PID Controller 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.

Design and Application of Fuzzy PID Controller Based on MATLAB

In an era where the development of intelligent technology is a pivotal force propelling economic advancement, enhancing production efficiency, diminishing costs, and reshaping daily life, the field of intelligent vehicle control technology has garnered significant attention among automotive researchers. The dynamics of smart cars during driving often exhibit nonlinearity and are susceptible to external environmental disturbances, such as wind resistance and road surface inequality. Traditional PID controllers have limited ability to suppress these interferences, so this study uses fuzzy PID algorithms to solve the inherent challenges in intelligent vehicle control. Through comprehensive analysis of the current landscape and technological advancements in intelligent vehicle control systems, coupled with rigorous MATLAB/Simulink simulations and real-world testing, the study showed that the overshoot of the adaptive fuzzy PID was reduced by 86% compared to the traditional PID. Consequently, it markedly enhances the precision and stability of speed control in intelligent vehicles, providing a groundbreaking perspective and practical methods for the enhancement of intelligent vehicle control systems with considerable application potential.

Drift compensation system of an autocollimator based on reciprocal roll angles and a feedback combination target

To address the issues of limited field of view and beam drift in traditional autocollimation measurements, a novel, to our knowledge, autocollimation measurement method is proposed. This method is based on the principle of full-circle continuous measurement using reciprocal roll angles of a compound biaxial system, combined with a feedback and compensation mechanism for beam drift using both calibration and compensation targets. A self-collimating small-angle measurement system model, coupled with a compound biaxial turntable and combined target, is established using ray tracing and perspective projection transformation techniques. Additionally, a closed-loop beam drift compensation system is developed by applying a BP neural network and a PID control algorithm. Experimental tests on the compensation system demonstrate a significant improvement in measurement accuracy and stability. The results show that this method satisfies the requirements for small-angle autocollimation measurement, and the method also enhances the overall stability of the autocollimation system.

A control-oriented comprehensive model of PEM electrolyzer considering double-layer capacitance

The modeling of electrochemical and thermal characteristics of proton exchange membrane (PEM) electrolyzer is significant in the hydrogen production process. However, the calculation of electrode impedance and double-layer capacitance during operation is not accurate enough currently, and the electrothermal coupling characteristics of hydrogen production devices are not fully considered. In this paper, the multi-stage equivalent circuit of the PEM electrolyzer is designed using electrochemical impedance spectroscopy (EIS) and the time-constant fitting method. In addition, according to the temperature control system of the actual hydrogen production device, the electro-thermal model of the PEM electrolyzer is established, in which the fuzzy PID control is used to maintain the temperature stability. Moreover, the electrical characteristics and temperature characteristics are simulated in different time scales. Finally, compared to the existing models, the proposed model is more accurate, and it indicates that the proposed model has better accuracy against the existing models.

Trajectory Planning and Performance Atlases of a New Omnidirectional Conveyor

This paper proposes an omnidirectional conveyor as a novel alternative to existing omnidirectional conveyors. With a symmetric and compact layout, this new structure ensures consistent kinematics and enhanced flexibility in trajectory planning. The kinematic model of the proposed omnidirectional conveyor is developed and verified through simulation in CoppeliaSim. Four typical classes of trajectories are generated and verified in the simulation environment. Using PID control, the actual trajectories of a package on the conveyor closely match the desired trajectories. In addition, this paper outlines the workspace and corresponding wheel patterns for the conveyor, demonstrating how different supported wheel patterns emerge when packages move across various areas of the conveyor. The discussion extends to fault tolerance and obstacle avoidance, examining the workspace and wheel patterns with one or two omni wheels failed. Furthermore, this paper provides a comprehensive analysis of the feasible desired movements for the packages on the conveyor under the constrained wheel speed. This provides insights and guidance on trajectory planning and design of the conveyor.

