Traditional PID Control Research Articles

Nonlinear PID Controller Parameters Optimization Using Improved Particle Swarm Optimization Algorithm for the CNC System

In this paper, a nonlinear PID (NLPID) controller is used to replace a traditional PID controller to overcome the influence of nonlinear factors in the computer numerical control (CNC) system. A particle swarm optimization based on a generalized opposition-based learning (G-PSO) algorithm is proposed to optimize the NLPID controller. The convergence speed and global optimization ability of the particle swarm optimization (PSO) algorithm are improved by introducing generalized opposition-based learning. The natural selection mutation is introduced into the G-PSO algorithm to further avoid the particles falling into local optimization. Different from the existing research, this paper designs a special fitness function according to the control objectives of improving system response speed and suppressing overshoot. By comparing the differential evolution (DE) algorithm, the ant lion optimizer (ALO) and the genetic algorithm (GA) through simulation, it is proven that the G-PSO algorithm has a faster convergence speed and better global optimization ability. Compared to Fuzzy PID and MRAC PID, G-PSO NLPID is shown to be more suitable for CNC systems. Additionally, it is proven through experiments that the rise time and settling time of the NLPID controller optimized by the G-PSO algorithm are 22.22% and 24.52% faster, respectively, than the traditional PID controller, and the system overshoot is successfully suppressed.

Sparrow search algorithm applied to temperature control in PEM fuel cell systems

Working temperature is a critical parameter that influences the performance of proton exchange membrane fuel cells (PEMFCs). Appropriate thermal management can improve the efficiency of PEMFCs and prevent irreversible internal damage such as membrane degradation. To solve the slow response and poor dynamic performance of traditional temperature control strategies, the dynamic temperature mode of PEMFCs is established and a temperature control strategy based on the sparrow search algorithm-proportional integral differential (SSA-PID) is proposed in this study. The performance of the SSA-PID temperature control is verified by the simulations of current step change, vehicle dynamic load, and working parameter variation. The results show that the proposed method has the advantages of fast convergence, better dynamic performance, and anti-disturbance ability. The maximum power density of the SSA-PID temperature controller is 6.58% and 12.94% higher than GA (genetic algorithm)-PID and traditional PID controllers, respectively. Moreover, the proposed method can ensure temperature fluctuations within 0.5 K under dynamic disturbance.

Research on Active Disturbance Rejection Thrust Stability Control of Permanent Magnet Reluctance Linear Motor

Permanent magnet reluctance linear motor (PMRLM) has high thrust density, simple and reliable structure due to the structure of permanent magnet and current double excitation. It is suitable for all kinds of large factories and logistics sorting centres. However, the existence of double salient pole structure and permanent magnet flux harmonic, the thrust pulsation of the motor is very large. In view of the above problems, the mathematical model of PMRLM is established, and the ADRC technology is proposed to be introduced into the thrust stability control. By building a simulation platform and comparing with the traditional PID control, the results show that the ADRC effectively reduces the thrust pulsation to a certain extent, and has good robustness and anti-interference.

Optimized PIDD2 controller for AVR systems regarding robustness

The novel method for tuning proportional-integral-derivative controller with second order derivative term (PIDD2) for the automatic voltage regulator (AVR) of synchronous generator is presented. The design method minimizes load disturbance response regarding constraints on sensitivity functions: maximum sensitivity Ms, maximum complementary sensitivity Mp and measurement noise sensitivity Mn. Simple solution is obtained by shifting PIDD2 controller in a new parameter space and by employing an iterative procedure. In each iteration the five controller parameters are obtained by solving the constrained optimization problem concerning sensitivities Ms and Mn for the fixed value of controller zeros damping factor ξ. The robustness index Mp is calculated subsequently. In that manner, maximum of complementary sensitivity Mp is met by means of auxiliary parameter ξ. The performance improvements are achieved in comparison with traditional PID controller, while robustness is retained. Further, the comparison is made with an optimization method using amplitude optimum principle that also minimizes load disturbance response with different optimality criterion. Experiments are carried out on a small lab synchronous generator setup in order to demonstrate practical potential in real noisy environment of the proposed method and to justify the increased order of AVR controller.

