Small Overshoot Research Articles

Research on DC Electronic Load System Based on PSO-PID Algorithm

At present, the control part of the DC electronic load is mainly based on analog control, that is, the control loop is controlled by an operational amplifier. Therefore, it is easy to cause slow response speed and overshoot in the process of current rise, and the improvement of response speed is of great significance to the test link. Therefore, this paper studies the digital control of DC electronic load. The DC electronic load control system has the characteristics of nonlinearity, and it is difficult to complete the theoretical modeling. Therefore, the MATLAB system identification toolbox is used to obtain the mathematical model. The traditional PID tuning method has a long time and poor effect. This paper adopts a parameter self-tuning PID controller based on particle swarm optimization algorithm. Through MATLAB simulation, the traditional PID control and particle swarm optimization PID control are simulated and compared. The results show that the digital controller has faster response speed and smaller overshoot than the conventional controller, and the system performance is significantly improved.

Sliding Mode Control of Brushless DC Motor Speed Control

Over the years, development in control industry has brought a conventional controller, Proportional-Integral-Derivative (PID) controller as Brushless DC (BLDC) motor speed regulator. The PID controller suffers from lengthy design time due to the large number of rules and parameter tuning. Thus, this paper proposes a newly developed Sliding Mode Controller (SMC) to be used as the BLDC motor speed controller. SMC is a modern speed controller which is also a non-linear speed controller where it can show high performance controlling non-linear plant like BLDC motor. SMC gives a speed performance comparable to the PID but with much robust speed performance in terms of small overshoot and short settling time. The motor performance with SMC is evaluated through simulation and experimental approach in terms of speed response under several test conditions. The performance is then compared with the motor performance with PID speed controller. Overall, SMC is outperformed PID controller in terms of speed performance with no overshoot and less settling time.

Research on the dynamic characteristic of a Seawater-Hydraulic-Relief-Valve with a damping orifice

Seawater-Hydraulic-Relief-Valve (SWHRV) is a significant control component for Seawater-Hydraulic-System in underwater equipment. It makes the Seawater-Hydraulic-System operate within the relief pressure range and ensure the safety of the whole Seawater-Hydraulic-System. The performance of a SWHRV with an orifice and damping chamber is researched in this paper. The nonlinear mathematic model is built based on the operation principal and the visualization graphic simulation model is built by use of AMESim software. The simulation results indicates it has well static performances and satisfies the industrial requirements. Its rated opening pressure is 100 bar, and the releasing flowrate reaches 100 l/min when the pressure increase to 107 bar. The pressure adjusting range is between 100 bar and 205 bar. And the maximum releasing flowrate is as high as 1600 l/min at the pressure of 205 bar. Due to the damping orifice and chamber, a damping force would generate accompanying with the dynamitic motion of the spool. The opening dynamic characteristic is evidently improved though the shutting time becomes longer in comparison with the relief valve without orifice. The pressure oscillation is eliminated and it could reach the steady state ultimately. Furthermore, the effect of the damping orifice and chamber’s structure parameters on the dynamitic characteristic is analyzed. The orifice’s cross-sectional area is the main effect factor and the influence of the initial chamber volume can be neglected. Finally, the relief valve achieves well dynamic characteristic with the peak time 0.003 s and the transition time 0.015 s. The overshoot is about 20%. It has well performance with fast response, small overshoot and strong robustness.

Fractional PIλDµ controller design for non-linear PEM fuel cell for pressure control based on a genetic algorithm

ABSTRACT A fractional-order dynamic model could more accurately model many real scenarios than an integer-order model and provide a more accurate description of numerous genuine dynamical processes. A seventh-order nonlinear proton exchange membrane fuel cell (PEMFC) model is linearised in this research, taking into account correct initial conditions and equilibrium points.We consider the fluctuating load current as a disturbance parameter to affect the system. The goal is to find a control law for the MIMO system using a fractional PID controller based on a genetic algorithm. The controller is a critical part of the fuel cell which controls its functioning and efficiency. The goal is accomplished by designing a fractional controller to adjust the natural response of the fuel cell reactor and maintain the desired Power output in the face of uncertainties and disturbances. The validation results demonstrate that the fractional PIλDµ(FOPID) control method has a smaller overshoot and higher stability than the PID control method. Moreover, it is also observed that the operation efficiency of the PEMFC has risen by 2% with a response timing of less than 0.1 s using the developed fractional PIλDµ(FOPID) control technique.

