Traditional Proportion Integration Differentiation Control Research Articles

Modeling and application research of absolute gravimeter driven by a V-shaped linear ultrasonic motor.

A V-shaped linear ultrasonic motor (LUSM), used as a driving element for the absolute gravimeter, generates structural vibrations when the motor starts and stops, which interfere with and influence the accurate measurement of the absolute gravimeter. In order to address this problem and provide better stability and measurement accuracy, this paper designs a fuzzy proportional integral differential (PID) controller and realizes the drive control of the motor on a field programmable gate array (FPGA) using the Verilog hardware description language (HDL). The advantages of the proposed controller are verified by comparing the simulation results with those of the traditional PID controller. First, the technical index of the LUSM as the driving element is studied through theoretical analysis based on the operation mechanism of the absolute gravimeter. Then, the relationship between the output performance and the input parameters (excitation voltage, preload, and driving frequency) of the motor in a vacuum/natural environment is experimentally investigated. Finally, an experimental system is constructed, and experiments are conducted to measure the acceleration of gravity and compare with the test results of the habitual FG-5 absolute gravimeter. The experimental results show that the output performance of the V-shaped LUSM meets the requirements of the driving element of the absolute gravimeter. The combination of the two control methods can realize the repeatable and stable operation of the system. Moreover, the motor with a mass of only 92g can output force directly without transmission components. The complexity and the weight of the system are significantly reduced, which has certain advantages.

A Deep Reinforcement Learning Approach to Injection Speed Control in Injection Molding Machines with Servomotor-Driven Constant Pump Hydraulic System

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller’s ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process.

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry.

On the use of PID control to improve the stability of the quad-rotor UAV

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

Design of Observer-Based Discrete-Type PID Control for Reconstructed Jump Model of Interconnected Power System Under Hybrid Attacks

In this article, observed-based discrete proportion-integration-differentiation (PID) control for multi-area interconnected power systems is addressed under hybrid attacks. Firstly, a reconstructed jump model for interconnected power systems is proposed by taking the circumstance of data packet loss into consideration, which caused by the stochastic transmission delay of sampled data. Then, different from the traditional continuous-type PID control, an observer-based discrete-type PID control strategy is designed for discrete-time power system. Besides, due to the sampled data may be tampered or shield in open communication networks, the hybrid attacks, i.e., deception attacks and denial-of-service attacks, with random manners are considered. Furthermore, an input-to-state stable criterion is derived by constructing appropriate Lyapunov-Krasovskii functionals. Finally, the discrete-type PID controllers and observer are achieved, and the validity of proposed approach are also demonstrated in the illustrative examples section.

Deep reinforcement learning method for turbofan engine acceleration optimization problem within full flight envelope

In order to solve the multidimensional restricted optimization problem of aeroengine acceleration process and improve the acceleration performance of full envelope, a design method of aeroengine full envelope acceleration controller based on deep reinforcement learning is proposed. Firstly, the thermo-aerodynamic principle of the engine acceleration process is analyzed, and the mathematical analytical model of the multidimensional restricted optimization problem of the turbofan engine acceleration process is established. On this basis, the design method of acceleration controller based on twin delayed deep deterministic policy gradient is studied. The proportional integral controller is designed to pursue the asymptotic stability at the acceleration destination and stable control is achieved through switching. Additionally, the flight envelope is divided into regions by clustering to reduce the span of data changes, so as to enhance the network convergence ability of deep reinforcement learning. Finally, based on the similarity conversion method of equal engine inlet total temperature, the acceleration controller of full flight envelope is realized. The digital simulation results demonstrate that compared with the traditional proportional integral differential controller, the acceleration controller based on twin delayed deep deterministic policy gradient shortens the acceleration time up to 48.33%, and the control effect in a large flight envelope (H=0∼10 km, Ma=0∼1.6) has been verified.

