Fuzzy Adaptive PID Control Research Articles

Trajectory Planning and Adaptive Fuzzy PID Control for Precision in Robotic Vertebral Plate Cutting: Addressing Force Dynamics and Deformation Challenges

This study explores trajectory planning and cutting force control for spinal surgical robots, focusing on managing cutting deformation and vibration during vertebral plate cutting—a critical challenge in robotic spinal surgery. Through theoretical analysis, simulation, and experimental validation, the research addresses the complexities of cutting force dynamics and the associated deformations. A dynamic equation for vertebral plate cutting is established, providing a theoretical foundation for trajectory planning. A new trajectory planning method using B-spline curves and an interpolation point constraint formula is introduced, enabling precise robot trajectory control. Additionally, an adaptive fuzzy PID control strategy with force feedback is proposed to dynamically adjust the feed rate, effectively suppressing cutting deformation and vibration in real time. Simulations and experiments validate the effectiveness of these methods in reducing force fluctuations and enhancing control stability. The findings offer valuable guidance for improving the performance and safety of spinal surgery robots, optimizing their trajectories, incorporating dynamic sensing, and enhancing safety controls. Future research should focus on refining control strategies for more complex surgical scenarios and validating their applicability under conditions closer to actual surgical environments. This work provides theoretical and practical insights into advancing robotic spinal surgery, contributing to better surgical outcomes and patient safety.

Modeling and Experimental Validation of a Cable-Driven Robot for 3D Printing Construction

Abstract Cable-driven robots are characterized by a large workspace, high speed and load-to-weight ratio, providing a new technology approach for large-scale autonomous 3D construction of lunar surface shelters. A novel deployable cable-driven construction 3D printer (CDCP) is developed in this study. Guiding pulleys and cable sagging are considered and modeled to improve the accuracy of the system. A fuzzy adaptive differential evolution PID (FDEPID) control is proposed to reduce end-effector motion errors in Cartesian space and thus improve printing quality and stability. Concentric and zigzag infill strategies are compared to optimize printing efficiency and infill effectiveness in path planning. Finally, experiments are carried out to validate the proposed FDEPID control and path planning strategies which demonstrate the great potential of the developed cable-driven printer in the construction of lunar architecture.

Research on Tracking Control Technology Based on Fuzzy PID in Underwater Optical Communication

In order to realize effective laser communication on underwater mobile platforms, the active tracking and alignment technology of underwater laser communication (UWLC) is studied. Firstly, the servo control principle of the UWLC system is analyzed. Secondly, aiming at underwater disturbance, an adaptive fuzzy PID controller is designed to realize parameter self-tuning, thereby improving the system’s anti-disturbance ability. The designed algorithm was simulated, and the simulation results show that the adaptive fuzzy PID algorithm has better anti-disturbance ability and tracking performance than the traditional PID. Finally, an experimental platform was built for dynamic tracking experiments, and the results show that the dynamic tracking accuracy of the designed control algorithm was improved by 30.29% compared with the traditional control algorithm, which provides a certain reference for the development of laser communication on underwater moving platforms.

Precise control algorithm of rotational speed of the valve-controlled hydraulic motor under load fluctuating conditions.

The present study focuses on the analysis and design of a novel fuzzy adaptive PID control algorithm, aiming to enhance the speed control accuracy of valve-controlled hydraulic motors under load fluctuating conditions. The method of model identification was adopted to calculate the transfer function of valve-controlled hydraulic motor based on the established hydraulic technology scheme. The chassis of the valve-controlled hydraulic motor is subsequently investigated, and a model for E-grade pavement is constructed using the harmonic superposition method. Through dynamic simulation, the load fluctuation range under two common operating conditions is determined. The fuzzy adaptive PID algorithm was subsequently designed in detail, with the error and its rate of change being considered as input parameters, while the increments of the proportional coefficient, integral constant, and differential constant were regarded as output parameters. Co-simulation data indicate that, compared with the PID algorithm, the average error of the fuzzy adaptive PID algorithm can be reduced by more than 50% and the rise time is reduced by 0.04 seconds. To validate the theoretical analysis, a tracked hydraulic chassis was developed and tested. At an expected speed of 200 rev/min, the average error decreased by 1.68 rev/min, while at an expected speed of 1000 rev/min, the average error reduced by 2.68 rev/min. The designed intelligent control algorithm can effectively improve the control accuracy and stability under load fluctuating conditions.

Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications.

Cyber-Security Attacks on Microgrid and Its Mitigation via Adaptive Fuzzy PID Controller

Cyber-Security Attacks on Microgrid and Its Mitigation via Adaptive Fuzzy PID Controller

Torsional vibration analysis and fuzzy adaptive PID control optimization of underwater vector propulsion shaft systems

To examine the torsional vibration of vector propulsion shaft system (VPSS), a four-degree-of-freedom vibration model of the VPSS is established by concentrated mass approach, and the torsional vibration differential equation of the VPSS is deduced based on the Lagrange approach. The vibration differential equations is addressed numerically applying the Runge-Kutta algorithm to investigate the effect of various working shaft angles β on the free and forced vibration of the VPSS under the excitation conditions. To further reduce the torsional vibration amplitude and avoid fatigue damage of the system, and considering the nonlinear relationship within the system, this study develops a fuzzy adaptive PID approach to control the VPSS. The results show that the maximum torsional vibration amplitude of the VPSS is reduced by more than 70% and the transient response time is shortened by more than 90% with this control approach, which effectively reduces the torsional vibration amplitude of the VPSS and thereby significantly improves the safety performance of the underwater vehicle. This may provide technical references for the vibration control theory of the VPSS as well as engineering applications.

Design and Simulation of a Multi-Channel Biomass Hot Air Furnace with an Intelligent Temperature Control System

Timely and effective drying of agricultural products is crucial for ensuring the quality and yield of grains. Biomass drying enhances energy utilization and reduces energy pressure. To this end, a novel multi-channel circulating biomass hot air furnace was designed to provide precise control of the heat source for grain drying, thereby improving the efficiency and quality of the drying process. The combustion process utilizes a multi-channel combined air supply to ensure complete combustion of biomass pellet fuel. During the heat exchange process, heat exchange plates isolate hot and cold areas, discharging combustion exhaust, while ensuring a pure air output. Using rapeseed as the drying subject, a temperature controller based on adaptive fuzzy PID was designed, targeting the biomass hot air furnace’s heat exchange system for modeling and verifying the model with the step response method. Model simulations were conducted in Matlab’s Simulink module using both adaptive fuzzy PID and traditional PID controllers, for a given signal. The settling times for the conventional PID and fuzzy PID were 445 s and 364 s, respectively, with overshoots of 20.1% and 6.3%, showing that the fuzzy PID controller performed better in terms of control performance. The validation tests showed that both control methods could maintain the temperature within ±5 °C. Compared to traditional PID control, the adaptive fuzzy PID control achieved a precision of ±3 °C. At the target temperature of 90 °C, the error was reduced to 3.7%, with a stabilization time of 1014 s. The use of fuzzy PID control exhibited better dynamic response characteristics, meeting the drying needs of rapeseed. This study provides a theoretical basis for the structural design and control system design of biomass hot air furnaces.

Deep learning-based intelligent control of moisture at the exit of blade charging process in cigarette production

Abstract Currently, in the production of cigarettes in the blade, charging export moisture control means is relatively single and can not effectively guarantee the excellent quality of cigarette filament. In this paper, first of all, the working principle of the tobacco blade charging machine is introduced, and the moisture of the tobacco leaf for the charging machine is dynamically analyzed, and the influence of the return air temperature control of the charging machine on the export moisture of the blade charging process is explored. Secondly, based on the traditional PID controller, an adaptive fuzzy PID controller is established by combining adaptive fuzzy rules, and then the stacked noise-reducing self-encoder in deep learning is combined with the adaptive fuzzy PID control to design the intelligent control structure of export moisture of leaf charging process. Finally, the effectiveness of export moisture intelligence control, process capability index, and the effect before and after application were analyzed in controlled experiments, respectively. The results show that the difference between the predicted value and the real value of blade export moisture in this paper’s method is only 0.5%, and the process capability index of this paper’s method is improved by 1.48 compared with the PID controller, and it can control the temperature of the return air of the charging machine in the range of 56.86℃~57.21℃. The intelligent control method of export moisture introduced by deep learning can accurately control the export moisture of the leaf dosing process, which effectively ensures the quality of tobacco filament making.