Testing Method for Intelligent Loading of Mining Emulsion Pump Based on Digital Relief Valve and BP Neural Network Control Algorithm

The mining emulsion pump is mainly used on a fully mechanized coal mining face, but it is rarely used on other occasions, so research on its loading test method is relatively limited. This paper proposes the application of a digital relief valve to the emulsion pump loading test. In addition, the small number of plungers in the emulsion pump will lead to large flow pulsation and pressure pulsation, and the nominal flow of different types of emulsion pumps varies greatly. These factors lead to the deficiency of a traditional PID control algorithm in control accuracy and efficiency. In order to improve control accuracy and efficiency, firstly, the influence of the flow rate of the tested pump and extension of the linear stepping motor shaft on the working pressure is studied. A backpropagation (BP) artificial neural network (ANN) model is used to fit a functional relationship between the three parameters. The flow rate of the tested pump and target pressure were provided as inputs to predict the extension of the linear stepping motor shaft, thereby realizing the remote intelligent control of the system pressure. Next, a BP ANN model is constructed, and its reliability is verified; the BP neural network algorithm and proportional-integral-derivative (PID) algorithm are compared through simulation. The simulation results show that the BP neural network algorithm has high control accuracy and small overshoot. Finally, two pumps with different flows are tested in a self-developed digital relief valve and test platform. The test results show that the proposed loading test method is intelligent and efficient, and it has high accuracy.

Optimasi PID Kontroller Pada Sistem Pengaturan Irigasi Menggunakan Metode Bat Algorithm (BA)

Provision of irrigation water on agricultural land aims to meet crop water needs. In its utilization, irrigation water must be used optimally. An automatic irrigation system is needed that is able to provide water for plants with the expected conditions. PID (Proportional, Integral and Derivative) is widely used for optimization of control systems. In irrigation control system and sensor connection with software program (Matlab 2013b). PID simulation was applied to the irrigation prototype used to increase efficiency and determine the amount of irrigation water to regulate the provision of irrigation water as needed. As a comparison controller, conventional PID (PID-Konv) was used, Matlab auto tuning PID (PID-Auto), and Bat Algorithm tuning PID (PID-BA). The simulation results show that PID-BA is the best method in the simulation with the smallest overshot (0.043) and the fastest settlingtime (84 seconds).

Komparasi PID, FLC, dan ANFIS sebagai Kontroller Dual Axis Tracking Photovoltaic berbasis Bat Algorithm

Photovoltaic is a renewable electrical energy generator that is very suitable for tropical countries that get a lot of sunlight. However, this generator has low efficiency. To overcome this deficiency, several researchers have optimized the conventional dual-axis tracking solar method. Research is needed to optimize using artificial intelligence, in this case, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Bat Algorithm (BA). By comparing the performance of the model without control, conventional PID model, PID Auto tuning MatLab, Fuzzy Logic Controller (FLC) method, ANFIS method, and ANFIS-BA method. The simulation results show that the best model design on the horizontal axis and vertical axis dual tracking photovoltaic is ANFIS-BA with the smallest overshot, smallest undershot, and the fastest settling time of all model designs.

Prescribed Performance Adaptive Tracking Control With Small Overshoot for a Class of Uncertain Second-Order Nonlinear Systems

This brief investigates the problem of prescribed performance adaptive tracking control for a class of second-order nonlinear systems with uncertainties. First, a new performance function is defined to guarantee that the tracking error has small overshoot and converges to a predetermined region for a user-defined time. Then, to effectively diminish the control input energy consumption caused by the introduction of constraints, a novel coordinate transformation function is designed. Next, an improved fixed-time backstepping control framework is established to achieve the tracking control objective. According to the theoretical analysis, all signals of the closed-loop system are bounded, and the tracking error converges to a preset domain with small overshoot for a user-designed time. Finally, numerical simulations confirm the effectiveness of the presented control strategy.

