PID Parameters Research Articles

Research on algorithms of system vibration in yaw process of large wind turbine

Aiming at the problem of system vibration during yawing of large wind turbines, the mathematical model is obtained by simulating the structure model of the yawing system, which is then represented by a general free vibration equation. The system vibration equation is modeled and simulated by MATLAB/Simulink. The model is controlled by PID, Fuzzy PID and Optimal Fuzzy PID intelligent algorithm, and the control result is obtained. By comparing the control results between different algorithms, it can be shown that although the PID algorithm and the Fuzzy PID algorithm can obviously and effectively control the vibration model of the yaw system, and the control effect of the Fuzzy PID algorithm is better than that of the PID algorithm, the PID parameters obtained by the “trial and compact method” are not the optimal parameters, which will affect the control effect of the Fuzzy PID. The Optimal Fuzzy PID algorithm can automatically optimize by MATLAB program to obtain the optimal PID parameters and achieve the optimal control effect.

A search and rescue robot device realization based on ROS operating system combined with visual algorithm

Aiming at the problem of low accuracy of intelligent devices to find missing people, a search and rescue robot device based on ROS intelligent navigation system was proposed. SolidWorks was used to improve the new mechanical structure that can switch autonomously to different terrain and combined with Yolov5 deep learning algorithm to complete face recognition. Using the stm32 single-chip microcomputer, we adjusted the cascade PID parameters of the motor and simulation to realize the omnidirectional movement control of the robot. Through the analysis and verification of the experimental data, the adaptive ability of the improved robot platform to the terrain was significantly improved. The method can accurately complete the task of face recognition and autonomous navigation, which is practical and improves the accuracy of finding the missing people.

The Efficiency of an Optimized PID Controller Based on Ant Colony Algorithm (ACO-PID) for the Position Control of a Multi-articulated System

In this article, a robot manipulator is controlled by the PID controller in a closed loop system with unit feedback. The difficulty of using the controller is parameter tuning, because the tuning parameters still use the trial and error method to find the PID parameter constants, namely Proportional Gain (Kp), Integral Gain (Ki) and Derivative Gain (Kd). In this case the Ant colony Optimization algorithm (ACO) is used to find the best gain parameters of the PID. The Ant algorithm is a method of combinatorial optimization, which utilizes the pattern of ants search for the shortest path from the nest to the place where the food is located, this concept is applied to tuning PID parameters by minimizing the objective function such that the robot manipulator has improved performance characteristics. This work uses the Matlab Simulink environment, First, after obtaining the system model, the ant colony algorithm is used to determine the proper coefficients 𝐾p, 𝐾i, and Kd in order to minimize the trajectory errors of the two joints of the robot manipulator. Then, the parameters will implement in the robot system. According to the results of the computer simulations, the proposed method (ACO-PID) gives a system that has a good performance compared with the classical PID.

Continuous Reactor Temperature Control with Optimized PID Parameters Based on Improved Sparrow Algorithm

To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of population diversity reduction and easy-to-fall-into local optimal solutions when the traditional sparrow algorithm is close to the global optimum. This differs from other improved algorithms by adding a new Gauss Cauchy mutation strategy at the end of each iteration without increasing the time complexity of the algorithm. By introducing tent mapping in the sparrow algorithm to initialize the population, the population diversity and global search ability are improved; the golden partition coefficient is introduced in the explorer position update process to expand the search space and balance the relationship between search and exploitation; the Gauss Cauchy mutation strategy is used to enhance the ability of local minimum value search and jumping out of local optimum. Compared with the four existing classical algorithms, ISSA has improved the convergence speed, global search ability and the ability to jump out of local optimum. The proposed algorithm is combined with PID control to design an ISSA-PID temperature controller, which is simulated on a continuous reactor temperature model identified by modeling. The results show that the proposed method improves the transient and steady-state performance of the reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a semi-physical experimental platform to verify its feasibility.