Improved active disturbance rejection controller for rotor system of magnetic levitation turbomachinery

<abstract> <p>The rotor of the magnetic suspension turbomachinery is supported by the magnetic suspension bearing without contact and mechanical friction, which directly drives the high-efficiency fluid impeller. It has the advantages of high efficiency, low noise, less fault and no lubrication. However, the system often has some unknown mutation, time variation, load perturbation and other un-certainties when working, and the traditional Proportion Integration Differentiation (PID) control strategy has great limitations to overcome the above disturbances. Therefore, this paper firstly establishes a mathematical model of the rotor of magnetic levitation turbomachinery. Then, a linear active disturbance rejection controller (LADRC) is presented, which can not only improve the above problems of PID control, but also avoid the complex parameter tuning process of traditional nonlinear active disturbance rejection control (ADRC). However, LADRC is easy to induce the overshoot of the system and cannot filter the given signal. On this basis, an improved LADRC with a fast-tracking differentiator (FTD) is proposed to arrange the transition process of input signals. The simulation results show that compared with the traditional PID controller and single LADRC, the improved linear active disturbance rejection control method with fast tracking differentiator (FTD-LADRC) can better suppress some unknown abrupt changes, time variation and other uncertainties of the electromagnetic bearing-rotor system. At the same time, the overshoot of the system is smaller, and the parameters are easy to be set, which is convenient for engineering application.</p> </abstract>

Control System of Liquid Fertilizer Variable-Rate Fertilization Based on Beetle Antennae Search Algorithm

In order to solve the problems of low precision of variable-rate fertilization and uneven fertilization flow of field liquid fertilizer applicator, a control system of variable-rate fertilization of liquid fertilizer based on beetle antennae search algorithm was proposed. First of all, this study established a mathematical model for the variable-rate fertilization control system of liquid fertilizer. Then, according to the control requirements, the search algorithm is used to optimize the three parameters of Proportion Integration Differentiation (PID). Finally, the response time and overshoot of the system are analyzed by software simulation, and the PID control based on beetle antennae search algorithm is compared and analyzed with fuzzy PID control and traditional PID control. The control effect of the control system is verified by a bench test. The results show that the actual response time of the variable-rate fertilization control system based on the beetle antennae search algorithm can reach 2 s, and the average relative error can reach 1.27%. Therefore, the control system of this study can achieve a better control effect, and the control method of this study provides a feasible scheme for the study of variable-rate fertilization.

Research on Adaptive Control of Air-Borne Bolting Rigs Based on Genetic Algorithm Optimization

Rotation speed and propulsive force are the two critical parameters in the work of the air-borne bolting rig. To address the problem that unreasonable rotation speed and propulsive force will induce the breakage of the drill pipe and the inability of the drill bit to cut coal adequately this paper proposes an adaptive control strategy for the air-borne bolting rig based on genetic algorithm optimization. Firstly, we obtain the corresponding coal hardness by the real-time acquisition of the working torque of the drill pipe. Then we calculate the reasonable rotation speed of the hydraulic motor and the propulsive force of the hydraulic cylinder on the coal of different hardness. Secondly, the genetic algorithm is applied to optimize the parameters of the PID (proportion integration differentiation) controller so that the system may attain the target value fast and reliably and achieve adaptive control. Finally, a simulation model of the slewing system and the propulsion system of the air-borne bolting rig are established in the AMESim hydraulic software, and the simulation tests were carried out under two distinct working conditions: single coal hardness and coal hardness of sudden change. The results indicate that the PID control strategy based on genetic algorithm optimization has a shorter response time, a smaller overshoot, and a lower steady-state error than the traditional PID control strategy.

Precision Control of Spraying Quantity Based on Linear Active Disturbance Rejection Control Method

Current methods to control the spraying quantity present several disadvantages, such as poor precision, a long adjustment time, and serious environmental pollution. In this paper, the flow control valve and the linear active disturbance controller (LADRC) were used to control the spraying quantity. Due to the disturbance characteristics in the spraying pipeline during the actual operation, the total disturbance was observed by a linear extended state observer (LESO). A 12 m commercial boom sprayer was used to carry out practical field operation tests after relevant intelligent transformation. The experimental results showed that the LADRC controller adopted in this paper can significantly suppress the disturbance in practical operation under three different operating speeds. Compared with the traditional proportional–integral–differential controller (PID) and an improved PID controller, the response speed of the proposed controller improved by approximately 3~5 s, and the steady-state error accuracy improved by approximately 2~9%.