Event-Triggered Neural Sliding Mode Guaranteed Performance Control

To solve the trajectory tracking control problem for a class of nonlinear systems with time-varying parameter uncertainties and unknown control directions, this paper proposed a neural sliding mode control strategy with prescribed performance against event-triggered disturbance. First, an enhanced finite-time prescribed performance function and a compensation term containing the Hyperbolic Tangent function are introduced to design a non-singular fast terminal sliding mode (NFTSM) surface to eliminate the singularity in the terminal sliding mode control and speed up the convergence in the balanced unit-loop neighborhood. This sliding surface guarantees arbitrarily small overshoot and fast convergence speed even when triggering mistakes. Meanwhile, we utilize the Nussbaum gain function to solve the problem of unknown control directions and unknown time-varying parameters and design a self-recurrent wavelet neural network (SRWNN) to handle the uncertainty terms in the system. In addition, we use a non-periodic relative threshold event-triggered mechanism to design a new trajectory tracking control law so that the conventional time-triggered mechanism has overcome a significant resource consumption problem. Finally, we proved that all the closed-loop signals are eventually uniformly bounded according to the stability analysis theory, and the Zeno phenomenon can be eliminated. The method in this paper has a better tracking effect and faster response and can obtain better control performance with lower control energy than the traditional NFTSM method, which is verified in inverted pendulum and ball and plate system.

A novel control scheme to mitigate temperature profile variation in a SOFC System

Maintaining temperature profile in a solid oxide fuel cell (SOFC) is one of the essential issues that need to be addressed during the load tracking. In this paper, a novel control scheme with three-loop structure is developed to mitigate SOFC temperature profile variation. This scheme can simply implemented by three variable-speed blowers. The first blower is an anode recirculation blower used to maintain anode inlet temperature, the second one is a cathode recirculation blower used to maintain cathode inlet temperature, and the last one is an air blower used to maintain cathode outlet temperature. Compared with other simple schemes with only one-loop or two-loop structure, the scheme with three-loop structure achieves the best performance in mitigating SOFC internal temperature profile variation and guaranteeing a safe margin on operation limits. With a 10% system load step drop, the three-loop control scheme presents the best performance in rapidity and stability, with a much smaller overshoot, a much shorter settling time, and the smallest ITAE criterion. The SOFC temperature profile variation is also smallest (deviating within 4.7 K). Time-dependent temperature gradient is much less than 3.0 K/min. Temperature gradient is significant less than 10 K/cm. Steam to carbon ratio is also kept more than 2.0. Furthermore, the three-loop control scheme can perform high system efficiency, which reaches 53.9% at the 90% load. As a result, the three-loop SOFC temperature control scheme is robust in safe and efficient operation.

Optimized cascaded PI controller for the load frequency regulation of multi-area power systems with communication delays

The safe operation of multi-area power systems (MPS) is facing great challenges with the explosive growth of renewable energy, where the load frequency regulation (LFR) may be disturbed due to the unpredictability and uncertainty of renewable energy. This paper considers the communication delays in the area control error signals for the LFR of MPS, where a cascaded proportional–integral–derivative (PID) control scheme optimized by genetic algorithms is proposed to handle the communication delays. By comparing the stability boundaries of PI parameters with/without different time delays, the adverse effects caused by communication delays are discussed. Based on the proposed cascaded control scheme, its parameters are optimized by the genetic algorithms. Simulation results in different scenarios validate the superiorities of the proposed control scheme, where it can reject disturbances of the load variance most quickly and obtain a small overshoot. The proposed cascaded PI control scheme optimized by the genetic algorithms shows great promising potential in load frequency regulation.

A Modified Active-Disturbance-Rejection Control with Sliding Modes for an Uncertain System by Using a Novel Reaching Law

This article presents a modified active-disturbance-rejection control (ADRC) combined with a sliding mode control (SMC) regarding the tracking control problems for plants with unmatched uncertainty. The proposed modified active-disturbance-rejection control with sliding mode (ADRC-SM) employs a reduced-order extended state observer (ESO) for estimating various uncertainties of system in time, including unmatched and matched uncertainties. Meanwhile, a novel reaching law of SMC was designed by using the cycloid function as the main controller of ADRC, which ensures the robustness of the uncertain system. Due to the reduced-order ESO tracking and compensating for various uncertainties in the system as a total disturbance, the upper bound of the disturbance in the SMC is relaxed. The gain coefficient of the reaching law only needs to be designed to be larger than the limit of the lumped disturbance; thus, the chattering problem is greatly reduced. The designed new reaching law of the cycloid function shortens the time for the system state’s convergence to the sliding mode’s surface. The cycloid function replaces the switching function in the traditional reaching law, making the actual control input continuous and shortening the approach time. Compared with traditional ADRC-SM, the use of multiple ESOs or intelligent algorithms to approximate plant parameters can be avoided, the design is simplified, its robustness is enhanced, computational costs are reduced, and the convergence time is reduced. The controlled object with unmatched uncertainty is transformed into a system with matched uncertainty using state-space transformation, which reduces the complexity of the controller’s design. In addition, the stability analysis of the closed-loop system is carried out based on the Lyapunov method. Simulations and experiments verify that the modified ADRC-SM has the merits of fast response, small overshoot, small steady-state error, strong anti-interference competence, and high control accuracy.