Research on Position Control of an Electro–Hydraulic Servo Closed Pump Control System

A control strategy combining adaptive backstep sliding mode control and dead-zone inverse compensation control in a series was proposed to solve the problem of system parameter uncertainty and system dead-zone in the process of position control in an electro–hydraulic servo closed pump control system. Firstly, an adaptive backstepping sliding mode controller was designed by introducing the sliding mode control principle. Secondly, the smooth dead-zone inverse function was constructed by using the smooth continuous index function to design the dead-zone inverse compensation controller, which is combined with the adaptive sliding mode controller to form a series controller. Finally, the feasibility of the controller was verified by using the pump control servo system of a lithium battery pole strip mill. The experimental results show that, compared with traditional PID control, the control strategy displayed no excessive overshoot before the steady state, the steady-state control accuracy could reach ±0.002 mm, and the time required to reach the steady state was 1 s to 2 s shorter, which shows this method’s ability to effectively improve the position control accuracy of the pump control system, as well as its dynamic response performance.

Neural Network Based Model Predictive Control for a Quadrotor UAV

A dynamic model that considers both linear and complex nonlinear effects extensively benefits the model-based controller development. However, predicting a detailed aerodynamic model with good accuracy for unmanned aerial vehicles (UAVs) is challenging due to their irregular shape and low Reynolds number behavior. This work proposes an approach to model the full translational dynamics of a quadrotor UAV by a feedforward neural network, which is adopted as the prediction model in a model predictive controller (MPC) for precise position control. The raw flight data are collected by tracking various pre-designed trajectories with PX4 autopilot. The neural network model is trained to predict the linear accelerations from the flight log. The neural network-based model predictive controller is then implemented with the automatic control and dynamic optimization toolkit (ACADO) to achieve real-time online optimization. Software in the loop (SITL) simulation and indoor flight experiments are conducted to verify the controller performance. The results indicate that the proposed controller leads to a 40% reduction in the average trajectory tracking error compared to the traditional PID controller.

Fuzzy fractional-order PID control for heat exchanger

Fractions have recently become widespread in technology and science. The current research looked at a fractional-order fuzzy PID controller. Furthermore, the tubular heat exchanger procedure aids in the investigation of controller behavior. The advantages of the recommended control strategy are highlighted to examine controller reactions under variations in variation and loads within the reference environment. There are additional comparisons with the conventional PID control scheme. The findings showed that the control approach performs better than a traditional PID control framework.

Workspace analysis and motion control strategy of robotic mine anchor drilling truck manipulator based on the WOA-FOPID algorithm

The manipulator is the key component of the anchor drilling robot to automatically complete the anchoring operation underground. Due to the complexity of its motion equation and the limitations of its control strategy, the real-time pose and the positioning accuracy of the manipulator are inferior, which seriously restricts the safety, efficiency, and speed of roadway excavation. In order to improve the positioning accuracy and realize the optimal efficiency of the manipulator, this article designs a manipulator structure with four degrees of freedom. With the help of the D-H method and the intelligent parameter setting method, this article carries out the basic theoretical research on the kinematics and the fractional order FOPID control algorithm of the manipulator of the mining roof bolter, and formulates a manipulator motion control strategy. At the same time, combined with numerical simulations and field experiments, we explore the robustness and control efficiency of the hydraulic system of the manipulator under the working conditions of a harsh environment and limited space, and reveal that the intelligent optimization algorithm can control the motion state of the manipulator more accurately and stably after the parameters of the fractional order FOPID controller are positively determined. This study effectively solved the dynamic model uncertainty caused by time-varying internal parameters and external loads of the hydraulic servo system, optimized and reconstructed the structure and motion coefficient parameters of the manipulator, and revealed the control mechanism of a precise spatial positioning and online trajectory planning of the hydraulic servo system of the manipulator. Compared with the traditional PID control algorithm, this algorithm has a faster response speed and better expected track tracking ability. This research lays a theoretical foundation for the precise positioning and automatic support of the manipulator, and also provides a reference for the design of similar motion control algorithms.