Modeling and adaptive controlling of cable‐drogue docking system for autonomous underwater vehicles

SummaryIn this article, a novel cable‐drogue docking system is proposed between an autonomous underwater vehicle (AUV) and one mobile underwater platform, to increase the docking safety and reduce the negative influences of turbulences around the platform shell on the AUV. The mathematical model of the cable‐drogue system and an radial basis function neural network (RBFNN)‐based Q‐learning proportional‐integral‐differential (PID) controller are developed for the system characteristics analysis and the stabilized control. First, the cable‐drogue's mathematical model is established as a link‐joint‐connected system, whose kinematic and dynamic equations are derived subjected to the hydrodynamic forces on system. One numerical solution procedure is also presented to obtain the motion state of the cable‐drogue system. Next, considering the current interferences on the cable‐drogue system, a cross rudder is added on the drogue to enhance the robustness. An RBFNN‐based Q‐learning PID controller is proposed to improve the control performances, in which the control parameters are adaptively optimized by an Q‐learning neural network. Finally, numerical simulations are made to study the steady‐state and dynamic characteristics of the cable‐drogue system in ocean environment, and to investigate the performances of both the traditional PID controller and the proposed RBFNN‐based Q‐learning PID controller in unknown dynamical system. Simulation results show the effectiveness of the kinematic and dynamic equations of the cable‐drogue system and the proposed controller in this article.

Optimization of Bridge Crane Control System Using Fuzzy PID Control and Speed Control of Frequency Converter

Objective: As a carrier, bridge cranes are widely used in port cargo handling. Due to the structure of the crane itself, the load inevitably produces displacement deviation and lifting weight swing. Therefore, the optimization investigation of the bridge crane control system is carried out to guarantee the fast and secure operation of the crane. Method: According to the characteristics of bridge cranes, combined with the experience of on-site operating staff, the fuzzy rules that meet the working conditions of the site are designed by using the advantages of fuzzy PID (Proportion-Integration-Differentiation) control algorithm without relying on an accurate mathematical model, to set the parameters of the fuzzy PID controller. The speed control technique of frequency converter is adopted to design the system to improve the safety of the overall system. Results: The results show that the adjustment time of the swing angle of crane weight of the traditional PID control requires 6.79s, and the maximum swing angle of crane weight is 0.039rad, and the adjustment time under the fuzzy control is 4.89s, and the swing angle of crane weight is 0.013rad. The fuzzy PID controller surpasses the traditional PID controller in the positioning accuracy. It is not sensitive to changes in the internal parameters of the system. Also, it has strong adaptability to changes in operating conditions. The swing angle of crane weight is smaller, which further improves the robustness. The vector frequency conversion speed control has a more precise control effect on the speed and running track of the operating mechanism of the bridge crane. This method can be applied to the automatic operation mechanism of bridge cranes and achieve good control performance. Conclusion: The conducted optimization investigation of the bridge crane control system makes the control system flexible, simple and robust, providing important theoretical support for related explorations.

Model-free adaptive control for the PEMFC air supply system based on interval type-2 fuzzy logic systems

Control aims to avoid oxygen starvation and maximize the net power output by maintaining the optimal oxygen excess ratio (OER), which varies between 1.8 and 2.5. Because of the nonlinearity of the proton exchange membrane fuel cell (PEMFC) air supply system and the different conditions, ensuring an optimal OER is still a challenge. In this study, a model-free adaptive controller is presented for the PEMFC air supply system based on feedback linearization and interval type-2 fuzzy logic systems (IT2 FLSs). Theoretical analysis and experimental results verify the effectiveness of the proposed control scheme. For the theoretical analysis, first, the PEMFC air supply system is transformed into a canonical form with the feedback linearization technique. Then, zero-dynamics stability is discussed in detail to determine the stability of the internal dynamics. Finally, an adaptive interval type-2 fuzzy logic system controller (AIT2FLSC) is designed on the basis of the Lyapunov stability theory, which does not require complete a priori knowledge of the system dynamics. For the experimental results, the root mean square error (RMSE), variance, and standard deviation (SD) of the tracking error are used as tracking performance metrics to evaluate the control accuracy of the proposed AIT2FLSC, which are 0.0968, 0.0093, and 0.0962, respectively. Compared with the traditional proportion integration differentiation controller (RMSE 0.1119, variance 0.0122, and SD 0.1105), this proposed algorithm obtains better adaptability and the RMSE of the tracking error improves 13.48%. Compared with the adaptive type-1 fuzzy logic system controller (AT1FLSC) (RMSE 0.1076, variance 0.0113, and SD 0.1063), this AT2FLSC has a stronger ability to deal with uncertainty and the RMSE of the tracking error improves 10% when the stack temperature is fixed (353.15 K). Furthermore, when the stack temperature is time-varying, the RMSE, variance, and SD of the tracking error under the AIT2FLSC are 0.0966, 0.0092, and 0.0960, respectively, which is less than AT1FLSC (0.1085, 0.0115, and 0.1073) and the RMSE of the tracking error improves 10.99%.