Stability Optimization Simulation of Industrial Robot in ROS Environment

This paper studies the end actuator motion state of an RRR industrial robot, and gives a bottom up decision-making scheme based on PID algorithm. Under the background of complex industrial control, any environmental factors will have a deep effect on the accuracy. The physical representation of the accuracy of the end effector is the electrical signal. Foscus on the signal difference from joint to joint, based on the ROS and gazebo, the simulation of the RRR mechanical arm model can theoretically and intuitively Reflect the differences. To minimize the error, proportional–integral–derivative controller will be sued in this model and then Adjust PID parameters of each controller. Assume that the external interference is a linear superposition of impulse signals. The impulse response which the model makes should return to normal as soon as possible. In this way, the purpose of anti-interference is achieved. At the same time, it focuses on solving the motion state of industrial manipulator. For general industrial robots, its actuator, a gripper, should have the function of reaching any position and taking any direction in a certain space area. In other words, if a reference frame o 'is fixed on the center of the hand claw, the robot can send o' to any direction at any position in the space area. The position and direction of the gripper are determined by the amount of motion from the base to the gripper pairs. In addition, with the rapid development of the Internet, image has become a common form of information. This paper will use image recognition technology to provide the required detection data and reference values for the PID controller. Thus, the workspace of the industrial robot can be reflected more completely.

Toward Ultrasonic Wire Bonding for High Power Device: A Vector Based Resonant Frequency Tracking and Constant Amplitude Control

The electrical packaging for high power devices such as IGBT or MOSFET is achieved by heavy aluminum ultrasonic wire bonding. In this process, the amplitude stability of ultrasonic transducer determines the bonding quality, which demands more accuracy in resonant frequency tracking (RFT) and constant amplitude control (CAC). In this investigation, we proposed a vector based resonant frequency (VRFT) tracking and vector based constant amplitude control (VCAC) to control the ultrasonic transducer. The current of dynamic branch was conducted by calculating the voltage and current vector of ultrasonic transducer. Small signal model of ultrasonic transducer was established and applied into RFT and CAC. A control strategy of fuzzy self-tuning PID closed loop was utilized to RFT and CAC. The PID parameters was calculated by the linear control theories and modified by the fuzzy controller which improved the vibration stability of the transducer. A prototype of 100Watt/60kHz ultrasonic driver was self-developed to verify the VRFT and VCAC. It demonstrated that the locked frequency of VRFT can be controlled within less than 0.04 millisecond when staring from the swept frequency, and the vibration response is fast with the varying bonding pressure. The amplitude of fuzzy PID VCAC is more stable and robust compared to the classical PID control, which improved the bonded joint quality in heavy aluminum wire packaging. Note to Practitioners—This article was motivated by the reliability and efficiency of IGBT/ MOSFET device packaging through ultrasonic aluminum wire bonding. This bonding process requires more amplitude stability of the ultrasonic transducer to achieve acceptable bonded quality. Currently, the resonant frequency tracking (RFT) by classical PID control is in slow speed, and the amplitude of the transducer cannot be controlled constantly due to the inherent static capacitor of the transducer. These issues for the ultrasonic control are originated from the engineer’s experience or simple heuristic rule instead of advanced approaches. To improve the vibration performance, we proposed a vector based model consisting of the relationship the dynamic branch current and the total current of the transducer, to control the RFT and constant amplitude control (CAC). Moreover, the vector based is utilized into the fuzzy PID control, where the calculation is reduced by area coefficient defuzzy. The verification experiments prove that the response time of the proposed vector based RFT and CAC, and the bonded joint quality is optimized obviously. It is noted that in our control strategy, the RMS value, the phase of voltage and current of the transducer are acquired by DSP processor, and the calculation of fuzzy PID controller is not complicated, which is feasible to implement in application. This significantly benefits the demand of accurate control to the ultrasonic power in electronic packaging.

Robust-PID controller design for magnetic levitation system using parameter space approach

Recently, implementation of air magnetic suspension systems has become more challenging due to the growing demand for lightweight technologies, comfortable cabins, vehicle safety stability, and pollution control. Magnetic levitation, or Maglev, is the method of propelling a target into the air by adjusting different magnetic forces. The absence of contact and the avoidance of wear and friction phenomena are key considerations in the applications of magnetic levitation technology. Due to the high nonlinearity in the modelling process of such kind of systems, the stabilizing of magnetic levitation has been considered as a challenging task for many researchers in control engineering sector. The computation of all stabilizing PID gains controllers for the magnetic levitation benchmark ED-4810 (Maglev) is demonstrated in this paper using two different scenarios. In the first one, the tuning parameters of the classical PID controller (KP, KI, and KD) are assumed to be the uncertain parameters. The second scenario demonstrates the uncertainty in the transfer function of the system by using the resistance and the inductance as uncertain parameters. The characteristic polynomial of the linearized uncertain model is shown to be an unstable affine polynomial using the Zero Exclusion Theorem and the singular frequencies technique. The parameter space approach is used to illustrate the values of all PID parameters in order to achieve robust stability in the two scenarios. The effectiveness of the presented graphical technique has been verified through MATLAB simulation to obtain robust stability for magnetic levitation systems.