Observer‐based PID control for fuzzy dynamic systems with memory triggering protocol

AbstractThis paper addresses the issue of observer‐based proportional–integral–derivative control for Takagi–Sugeno fuzzy dynamic systems using a memory triggering protocol. To tackle the challenges of managing nonlinear systems, the Takagi–Sugeno fuzzy approach is employed to approximate these systems with a series of local linear models. A novel memory event‐triggering mechanism is introduced, utilizing a computer's storage unit to store historical data and adjust the triggering threshold based on this data, thereby reducing unnecessary network resource usage. Given that the system state can be affected by external disturbances and become unpredictable, an observer‐based proportional–integral–derivative controller is designed to manage these uncertainties. Finally, a car‐damper‐spring model is presented to validate the proposed methodology.

Nonlinear second order plus time delay model identification and nonlinear PID controller tuning based on extended linearization method

PID control systems based on the first order plus time delay model (FOPTD), which approximate the full system dynamics, are well-accepted for a wide range of linear processes. While such controllers can be applied to overdamped nonlinear processes, they often experience excessive overshoots and oscillations for general nonlinear processes. To overcome this limitation, we propose a novel method to design a nonlinear PID controller based on the second order plus time delay (SOPTD) model. The system nonlinearity requires parameter adjustments of the linearized model across operational ranges. Hence, in this work, it is handled by the extended linearization method (ELM), ensuring local stability under the assumptions of slow and small changes in operating points. Importantly, the model achieves global input-to-output stability even without the above constraints, provided there are no structural and parametric errors. The resulting nonlinear SOPTD model can describe changes in process gain and two time constants as the operation point varies. We demonstrate the applicability of our approach with a polymerization reactor simulation and liquid-level control experiments.

Adaptive fault-tolerant containment control for heterogeneous uncertain nonlinear multi-agent systems under actuator faults

As modern control systems involve a growing number of actuators, the consequent number of faults occurrence, which may deteriorate the closed-loop system performance, is increasing. Focusing on the containment control problem of generic nonlinear high-order heterogeneous uncertain Multi-Agent Systems, this study proposes a novel distributed adaptive fault-tolerant Proportional-Integral-Derivative control protocol to improve the reliability and the resilience of the whole networked systems against actuator failures. The Lyapunov theory, combined with the Barbalat lemma, provides the design of suitable adaptive mechanisms that adjust the PID control actions to ensure the asymptotic convergence of the closed-loop containment errors for the overall network, hence implying the convergence of each agent state toward the convex region shaped by the multiple leaders, despite the occurrence of actuators faults. The fulfillment of the fault-tolerant containment objective is ensured by requiring a lower computational burden with respect to alternative solutions, hence resulting in more attractive for wider practical engineering applications. Numerical simulation results, also including a comparison analysis with a state-of-the-art controller, corroborate the efficiency and the advantages of the proposed distributed PID controller.

Cascade Model Predictive Control for Enhancing UAV Quadcopter Stability and Energy Efficiency in Wind Turbulent Mangrove Forest Environment

Unmanned aerial vehicle (UAV) modelling and control, particularly in quadrotors with high position-orientation coupling, present significant challenges for practical applications, such as environmental monitoring missions in windy mangrove forests. Conventional control strategies like the PID controller, often employed in simulations due to their simplicity, often underperform in real-world scenarios due to their linear assumptions. This research proposes a novel hierarchical cascaded model predictive control system for altitude, attitude, and battery efficiency for quadrotor in mangrove area. This control system addresses computational complexity by decomposing the overall MPC strategy into two distinct schemes, one for translational displacements and another for rotational movements, enhancing the UAV's resilience to wind turbulence, a significant disturbance factor in mangrove environments. Rigorous simulation and experiment test flight involving complex trajectory tracking and windy conditions demonstrate the proposed controller's superior performance compared to conventional PID controller, particularly in terms of stability, disturbance rejection, underscoring its potential for UAV applications in challenging environments.