Application of the Gray Wolf Optimization Algorithm in Active Disturbance Rejection Control Parameter Tuning of an Electro-Hydraulic Servo Unit

A valve-controlled hydraulic cylinder system has the characteristics of uncertainty and time-variance, and the electro-hydraulic servo unit encounters shock, vibration, and other external interference when working, which seriously affect the stability of the valve-controlled hydraulic cylinder system. Therefore, it is necessary to introduce an active disturbance rejection controller (ADRC) into the electro-hydraulic servo control. However, there are many ADRC parameters, and it is difficult to set these only with expert experience. Therefore, we propose applying the gray wolf optimization algorithm (GWO) to the ADRC, to auto-tune the parameters and find the optimal solution. In addition, the advantages of the GWO in ADRC parameter tuning are proven and analyzed. The simulation and experimental results showed that the GWO algorithm had a faster mean time for parameter tuning and the smallest fitness value (integrated time and absolute error), compared to the particle swarm optimization algorithm and genetic algorithm. Moreover, a valve-controlled cylinder system, after parameter tuning by the gray wolf optimization algorithm, could accurately adjust the parameters of the auto-disturbance rejection controller, with a faster response speed, smaller overshoot, and better anti-disturbance ability.

Fuzzy Neural Network PID Strategy Based on PSO Optimization for pH Control of Water and Fertilizer Integration

In the process of crop cultivation, the application of a fertilizer solution with appropriate pH value is more conducive to the absorption of nutrients by crops. If the pH of the irrigation water and fertilizer solution is too high, it will not only be detrimental to the absorption of nutrients by the crop, but will also damage the structure of the soil. Therefore, the precise regulation of pH in water and fertilizer solutions is very important for agricultural production and saving water and fertilizer. Firstly, the article investigates the hybrid control of fertilizer and water conditioning systems, then builds a fuzzy preprocessing controller and a neural network proportional–integral–differential controller, and optimizes the neural network parameters by means of an improved particle swarm algorithm. The effectiveness of the controller was verified by comparison with the common proportional–integral–differential control and fuzzy algorithm control for fertilizer control and fuzzy preprocessing neural network control. Simulation experiments for this study were designed through the MATLAB/Simulink simulation platform, and the simulation results show that the algorithm has good tracking and regulation capabilities in the system. Finally, the four control algorithms are experimentally validated under different pH regulations using designed field experiments. The results show that, compared with other control algorithms, the control algorithm in this paper has a smaller overshoot and good stability with a shorter rise time, which can achieve the purpose of better regulating the fertilizer application system.

Research on Fuzzy Control Method of Automobile Antilock Braking System

The automobile Antilock Braking System (ABS) can prevent the wheel from locking by automatically adjusting the brake pressure at a high speed during emergency braking. While improving the braking effect of the car, it can also keep the steering ability of the car and ensure the safety of passengers. The automobile braking process has a strong nonlinearity and uncertainty, so exploring a simple and reliable control strategy is the focus of current research. Based on this, this paper proposes a variable-domain fuzzy proportional integral differential (PID) control strategy using a particle swarm optimization (PSO) algorithm to iteratively find the optimal theoretical domain. First, the PSO strategy is used to obtain the optimal regulation parameters. Then, the dynamic antiinterference ability of the control system is guaranteed by the variable theory domain fuzzy PID control, and the PID parameters and variable theory domain expansion factor are optimized by the PSO to increase the utilization degree of fuzzy rules initially set. Finally, compared with the traditional control strategy, the simulation and real vehicle test prove that the proposed system can significantly improve the control accuracy of abs. The proposed system has the advantages of small overshoot, short adjustment time, strong antiinterference ability, and practicability, which greatly improves the tracking performance of the ABS.

A Simplified Finite-Time Fuzzy Neural Controller With Prescribed Performance Applied to Waverider Aircraft

This article addresses a finite-time prescribed performance controller within the concise fuzzy-neural framework with application to a waverider aircraft. First, new finite-time performance functions are developed to construct a constraint funnel, which accomplishes that tracking errors converge to their steady-state values in a given time (i.e., finite-time convergence), being expected to guarantee tracking errors with small overshoots. Then, the equivalent transformation approach is introduced to unify unknown dynamics such that the control complexity is reduced. Moreover, to further reduce computational costs, a single-learning-parameter-based regulation scheme is developed for fuzzy-neural approximation. Finally, the proposed method is applied to a waverider aircraft to test its effectiveness and superiority.