Theoretical and Experimental Investigations on High-Precision Micro-Low-Gravity Simulation Technology for Lunar Mobile Vehicle

With the development of space technology, the functions of lunar vehicles are constantly enriched, and the structure is constantly complicated, which puts forward more stringent requirements for its ground micro-low-gravity simulation test technology. This paper puts forward a high-precision and high-dynamic landing buffer test method based on the principle of magnetic quasi-zero stiffness. Firstly, the micro-low-gravity simulation system for the lunar vehicle was designed. The dynamic model of the system and a position control method based on fuzzy PID parameter tuning were established. Then, the dynamic characteristics of the system were analyzed through joint simulation. At last, a prototype of the lunar vehicle's vertical constant force support system was built, and a micro-low-gravity landing buffer test was carried out. The results show that the simulation results were in good agreement with the test results. The sensitivity of the system was better than 0.1%, and the constant force deviation was 0.1% under landing impact conditions. The new method and idea are put forward to improve the micro-low-gravity simulation technology of lunar vehicles.

Examination of the Perspective Regulator of Civil Quad-Rotor UAV Relay on F-PID Controller

In the operation of farming processes, the limitation of the civilian quadrotor UAV transport and the features of its powerful collar would yield more significant interference to the aviation control version. Mainly utilize traditional PID regulator, but the mood adjustment of the murmur will have a big overshoot. This article sets on the application of fuzzy controllers in courteous four-rotor UAVs for this case. By creating and improving the fuzzy rule tableland, rather than changing PID parameters often back. Additional sound control of energy outcome to support system steady-state implementation. By utilizing MATLAB to imitate flying perspective and compare with the simulation effects of the traditional algorithm of PID control. The outcomes numerical results indicate that the process may enhance PID capability and decrease the fluctuation of aviation perspective control throughout process.

System State Determination using Database-Driven Control Data

The database-driven (DD) PID control is a method of using a database to adjust PID parameters. System input/output data are stored in the database. The DD-PID control can be expected to expand the technology to smart systems such as state determination. In this paper, a state value determination method using the operation results of the DD-PID control is newly proposed. The proposed method has the same data structure as the DD-PID control and can determine the current system state value based on a DD approach. In addition, the proposed method improves accuracy by using logit transformations near the upper and lower limits of the system state value range. The effectiveness of the proposed method is verified by a numerical simulation.

CMACメモリによるデータベース駆動型PID制御系の実現

As a nonlinear control approach, a database-driven PID (DD-PID) controller has been proposed that uses distance calculations of an initial database to tune PID parameters. However, sorting operations are required in extracting neighborhood data from the database, which imposes a large computational load. Therefore, it is impossible to finish this computation within a restricted sampling period in control systems with fast response time and insufficient memory capacity. On the other hand, the cerebellar model articulation controller (CMAC) has advantages for these problems and has similar constructs to the DD approach. Therefore, this paper combines advantages of these two approaches, and proposes a CMAC-PID controller design based on off-line optimization of a database. Finally, a comparison with conventional off-line CMAC-PID controller and DD-PID controller through simulations are considered.

Intelligent Adaptive PID Control for the Shaft Speed of a Marine Electric Propulsion System Based on the Evidential Reasoning Rule

To precisely and timely control the shaft speed for a marine electric propulsion system under normal sea conditions, a new shaft speed control technique combining the evidential reasoning rule with the traditional PID controller was proposed in this study. First, an intelligent adaptive PID controller based on the evidential reasoning rule was designed for a marine electric propulsion system to obtain the PID parameters KP, KI, and KD. Then, a local iterative optimization strategy for model parameters was proposed. Furthermore, the parameters of the adaptive PID controller model were optimized in real time by using the sequential linear programming algorithm, which enabled the adaptive adjustment of KP, KI, and KD. Finally, the performance of the adaptive PID controller regarding the shaft speed control was compared with that of other controllers. The results showed that the adaptive PID controller designed in this study had better control performance, and the shaft speed control method based on the adaptive PID controller could better control the shaft speed of the marine electric propulsion system.