Design of active disturbance rejection controller to realize temperature control of PEMFC

In the proton exchange membrane fuel cell (PEMFC) systems, the operating temperature affects the gas transport, reaction rate and water balance inside the PEMFC stack, thus affecting the output performance and lifetime of the stack. Therefore, it is necessary to study the thermal management methods and control strategies to maintain the stack temperature at the expected value. A thermal management system model of the PEMFC based on the electrochemical reaction and thermodynamics is established. This study proposes a novel active disturbance rejection controller (ADRC) to solve the problem of excessive temperature fluctuation of liquid-cooled stack with the dynamic load current changes. The simulation data show that compared with the PID, Fuzzy-PID and PSO-PID controllers for the cooling fan, the overshoot amount of the coolant inlet temperature with the ADRC is significantly reduced from 1.02 %, 0.77 % and 0.26 % to 0.07 %, and the integral of time absolute error (ITAE) is decreased from 5.299 × 104, 5.206 × 104 and 1.255 × 104 to 3.930 × 103, respectively. The degree of temperature fluctuation is reduced and the parasitic power does not significantly increase. Therefore, the ADRC proposed in this study improves the temperature control effect of the PEMFC stack, which is conducive to enhancing the stack performance.

Dynamic Multi-objective Optimization based on Classification Response of Decision Variables

In recent years, many dynamic multi-objective optimization algorithms (DMOAs) have been proposed to address dynamic multi-objective optimization problems (DMOPs). Most existing DMOAs treat all decision variables uniformly and respond to them in an identical manner. This paper proposes a dynamic multi-objective optimization algorithm based on the classification response of decision variables (CRDV-DMO). Firstly, CRDV-DMO categorizes the decision variables into convergence variables and diversity variables. Different decision variables adopt distinct response strategies. The response strategy of diversity variable (RSDV) uses Latin hypercube sampling to generate the diversity variables of the new environment. For each dimensional convergence variable, the response strategy of convergence variable (RSCV) first evaluates whether the basic center prediction strategy (CPS) yields positive feedback or negative feedback, further determining the predictability of that dimensional convergence variable. RSCV then decides to either use the basic CPS to generate the convergence variable for that dimension or to retain that dimensional convergence variable from the current environment, based on the predictability of that dimensional convergence variable. The proposed algorithm is extensively studied through comparison with several advanced DMOAs, demonstrating its effectiveness in dealing with the benchmark DMOPs and the parameter-tuning problem of the PID controller on a dynamic system.

Simplified dynamic modeling and Fuzzy gain scheduled PID control of woodward-governor controlled grid-interactive twin-shaft gas turbine plants

Simplified dynamic modeling and Fuzzy gain scheduled PID control of woodward-governor controlled grid-interactive twin-shaft gas turbine plants

A multi-energy inertia-based coordinated voltage and frequency regulation in isolated hybrid power system using PI-TISMC

This paper proposes novel multi-energy inertia support for simultaneous frequency and voltage control of an isolated hybrid power system (IHPS). Multi-energy storage (gas inertia – hydrogen storage, thermal inertia – solar thermal storage, hydro inertia – gravity hydro storage, chemical inertia – battery energy storage) supported by demand side management (DSM) for simultaneous voltage and frequency regulation and backed by biodiesel generators, are the essential elements of IHPS. A novel control strategy of concurrent virtual droop control, virtual damping control, virtual inertia control, and virtual negative inertia control is proposed to utilise multiple inertia sources and to improve LFC and AVR performance effectively. The effective coordination of inertia sources in eradicating oscillations in IHPS, is aided by a developed cascaded proportional integral-tilt-integral-sliding mode (PI-TISMC) controller. The performance of PI-TISMC is compared with PID, PI-PID, and PI-SMC controllers. A maiden attempt has been done by training five diverse classes of optimization techniques to optimize the parameters of controllers in the present work. The results are evaluated in MATLAB and it is evident from the results that the performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). The performance of frequency control is improved by 6.5%, 7.8% and 3.4 s (over shoot, undershoot, and settling time). Similarly, the performance of voltage control is improved by 6.7%, 4.8% and 2.3 s (over shoot, undershoot, and settling time) by employing developed PI-TISMC controller and proposed concurrent inertia control. The combination exhibits superior performance in minimizing oscillations in IHPS due to variations in loading and solar insolation.