Research of RBF-PID Control in Maglev System

Control of the maglev system is one of the most significant technologies of the maglev train. The common proportion integration differentiation (PID) method, which has fixed control parameters, ignores the non-linearity and uncertainty of the model in the design process. In the actual process, due to environmental changes and interference, the inherent parameters of the system will drift significantly. The traditional PID controller has difficulty meeting the control requirements, and will have poor control effect in the actual working environment. Therefore, a radial basis function (RBF)-PID controller is designed in this article, which can use the information from the levitation system identified by the RBF network to adjust the parameters of the controller in real time. Compared with the traditional PID control method, it is shown that the RBF-PID method can improve the control performance of the system through simulation and experiment.

Research on control method of unmanned underwater vehicle dynamic positioning based on energy consumption optimization

To satisfy the requirements of position control accuracy and allow unmanned underwater vehicle to maintain in the target zone with energy consumption as little as possible, this article proposes a flexible dynamic positioning strategy and control method, so as to extend the operating time of unmanned underwater vehicle dynamic positioning. Taking the distance between unmanned underwater vehicle and target point as the judgment condition for control method switching, this article delimits the working zone of different control levels for unmanned underwater vehicle and designs corresponding dynamic control methods for different working conditions. When unmanned underwater vehicle is far away from the target point, cuckoo search optimization method is proposed to plan the optimal motion scheme of energy consumption for the process of unmanned underwater vehicle reaching the target point. When unmanned underwater vehicle is close to the target positioning point, a flexible model predictive control method is proposed to reduce the energy consumption of unmanned underwater vehicle in the process of dynamic position control. The simulation case is designed to verify the flexible dynamic control capability of monomer unmanned underwater vehicle. The experimental results show that the strategy and control method proposed in this article, compared with the traditional proportion integration differentiation control method, could obtain the same control effect and reduce energy consumption, to achieve the purpose of prolonging the unmanned underwater vehicle operating time.

Mechanism and Position Tracking Control of a Robotic Manipulator Actuated by the Tendon-Sheath

To achieve a manipulator that is lighter and more anthropomorphic, a 7-DOF robotic manipulator driven by the tendon-sheath is designed. Its mechanical system and control system are both introduced in this paper. An encoder is installed on both the driver side and the joint side to realize double closed-loop control. Considering the transmission characteristics of the reducer and the tendon-sheath, a position transmission model of a single joint is constructed. Furthermore, the correctness of the transmission model is verified based on the prototype joint four. A proportional-integral-differential (PID) controller and fuzzy PID controller with time-delay estimation (TDE) are designed based on double encoders, and some experiments on the position tracking control are carried out for the robotic joints. The experimental results show that each joint can rotate normally and there is no interference between the different tendon-sheaths. When the drive motor moves to follow the position command of the sinusoidal signal, the TDE-based fuzzy PID controller can reduce the maximum tracking error compared with the traditional PID controller. According to the experimental results, it is feasible to apply the tendon-sheath transmission to the manipulator, which provides a reference for the development of other equipment based on the tendon-sheath.

Active Disturbance Rejection Control of Differential Drive Assist Steering for Electric Vehicles