More Detailed Disturbance Measurement and Active Disturbance Rejection Altitude Control for a Flapping Wing Robot Under Internal and External Disturbances

With the goal of designing a biologically inspired robot that can hold a stable hover under internal and external disturbances. We designed a tailless Flapping-wing Micro Aerial Vehicle (FMAV) with onboard 3D velocity perception. In this way, the wind disturbance caused by the relative motion of the FMAV can be quantified in real time based on the established altitudinal dynamics model. For the rest of the total disturbance, an active disturbance rejection controller is proposed to estimate and suppress those disturbances. In comparison with the traditional PID controller, this proposed approach has been validated. The results show that, in the hovering flight with the internal unmodeled dynamics, the root-mean-square of height controlled is only 2.53 cm. Even with the different weights of loads mounting on the FMAV, the ascending trajectory of flights remains impressively consistent. In the forward flight with the external disturbance, the root-mean-square error of height controlled is 2.78 cm. When the FMAV flies over a ladder introducing an abrupt external disturbance, the maximum overshoot is only half of that controlled by the PID controller. To our best knowledge, this is the first demonstration of FMAVs with the capability of sensing motion-generated wind disturbance onboard and handling the internal and external disturbances in hover flight.

Generative adversarial interactive imitation learning for path following of autonomous underwater vehicle

Autonomous underwater vehicle (AUV) is playing a more and more important role in marine scientific research and resource exploration due to its flexibility. Recently, deep reinforcement learning (DRL) has been used to improve the autonomy of AUV. However, it is very time-consuming and even unpractical to define efficient reward functions for DRL to learn control policies in various tasks. In this paper, we implemented the generative adversarial imitation learning (GAIL) algorithm learning from demonstrated trajectories and proposed GA2IL learning from demonstrations and additional human rewards for AUV path following. We evaluated GAIL and our GA2IL method in a straight line following task and a sinusoids curve following task on the Gazebo platform extended to simulated underwater environments with AUV simulator of our lab. Both methods were compared to PPO—a classic traditional deep reinforcement learning from a predefined reward function, and a well-tuned PID controller. In addition, to evaluate the generalization of GAIL and our GA2IL method, we tested the trained control policies of the previous two tasks via GAIL and GA2IL in a new complex comb scan following task and a different sinusoids curve following task respectively. Our simulation results show AUV path following with GA2IL and GAIL can obtain a performance at a similar level to PPO and PID controller in both tasks. Moreover, GA2IL can generalize as well as PPO, adapting better to complex and different tasks than traditional PID controller.

Improving trajectory tracking performance of autonomous farming vehicles using adaptive extended state observer and finite-time technique

The ability of the autonomous farming vehicle to accurately and quickly track complex reference trajectories in the field is critical to crop yields and farmer incomes. The novelty of this paper is to design a new adaptive finite-time trajectory tracking control strategy with an adaptive extended state observer to improve the autonomous farming vehicle’s trajectory tracking accuracy and convergence speed in the complex farm operating environment. The adaptive extended state observer is constructed to deal with the slip disturbance and parameter uncertainty to improve the tracking accuracy. The finite-time control strategy is adopted to improve the convergence speed of the trajectory tracking of the autonomous farming vehicle. The effectiveness and advantages of the proposed control strategy are verified by comparing it with the active disturbance rejection control and traditional PID control strategies under MATLAB/Simulink environment.

Application of Boiler Optimization Monitoring System Based on Embedded Internet of Things

In order to study the deficiency of tuning PID controller in traditional boiler combustion air system, this paper proposes an embedded Internet of things monitoring system and puts forward a method of fuzzy PID controller with the help of nonlinear optimization algorithm to reasonably set the initial value of control parameters. Through the simulation comparison with the traditional engineering tuning PID controller, it is found that when the simulation process is t = 2000 s, the step disturbance with amplitude of 0.1 is added to the system input, and there will be slight disturbance in the combustion process. Through fuzzy PID and strong robustness support, the modeling error of the monitoring system model can be controlled between 10%∼25%, which proves that the nonlinear optimized PID has strong anti-interference and can meet the process requirements of the system.

