adaptive Fuzzy Neuron PID control algorithm (IRAFNPID) to avoid congestion in TCP/AQM networks. By means of simulations, we evaluate and compare the performance of traditional PID, single neural adaptive PID(SNAPID) and IRAFNPID, simulations with experiment data analysis and find that IRAFNPID has better convergence, stability, robustness, goodput and lower loss ratio.

A multi-input-single-output smith predictor for feeders control in SAG grinding plants

The accurate control of feeders in semiautogenous grinding(SAG) mills plays an important role for obtaining good operational results. This brief presents the design of a Smith predictor for multi-input-single-output (MISO) systems, representing a set of feeders in a SAG plant. The design considers the special characteristics of the application, as well as issues like performance and robustness of the proposed controller. Experiments carried out at an industrial plant, under several operational conditions, demonstrate the effectiveness of the proposed strategy compared to the one based on a single proportional-integral derivative (PID) controller.

Robust PID Controller Design for Hydroturbines

The design and analysis of a robust proportional, integral, derivative (PID) controller for a hydraulic turbine generator governor using a frequency-response technique is presented. The approach uses data from literature on the turbine and on the interconnected system in various modes of operation through a model in the literature to design a single PID controller to guarantee stability and performance over the entire operating range. To ensure stability of the loop for as yet unanticipated operating situations, gain margin, and phase margin, constraints are imposed during the design of the controller. The new PID governor is compared with a high-order controller designed by the H/sub /spl infin// method.