The differential drive assist steering (DDAS) system makes full use of the advantages of independent control of wheel torque of electric vehicle driven by front in-wheel motors to achieve steering assistance and reduce the steering effort of the driver, as the electric power steering (EPS) system does. However, as an indirect steering assist technology that applies steering system assistance via differential drive, its linear control algorithm, like existing proportion integration differentiation (PID) controllers, cannot take the nonlinear characteristics of the tires’ dynamics into account which results in poor performance in road feeling and tracking accuracy. This paper introduces an active disturbance rejection control (ADRC) method into the control issue of the DDAS. First, the third-order ADRC controller of the DDAS is designed, and the simulated annealing algorithm is used to optimize the parameters of ADRC controller offline considering that the parameters of ADRC controller are too many and the parameter tuning is complex. Finally, the 11-DOF model of the electric vehicle driven by in-wheel motors is built, and the standard working conditions are selected for simulation and experimental verification. The results show that the ADRC controller designed in this paper can not only obviously reduce the steering wheel effort of the driver like PID controller, but also have better nonlinear control performance in tracking accuracy and smooth road feeling of the driver than the traditional PID controller.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

In this paper, the dynamic model of unmanned helicopter is modeled by frequency domain identification method. An adaptive fuzzy Proportion Integration Differentiation (PID) controller is established, with its design carried out from the aspects of the input and output fuzzy subset, the membership function selection, the fuzzy rule generation, as well as the fuzzy reasoning. An improved genetic algorithm is employed to optimize the initial expert empirical fuzzy rules, which avoids the traditional method from falling into the local optimal solution in the process of optimization. Specifically, adaptive crossover and mutation probability are adopted to accelerate the convergence speed of the genetic algorithm. We then apply the proposed algorithm to optimize the membership function and fuzzy control rules of the fuzzy controller. Simulation results indicate that our adaptive fuzzy PID has better control effect and anti-interference ability than the traditional PID control law. Additionally, our adaptive fuzzy PID controller is verification by flight test thought AF25B unmanned helicopter platform.

Global Asymptotic Nonlinear PID Control With a New Generalized Saturation Function

In order to solve the problem of the strict condition of traditional saturation function, a new generalized saturation function was proposed and applied in nonlinear PID (Proportion-Integration-Differentiation) control laws, which consisted of linear D + nonlinear PI and linear PD + nonlinear PI. The new generalized saturation function has powerful reaction near the equilibrium point, and has the capability to make the control converge to the equilibrium point swiftly. The global asymptotic stability condition of nonlinear PID control laws were derived by employing Lyapunov’s method and LaSalle’s invariance principle. In order to improve the accuracy of nonlinear PID control laws, time integration of the absolute value of position tracking error and time integration of the absolute value of torque error were chosen as the objective functions. Global asymptotic stability conditions and rated driving torque of each motor were set as the constraint conditions. Nonlinear PID controller parameters were tuned by employing multi-objective genetic algorithm, non-dominated sorted genetic algorithm-II (NSGA-II). Compared with the optimization results of nonlinear PID with traditional saturation function, the accuracy of position tracking using the proposed method was improved by nearly one order of magnitude. The new generalized saturation functions with minimum time integration of position tracking error were selected to study the robustness of the nonlinear PID controller in modeling uncertainty, input torque disturbance, and noise. The position tracking accuracy of the proposed method compared to those of the traditional PID controller and nonlinear PID controller with traditional saturation function was improved by nearly two orders of magnitude and one order of magnitude, respectively. The introduced saturation function significantly improves position tracking accuracy and robustness of the nonlinear PID controller.

Luenberger-Sliding Mode Observer Based Backstepping Control for the SCR System in a Diesel Engine

In order to keep the ammonia (NH3) slip of the downstream selective catalytic reduction (SCR) system at a low level and simultaneously achieve a high nitrogen oxide (NOX) conversion rate, a Luenberger-sliding mode observer based backstepping control method is proposed. Considering that the internal working condition of the catalyst cannot be measured by commercial sensors directly, a Luenberger-sliding mode observer is designed to estimate the ammonia concentration at the middle of the catalyst. In addition, based on the stepped distributed characteristic of the surface ammonia coverage ratio along the SCR axial direction, a backstepping control method is utilized for the SCR system, in which the SCR system is decomposed into two subsystems. Firstly, the Lyapunov function is designed to ensure the convergence of the downstream subsystem, and then the virtual control law is obtained. After that, taking the virtual control law as the tracking target of the upstream subsystem, the Lyapunov function of virtual control law is given. Finally, the actual control law of the whole closed loop system is acquired. Simulations under different conditions are conducted to investigate the effect of the proposed control method. In addition, comparisons with the traditional PID (Proportion Integration Differentiation) control are presented. Results show that the proposed method is much better than the PID control method in overshoot, setting time, and tracking error.