Model reference adaptive control with adjustable gain for piezoelectric actuator

In this paper, a model reference adaptive control with adjustable gain is investigated to reduce the nonlinear hysteresis effect of piezoelectric actuator. Firstly, the unknown system dynamics of piezoelectric actuator are described based on Bouc-Wen model. By linearizing the nonlinear hysteresis effect, the corresponding reference model is designed to track the output error. Then, the stability and robustness of the model reference adaptive control system is ensured by the Lyapunov stability theorem. In addition, an iteration adjustment rule is proposed to improve the convergence rate of the tracking error. Finally, the simulation results of the proposed control are compared with the traditional PID control and other model reference adaptive control. The results demonstrate that the proposed method has a faster convergence speed and smaller error range.

Implementation of a multi-setpoint strategy for fire-tube boilers utilized in food and beverage industry: Estimating the fuel saving potential

The present work aims at estimating the fuel saving potential of implementing a multi-setpoint control strategy for a fire-tube boiler that addresses a steam demand with a notable (daily) variation in the requested pressure level. The daily steam consumption profile of an Italian cheese factory is accordingly considered as the case study. Next, a dynamic PID controller is developed and tuned, aiming at enhancing the boiler’s controllability at partial load conditions (in comparison with a traditional PID controller). In the next step, a multi-setpoint strategy is implemented, in which the pressure setpoint (following the desired pressure profile) is dynamically modified. The yearly fuel consumption of the boiler is then determined and is compared with the consumed fuel while employing the currently utilized constant-setpoint approach. The obtained results demonstrate that employing the proposed strategy results in reducing the yearly fuel consumption by 4.5%. The approach that is proposed in the present study can be employed by many steam end-users in the food and beverage industry, specifically in Italy, whose needed pressure level is highly variable.

Design of fractional-order NPID controller for the NPK model of advanced nuclear reactor

Advanced heavy water reactor (AHWR) is a type of advanced nuclear reactor which is the most efficient nuclear fission reactor for power generation due to the large thorium reserve. Its safe, stable, and efficient operations are critical for the revival of the fission energy sector. Incorporation of a robust control scheme into the nuclear reactor model is required for proper trajectory tracking of demand power. The AHWRs are coupled, significantly nonlinear, and multi-input multi-output (MIMO) systems. External disruptions and time-varying characteristics harm the systems' performance. As a result, the controller built for these systems must be able to deal with the complexity, which is most challenging for control engineers. A fractional-order nonlinear proportional, integral, and derivative (FONPID) control method is presented in this study for normalized power distribution management of the AHWR using normalized point kinetic equations (NPKEs) for trajectory tracking, disturbance rejection, and noise suppression tasks to improve the output power. All controller settings are fine-tuned using a genetic algorithm (GA) with the sum integral of time and square error (ITSE). The suggested FONPID controllers' performance is compared to its integer-order control structure, i.e., NPID and classical PID control structure. External disturbances at controller output and random noise at the sensor output are tested for resilience to establish the usefulness of the suggested control methods. The simulation results showcased that the proposed FONPID controller outperforms its integer-order (IO) counterpart as well as the traditional PID controller.

Performance Analysis of Heat Exchanger System Using Deep Learning Controller

Conventional PID controllers have utilised in most of the process industries. Despite being the most used controller, the traditional PID controller suffers from several disadvantages. Due to rapid development in the field of the process control system, various controllers have been developed that try to overcome the limitations of the PID controller. In this paper, a heat exchanger system has been simulated, and the generated data has been used to train a deep learning-based controller using Backpropagation. The obtained results are compared with the conventional controller on several metrics, including time response, performance indices, frequency response etc. The proposed model outperforms the conventional controller on all the evaluation metrics.