Warp yarn tension coupling models and fuzzy PID control for enhancing warp beam unwinding tension stability in sizing machines

The coupling model between unwinding tension and various factors is crucial for understanding warp beam unwinding characteristics and designing tension control systems. However, currently established models are simple and fail to reflect the complexity of the actual unwinding process. Additionally, the traditional unified PID control method for multiple warp beams is difficult to ensure stable unwinding tension for single warp beam, negatively impacting the quality and efficiency of the sizing process. To address these issues, coupling models between tension, radius, speed, turns and air pressure during the warp beam unwinding process were established and validated using constructed detection devices. Experimental results showed that the four types of measured warp beams fit the three coupling models with [Formula: see text] values exceeding 90%, confirming the accuracy of these models. Subsequently, a novel fuzzy adaptive PID control system for single warp beam unwinding tension was proposed based on these models. Unlike traditional methods that measure tension directly, the system utilizes unwinding turns detection device to monitor the real-time changes. Simulation results confirmed that the proposed system maintains constant tension under different reference tensions and unwinding radius. Compared to traditional PID control, it significantly reduces tension fluctuation, with reductions of 64.49% under different speed step disturbances and 23.66% under noise disturbances. The validated models and proposed tension control system in this paper provide a solid theoretical foundation and practical guidance for optimizing the new generation of sizing machines.

Adaptive Fuzzy PID Car Control using a Bilateral Teleoperator

This paper investigates the application of Adaptive Fuzzy PID Control in the context of car control using a bilateral teleoperator. A bilateral teleoperator allows the operator to operate a remote car using his own controls and at the same time receive feedback on the car’s condition. The goal of the research is the analysis and comparison of different control methods, including PID controller, Adaptive Fuzzy PID controller, and the use of energy and wave variables of the bilateral teleoperator. The paper presents a car control model by means of a bilateral teleoperator, which was implemented in a simulation environment. Then performance comparisons of different control methods were made. The PID controller was used as the basic method, while the Adaptive Fuzzy PID controller was additionally included to achieve system adaptivity. Also, the use of energy and wave variables of the bilateral teleoperator was additionally investigated. The results of the comparison show that the use of bilateral teleoperator wave variables gave the best response of the control system. The analysis of signal waveforms and frequencies enabled more precise monitoring of the car’s condition and the detection of possible problems or instability in the system. This knowledge can be of great importance for improving the performance of car control by means of a bilateral teleoperator.

Control Strategy of New Energy Vehicle Interior Thermal Management System Based on Intelligent Assisted Algorithms

<p>Reasonable and efficient utilization of pure electric vehicle thermal management system can improve the battery utilization rate of new energy vehicles, thereby increasing the vehicle’s range and charging anxiety. This article proposes a new tram thermal management system scheme for temperature management in new energy vehicles, and designs corresponding thermal management system control strategies based on fuzzy adaptive PID control method. Finally, in order to verify the effectiveness of the management plan and control strategy, Matlab software was used for simulation. The simulation results showed that the intelligent control method proposed in this paper can increase the battery utilization rate by 17%.</p> <p> </p>

Design and Optimization of Hydropneumatic Suspension Simulation Test Bench with Electro-Hydraulic Proportional Control

Available hydropneumatic suspension simulation test benches have insufficient loading accuracy and limited functionality rendering them unsuitable for performance testing of heavy vehicles with this type of suspension. Therefore, a multi-functional compound simulation test bench was designed that used an electro-hydraulic proportional control technique. A mathematical model was established to describe the hydraulic loading system, and the transfer function of the system was derived. The gain and phase margins confirmed the stability of the system. A simulation model was established in the Simulink environment and step and sine signals of different frequencies were applied separately to analyze the dynamic characteristics of the system. The results showed that the system responded slowly and exhibited phase lag and signal distortion. The dynamic characteristics of the system were improved by incorporating an adaptive fuzzy PID controller. Simulation results showed that the response of the system to the step signal stabilized at the preset value within 0.3 s with no oscillation or overshoot. The improved system performed well in replicating the random vibrations of heavy vehicles operating on Class B and C roads. This confirmed that the system can satisfy the loading requirements of heavy vehicle hydropneumatic suspensions and can be used as a simulation test bench for such suspensions.