Research on the Control Performance of Depth-Fixed Motion of Underwater Vehicle Based on Fuzzy-PID

The control performance of the fixed-depth motion is related to the performance of the underwater vehicle. Due to the complex and changing environment underwater and various potential risks, the variety of underwater operations, and the variability of the structural parameters and environmental parameters of the underwater vehicle, the control performance is compromised when performing constant depth motion. It is of much significance to study the control method of the fixed-depth motion to improve the performance of the underwater vehicle. The transfer function of underwater vehicle’s depth-setting motion was established, and the fuzzy-PID controller was established for simulating the control of depth-setting movements of underwater vehicles. The interaction law between PID initial parameters and controller performance was studied, and the interaction law between the change of underwater vehicle mass and the hydrodynamic coefficient and controller performance was also studied. The results show that the fuzzy-PID controller can realize the control of the underwater vehicle’s depth-setting motion, and the control effect was independent of the initial PID parameters, thus avoiding the dependence of the formulation of PID parameters on manual control experience. When the mass and hydrodynamic coefficient of the underwater vehicle change, the fuzzy-PID controller can still maintain good control performance and has strong adaptive ability.

Active tension control of the conical winding system based on the neural network control algorithm of radial-basis functions

A conical winding formation and tension control system was proposed in the doubling operation based on the yarn guide mode of a single spindle in this study. Conical winding formation realized the radial unwinding of wound package. An overfeed mechanism was introduced to achieve closed-loop control of yarn tension. The overfeed wheel was driven by a brushless Direct Current motor. The tension control system combined a Proportion Integration Differentiation controller with a radial-basis-function neural network, whose purpose was to meet the control requirements of the brushless DC motor. This system consisted of three main steps: Firstly, the radial-basis-function neural network was used to identify the system online. Secondly, the gradient descent method was used to adjust the node weight, center vector, and baseband width. Finally, incremental PID parameters online were adjusted according to the identified Jacobian information. A mathematical model of a control system was established in Matrix Laboratory. An experimental platform was designed for doubling winder to compare the control effects of Radial-basis-function-PID with traditional PID. The simulation results showed that the RBF-PID had a smaller overshoot of yarn tension, shorter adjustment time, and smaller steady-state error compared with the traditional PID controller in doubling operation by simulating the mathematical model. Experimental results showed the RBF-PID controller had good performance and stability and could be applied to yarns with different average linear velocity, yarn counts and strands . The yarn tension fluctuation will not exceed ±3% of the target value when the experimental materials and the cone angles are unchanged.

A segmented optimal PID method to consider both regulation performance and damping characteristic of hydroelectric power system

Hydropower has become the main force of grid frequency regulation due to its regulation flexibility and rapid response characteristics. However, when the operating conditions are changed, the continued pursuit of a faster response speed deteriorates the damping characteristics of the system with wider water-head, causing low-frequency oscillations, threatening the power station's safety and stability and the power grid. In this study, an improved quantized damping method is applied. The settling time and damping coefficient variation under different PID parameters and operating conditions are recorded, revealing the contradiction between regulation performance and damping characteristics. Then the segmented optimal PID controller is proposed to balance this contradiction. The twin-delayed deep deterministic policy gradient learning algorithm enables the controller to find the optimal policy online. With the settling time-damping threshold control strategy, the controller optimizes parameters according to operating conditions, and changes parameters when reaching the threshold. The results show that, compared with using a set of PID parameters, the damping of the hydropower system is increased by 0.35 from negative to positive. In contrast, the settling time increases by 11.63s within limits. The proposed controller ensures the safe and coordinated operation of the power grid and the power station of the hydroelectric power system with a wide water-head.

Improved control strategies for the environment within cell culture bioreactors

This paper describes the development of improved control strategies for the standard environmental conditions in a fed-batch bioreactor used for monoclonal antibody cell culture. The consequences of relying on fixed parameter PID based controllers are considered and poor performance is demonstrated as a consequence of non-linearity and loop interactions. The benefits from adopting a more sophisticated control strategy are considered. Model Predictive Control (MPC) relies on a process model that can be identified from small system perturbations. It considers the predicted longer-term response and consequently can deliver improved control and satisfy user defined constraints. Results from experimental trials demonstrate the capability of MPC and the merits are discussed with regards to industrial application.

Research on the optimization strategy of lithium battery electrode correction system based on algorithm fusion

In the control system of a lithium battery rolling mill, the correction system was crucial. This was because the correction system had a significant impact on the performance of the lithium battery rolling mill, including high precision and efficient rolling quality. However, the non-linearity of the correction system and the uncertainty of the correction system made it a challenging problem to achieve a high precision correction control. The contribution and innovation of this paper was a genetic fuzzy PID control strategy based on Kalman filter, which was proposed and applied to the control of lithium battery rolling mill correction technology. In order to achieve intelligent control of a high-precision electrode rolling mill correction system, an algorithm fusion control scheme was proposed. Firstly, a novel and detailed correction system model was presented. Next, the initial PID parameters of the correction were optimized by means of a genetic algorithm so that the PID parameters could be adapted to the correction control process and then optimized again by adding an extended Kalman filter. Finally, the lithium battery rolling mill correction control system was validated, tested and commissioned in the field. The results showed that the designed algorithm could meet the working requirements of the lithium battery rolling mill and that it improved the accuracy of the correction system. In the actual lithium battery rolling mill production process, the algorithm was compared with a conventional PID. Compared with the common single algorithm, the fusion algorithm proposed in this paper was a complete set of high precision correction control system algorithm to solve the high precision problem faced by the correction system in the actual lithium battery rolling mill correction system.