Application of Artificial Neural Networks in Chemical Process Control

An important data-driven model is the artificial neural network. Artificial neural networks have been widely used in many domains of chemical processes due to its robustness, fault tolerance, self-adaptive capability, and self-learning ability. For the chemical process with nonlinearity and strong coupling, artificial neural networks can model and control the process well and make up for the lack of traditional PID control technology. As a result, ANN has emerged as a significant positive trend for chemical process control. In this paper, the principle, development history, and common structure of artificial neural networks are first outlined. Then the role of artificial neural networks in chemical process control is introduced in three aspects: improved PID control, improved model predictive control, and for hybrid models. The important effect of artificial neural networks in chemical process control is reflected by comparison. Finally, it is proposed that chemical process control can be more developed by applying more deep learning algorithms and developing multiple neural networks and hybrid models in chemical process control.

Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks.

Multimodal orbital angular momentum is a research hotspot in the field of electromagnetic wave communication. How to accurately detect and identify multimodal orbital angular momentum data is a current academic problem. Based on the theory of mechanically reconfigurable arrays and neural networks, the purity, detection method, and transmission and reception of orbital angular momentum vortex waves are modeled in this paper. Through the network identification of the dynamic model of the three-degree-of-freedom reconfigurable manipulator, the paper takes the identification result and the control input of the single neuron PID as the input of the system control torque of the manipulator and realizes the reconfigurable manipulator. In the simulation process, the local approximation effect of the nonlinear control system used is very ideal. The single neuron PID controller overcomes the shortcomings of time-consuming and unsatisfactory control accuracy caused by the constant parameter of the traditional PID controller and realizes the circular loop. On the other hand, at the point of interest of the human eye, its resolution value is the largest, and its value gradually decreases as the distance from the pit increases. The experimental results show that the three-transmitting and three-receiving orbital angular momentum vortex wave transceiver system based on the mechanically reconfigurable array and neural network theory is relatively complete, and the transmission coefficient between the same modes reaches 0.827, which is much higher than that between different modes. On this basis, the modal purity, detection method, and reception of orbital angular momentum are studied accordingly. At the same time, the damage to the microscopic particles can be greatly reduced. At the same time, the information delay is reduced to 8.25%, which effectively improves the isolation characteristics of different modal orbital angular momentum channels and promotes the communication transmission of multimodal signals.

Model‐based robust control design and experimental validation of collaborative industrial robot system with uncertainty

AbstractBased on the traditional PID control and robust control algorithm, a novel practical robust control method is designed for the 6‐DOF collaborative industrial robot with uncertainty. The proposed algorithm consists of a robust term and a model‐based PD control term, which we call MPDP controller. It is demonstrated by Lyapunov theoretical analysis that the algorithm is able to guarantee uniform boundedness and uniform ultimate boundedness of the system. Simulations and experiments show the good performance of MPDP control in a robot with smaller steady‐state tracking errors and better robustness compared to PID controllers.

Model-based control of axisymmetric hexarot parallel manipulators

In this research, the control of a hexarot parallel mechanism is investigated. Parallel kinematic simulators or machine tools manipulators suffer from many problems to have a good motion tracking. The inverse kinematics and dynamic formulation that have been developed recently via the Newton–Euler method for a general hexarot mechanism is briefly presented. PID controller and fuzzy incremental controller have been designed and developed as an alternative option of the model-based controller. The efficiency of the model-based controller compared to other controllers has been evaluated. An important contribution of this paper is an appropriate control strategy that improves the tracking performance utilizing the cost effective joint-space sensor. The control scheme development starts with the robust design of the controller-observer of the single actuators. The single objective genetic algorithm is used to tune the controller gains to achieve the least tracking error of joint position. This, moreover, is improved by a centralized feedforward dynamics compensation of the gravity. The validity of the developed model is checked under Simulink environment of MATLAB software, and it is found that the developed model-based controller has the preferable tracking performance rather than the traditional PID and fuzzy incremental controllers The proposed model-based controller is able to decrease the motion tracking error from 55, 20 and 12 to 4 (deg2) for spiral motion compared with PID, FIC and observer-PD, respectively.