Research on Control Strategy of APSO-Optimized Fuzzy PID for Series Hybrid Tractors

Energy management strategies are crucial for improving fuel economy and reducing the exhaust emissions of hybrid tractors. The authors study a series diesel-electric hybrid tractor (SDEHT) and propose a multi-operating point Fuzzy PID control strategy (MOPFPCS) aimed to achieve better fuel economy and improved control. To further improve the vehicle economy, the adaptive particle swarm optimization method is used to optimize the key parameters of the Fuzzy PID controller. A co-simulation model in AVL-Cruise and Matlab/Simulink environment is developed for plowing mode and transportation mode. The simulation results show that under the two operation modes, the equivalent fuel consumption of the adaptive particle swarm optimization multi-operating points Fuzzy PID control strategy (APSO-MOPFPCS) is reduced by 18.3% and 15.0%, respectively, compared to the engine single-operating point control strategy (ESOPCS). Also, it was found to be reduced by 9.5% and 4.6%, respectively, compared to the MOPFPCS.

Application study of the power control system for air-cooled proton exchange membrane fuel cell based on fuzzy adaptive with heuristic adaptive period strategy

As a zero-emission, non-polluting power generation device, hydrogen fuel cells are of great interest to the global industry with the advantages of high-power generation efficiency, wide source, and wide application scenarios. Influenced by various factors, it leads to a complex fuel cell control system and unstable output characteristics, which is one of the difficulties in the current technology development. In this section, an embedded power control system according to an STM32 microcontroller is developed to improve the steady-state characteristics of cathode open air-cooled fuel cell (PEMFC), and a fuzzy adaptive PID control algorithm according to the heuristic adaptive strategy of time interval is proposed. The control system achieves real-time detection of operating status. It maintains the stable output of cathode open air-cooled PEMFC by predicting vehicle speed in real-time to precisely control the hydrogen supply and external additional fans to control the operating temperature by the control strategy.

Non-Invasive Intraoral Stand-Alone Tongue Control System Based on RSIC-V Edge Computing

The intelligent tongue control system is of great significance for assisting the independent life of patients with a limb disability. In order to more accurately control the assisted living equipment of incompetent patients and solve the power-loss problem of the intelligent tongue control system, this research designs a non-invasive pressure sensor array for tongue touch signal detection in the oral cavity and proposes a tongue control system based on RSIC-V edge computing. The system converts the tongue touch pressure data into specific control instructions on the edge of the RSIC-V chip and transmits them to the receiver, thus reducing the transmission of data. This study takes control of the wheelchair motor as the test object. In the experiment, the speed response time test, the center click task, and the power consumption experiment are carried out, whose results show that the adaptive fuzzy PID control algorithm has good robustness in the system; when the DC motor with a given speed of 750 r/min reaches the steady state, its rise time is 0.108 s and the adjustment time is 0.59 s. The dynamic power consumption of the non-intrusive intraoral stand-alone tongue control system proposed in this paper is found to be 3.745 MW, which is 11.5% lower than the total power consumption of the sTD system.

Continuous gradient temperature control of microfluidic chip based on thermoelectric cooler

High-efficiency and accurate temperature control is the key to the application of microfluidic technology in biology, chemistry and medicine fields. At present, gradient temperature control of microfluidic chip in a wide range below room temperature still faces challenges. Based on a microfluidic platform of double emulsion droplets, this study builds a temperature control system and attempts to achieve continuous gradient temperature control on time scale of microfluidics based on thermoelectric cooler. Temperature control unit is designed and relevant control strategies are discussed to achieve accurate temperature control. Meanwhile, the strategy to achieve continuous gradient temperature control of microfluidic chip is mainly explored. Finally, combining with the working characteristics of thermoelectric cooler, continuous gradient temperature control of microfluidic chip is experimentally realized based on adaptive fuzzy PID control, while PID control shows remarkable overshoot, deviation and oscillation. Moreover, small gradient temperature control from 10 °C down to −15 °C, −20 °C and then −25 °C is precisely realized by using adaptive fuzzy PID control, as well as large gradient temperature control from 20 °C down to 0 °C and then −20 °C. Specifically, the maximum deviation of temperature is 0.192 °C in small gradient temperature control, and the steady state error is within ±0.07 °C. In large gradient temperature control, the maximum deviation of temperature is 0.309 °C, and the steady state error was within ±0.1 °C. This study provides diversified temperature control in a wide range for microfluidic analysis.