Design and Experimental Characterization of Artificial Neural Network Controller for a Lower Limb Robotic Exoskeleton

This study aims to develop a lower limb robotic exoskeleton with the use of artificial neural networks for the purpose of rehabilitation. First, the PID control with iterative learning controller is used to test the proposed lower limb robotic exoskeleton robot (LLRER). Although the hip part using the flat brushless DC motors actuation has good tracking results, the knee part using the pneumatic actuated muscle (PAM) actuation cannot perform very well. Second, to compensate this nonlinearity of PAM actuation, the artificial neural network (ANN) feedforward control based on the inverse model trained in advance are used to compensate the nonlinearity of the PAM. Third, a particle swarm optimization (PSO) is used to optimize the PID parameters based on the ANN-feedforward architecture. The developed controller can complete the tracking of one gait cycle within 3.6 s for the knee joint. Among the three controllers, the controller of the ANN-feedforward with PID control (PSO tuned) performs the best, even when the LLRER is worn by the user and the tracking performance is still very good. The average Mean Absolute Error (MAE) of the left knee joint is 1.658 degrees and the average MAE of the right knee joint is 1.392 degrees. In the rehabilitation tests, the controller of ANN-feedforward with PID control is found to be suitable and its versatility for different walking gaits is verified during human tests. The establishment of its inverse model does not need to use complex mathematical formulas and parameters for modeling. Moreover, this study introduces the PSO to search for the optimal parameters of the PID. The architecture diagram and the control signal given by the ANN compensation with the PID control can reduce the error very well.

Research on PID Controller of Excavator Electro-Hydraulic System Based on Improved Differential Evolution

An electrical hydraulic control system (electro-hydraulic system) is thought to be a key component in excavator operation systems. Control methods with fixed parameters may not yield optimal system performances because a hydraulic system has various nonlinear uncertainties due to the leakage and compressibility of the fluid medium. Hence, a novel PID controller based on improved differential evolution (IDE) is introduced to excavator electro-hydraulic systems for interconnected hydraulic systems. The proposed algorithm not only adjusts the PID parameters of the different working conditions but also adjusts the scaling factor and crossover probability. Then, the proposed PID controller based on IDE and the excavator bucket control system are modeled and simulated on the MATLAB simulation platform. The simulation results demonstrate that the proposed controller has better performance in settling time, rise time, and convergence speed compared to the PID controller based on standard differential evolution and the Ziegler–Nichols (ZN) PID controller with a novel object function. Eventually, the IDE-PID controller is assessed on a 23-ton excavator, and good transient behavior and trajectory accuracy are obtained in comparison to the SDE-PID controller.

Mercury fractionation - Problems in method application

Mercury (Hg) is a global pollutant with a negative effect on human and ecosystem health. Mercury is toxic in all forms. The toxicity, however, varies depending on the form of mercury, determining its physical and chemical properties. Therefore, knowledge on the chemical speciation of mercury is key for the understanding of its transport and transformations in the environment. Analysis of mercury speciation, however, is time-consuming and involves high risk of contamination. The mercury thermodesorption method offers many new possibilities. The main advantages of this method are identifying which groups of compounds are being transformed in the atmosphere, sediment and soil, suspended matter and plankton, and in organisms from different trophic levels. A great advantage of the method is also its application in mercury analyzers, where it is possible to control the heating and cooling temperatures of. The standardisation of fractionation nomenclature for all matrices (both biotic and abiotic) will be helpful in application of this mercury fractionation method too. It has also disadvantages, mostly in the technical preparation of the analyzer. The analyzer must be prepared for fractionation: setting the ventilator and adjusting the PID parameters so that the pre-set heating (t1) and combustion (t2) times reach the set value in the method program. Also, any modification of the heater forces a re-optimisation of the method with mercury standards, as certified reference materials for Hg fractionation in environmental matrices are not available. The HgF2 fraction cannot be used as the methylmercury concentration, which is undoubtedly the biggest drawback of this method.