Adaptive fuzzy finite-time control with prescribed performance for waverider vehicles

This paper proposes an adaptive fuzzy control method with prescribed performance for Waverider Vehicles (WVs), being able to guarantee finite-time convergence and small overshoot for tracking errors. Firstly, we design a new type of performance function that is independent of the initial error, and possess finite-time convergence and small overshoot. Then, we transform the inequality constraints on tracking errors into an unconstrained equation by introducing a transformed error. On this basis, we design a prescribed performance control (PPC) approach to limit the tracking errors within prescribed funnels utilizing the transformed error and fuzzy approximation, which ensures that satisfactory transient performance and steady-state accuracy can be guaranteed for tracking errors. Compared with the existing PPC, the improvement is to assure finite-time convergence of tracking errors with almost zero overshoot. Finally, compared simulations are given to verify the advantage.

Optimization of Spring Constant of a Pneumatic Artificial Muscle-Spring Driven Antagonistic Structure

Pneumatic artificial muscles (PAMs) have been widely applied to robotic systems, especially assistive devices, which could benefit from PAMs’ intrinsic viscoelasticity. However, nonlinearity and hysteresis make it challenging to achieve high-accuracy control. Using pre-tensioned springs to replace one side PAMs in an antagonistic structure is one efficient way to improve control accuracy. However, in the previous study, the selection method for spring, especially the range of spring constant has not been sufficiently investigated. This study designed a method to find the optimal spring constant based on gradient descent algorithm; and verified it with prototype and simulation experiments. Results showed that the group with optimal spring constant leads to a faster response time, smaller overshoot, and lower steady-state error, which indicated that the proposed method could find optimal spring constant. Moreover, the sensitivity of PAMs dynamic model parameters to the optimal spring stiffness was investigated. This study provides insights to practical use of soft actuators.

Automatic Generation Control of Single Area Hybrid Power System

In the power system, the load demand is continuously changing as per system requirement and the power system controller should efficiently compensate for the load requirement. The frequency instability may lead to equipment damage, load shedding, and possible blackouts, as a reason frequency is the most important design parameter to achieve the stability of a power system. The stable operation of an interconnected Hybrid Power System depends on a balance between production and consumption centers. To ensure system stability, different control techniques or control algorithms to compensate for the deviations in the system are employed but selecting the proper controller and its correct gain is tedious. In this research paper, the load frequency control of a single area hybrid power system is carried out using the Proportional-Integral and Derivative (PID) controller. The hybrid power system may consist of a renewable energy system (Wind/Solar) and a conventional interconnected power system (Thermal/Hydro). To obtain the optimal tuning of the PID controller, this research utilizes the PID Tuner from MATLAB. The performance of the PID controller for a single area hybrid power system will be analyzed for various load variation cases to show its effectiveness. The proposed PID-based governor provides better frequency control during load variations with smaller overshoot, undershoot, and short settling time of 10 seconds, as compared to P and PI.

Design and Temperature Modeling Simulation of the Full Closed Hot Air Circulation Tobacco Bulk Curing Barn

For now, the open humidification method is applied in the tobacco bulk curing barn, which has some disadvantages, such as the loss of the oil content and aroma components of the tobacco leaves and the waste heat loss of the exhaust air flow. In this context, a tobacco bulk curing barn with totally closed hot air circulation is designed to perfect the curing quality of tobacco and avoid the loss of residual heat in the bulk curing barn. Meanwhile, due to the balance and symmetry of input and output of the curing barn temperature, according to the law of conservation of energy, a mathematical model of the temperature control system of the closed hot air circulation tobacco bulk curing barn is established, and the temperature transfer function of the system is obtained. On this basis, 10 algorithms are used to optimize the full closed hot air circulation tobacco bulk curing barn temperature control system PID parameters. The result of the sobol sequence seeker optimization algorithm (SSOA) is better than the other algorithms. So, the PID control strategy based on the SSOA is used to simulate and experiment the temperature control system of tobacco bulk curing barn. The simulation and experimental results show that for the tobacco bulk curing barn temperature control system, the sobol sequence seeker optimization algorithm PID control has better dynamic characteristics compared with fuzzy PID control, and the temperature control system of tobacco bulk curing barn has fast adjustment and small overshoot. Therefore, the new baking barn with proper PID parameters can improve the tobacco’s curing quality and save energy by reducing the residual heat.