Multi-objective optimal design of a fuzzy adaptive robust fractional-order PID controller for a nonlinear unmanned flying system

In this study, a fuzzy adaptive robust fractional-order proportional-integral-derivative (FARFOPID) controller is introduced for stabilization of a nonlinear unmanned flying system. At first, a proportional-integral-derivative scheme improved by the fractional-order technique is employed to control the complicated dynamics of the considered system. An adaptive mechanism based on sliding surfaces is proposed to update the effective gains of the fractional-order PID controller. To improve the performance of the controller in the presence of disturbances and uncertainties, fuzzy systems are used to adjust the parameters of the sliding surfaces. The fuzzy systems, considered in this article, employ the singleton fuzzifier, product inference engine and center average defuzzifier as well as triangular-trapezoidal membership functions. Then, the multi-objective thermal exchange optimization (MOTEO), which has presented a high convergence speed and good performance for global search problems, is utilized to determine the controller parameters in such a way that control efforts and system errors would be minimized, simultaneously. Finally, simulation results clearly depict the effectiveness and efficiency of the proposed approach to handle the regarded unmanned flying system in comparison with other controllers.

Design of a Closed Piggery Environmental Monitoring and Control System Based on a Track Inspection Robot

To improve environmental quality in enclosed piggeries, a monitoring and control system was designed based on a track inspection robot. The system includes a track mobile monitoring platform, an environmental control system, and a monitor terminal. The track mobile monitoring platform consists of three main components: a single-track motion device, a main box containing electronic components, and an environmental sampling device. It is capable of detecting various environmental parameters such as temperature, humidity, NH3 concentration, CO2 concentration, light intensity, H2S concentration, dust concentration, and wind speed at different heights below the track. Additionally, it can control on-site environmental control equipment such as lighting systems, ventilation systems, temperature control systems, and manure cleaning systems. The networked terminal devices enable real-time monitoring of field equipment operating status. An adaptive fuzzy PID control algorithm is embedded in the system to regulate the temperature of the piggery. Field tests conducted on a closed nursery piggery revealed that the system effectively controlled the maximum temperature range within 2 °C. The concentrations of CO2, NH3, and PM2.5 were maintained at a maximum of 1092 mg∙m−3, 16.8 mg∙m−3, and 35 μg∙m−3, respectively. The light intensity ranged from 51 to 57 Lux, while the wind speed remained stable at approximately 0.35 m∙s−1. The H2S concentration was significantly lower than the standard value, and the lowest relative humidity recorded was 18% RH at high temperatures. Regular humidification is required in closed piggeries and other breeding places when the system does not trigger the wet curtain humidification and cooling function, as the relative humidity is lower than the standard value. By controlling the temperature, the system combined with a humidification device can meet environmental requirements. The control method is simple and effective, with a wide range of applications, and holds great potential in the field of agricultural environmental control.

Clamping Force Control Strategy of Electro-Mechanical Brake System Using VUF-PID Controller

Clamping force control is one of the key technologies in the algorithm design and implementation of electro-mechanical braking system, whose control effects directly affect the vehicle braking performance and safety performance. In order to improve the clamping force control performance of electro-mechanical braking (EMB) system, an EMB clamping force control method based on Variable universe adaptive fuzzy PID (VUF-PID) controller is proposed, and stretching factors are added to the fuzzy PID control. According to the operation of the controlled object, the fuzzy theory domain can be adjusted in real time to keep the system in the proper parameter value and improve the adaptive ability of the system. The response characteristics and effectiveness of clamping force under step braking condition, gear switching braking condition and sine braking condition are verified by simulation experiments using MATLAB/Simulink. The results show that the proposed VUF-PID control method has strong tracking characteristics and stability characteristics, and meet the braking requirements under different braking conditions.