Cyber attacks detection and mitigation in hybrid power system using type-2 fuzzy controller

Abstract This paper presents a novel control technique using Fuzzy Type-2 controller for regulating the frequency deviations in an independent Hybrid Power System (HPS). The HPS comprises of a wind system and a solar photovoltaic (PV) system as primary energy sources, along with Fuel Cells (FCs) as additional generation systems and Double Layer Capacitor (DLC) Banks as storage devices to meet the load demand. The frequency variations occur in the HPS due to the intermittency in power outputs from the primary sources, the load changes as well as any attacks in the system. The frequency regulation is achieved by effective coordination control of FC and DLC with the aid of a Fuzzy Type-2 controller and a high pass filter (HPF). The DLC technology adequately satisfies the residual power of hybrid systems resulting from the slow dynamics of FCs. The Fuzzy Type-2 controller is trained using an input-output data set of a PID controller whose gains are tuned to their optimal values using Monte-Carlo state estimators. The proposed system is simulated using real weather data to validate the capability of the Hybrid Power System (HPS) in supplying isolated loads while maintaining power frequency balance conditions. Also the HPS is subjected to attacks in daily load variation and the results are compared with the system without attacks. The simulation results show that the peak over shoots are reduced considerably but the settling time is 0.2 seconds in the system with no attacks where as the settling time is prolonged to 1.2 seconds with the attacks. This paper demonstrates the effectiveness of the Fuzzy Type-2 controller for the coordination control of DLC and FC to achieve generation-load balance condition in comparison with that of the PID controller for HPS system with and without attacks.

Enhanced Depth Control and Stability in Submersible Pylon Inspection Robots Using IMU-Based Extended Kalman Filter and PID Control

The research developed a Submersible Pylon Inspection Robot (SPIR) to clean and inspect underwater structures automatically, facing challenges in stability and depth of operation. Integrating the Inertial Measurement Unit (IMU) with the Extended Kalman Filter (EKF), as well as implementing customized PID controls, aims to improve the accuracy and stability of SPIR in dynamic underwater environments. The results show that the SPIR control system can follow the reference depth at shallow depths well, but has difficulty maintaining stability at deeper depths. The position error graph on the Z-axis shows the initial fluctuation that decreases over time, reflecting the increased calibration. The use of drive motors also shows different working patterns, with some motors active in depth settings and others for stabilization. This study shows that the integration approach of IMU, EKF, and PID control can improve SPIR performance, although further adjustments are required to meet the extreme challenges in underwater environments.

Optimizing actual PID control for walking quadruped soft robots using genetic algorithms

The construction of soft robots’s models and controllers remains a significant challenge. In this paper, we propose a new walking control method for the quadruped soft robot named genetic algorithm-optimized PID. First, we construct the control model correlating valve voltage with leg bending based on the geometrical analysis. This modeling approach leverages the characteristics of novel leg structure and bend sensor, thereby streamlining the control model for locomotion of quadruped soft robotic. Moreover, We apply the genetic algorithm to automatically tune parameters and optimize PID controllers, aiming to enhance control performance. The application of the proposed method to the walking control has been uniquely demonstrated on a real 3D-printed quadruped soft robot. Experimental results indicate that the genetic algorithm-optimized PID controller significantly improves trajectory tracking compared to the Ziegler-Nichols tuning method. This optimization increases the robot’s walking speed from 5 mm/s to 8 mm/s, reduces the error rate by 2.4064%, decreases overshoot by 12.55%, and shortens response time by 0.5 s, substantially enhancing the controller’s overall performance. Additionally, compared to particle swarm optimization, the proposed method further improves performance by reducing the error rate by 0.4079%, overshoot by 8.4%, and response time by 1.0 s.

Optimization of PID Control of a Snake Robot’s Winding Crawling Motion

This paper explores the application of PID control to the winding crawling motion of a snack robot. Snack robots, mimicking the movement of real life limbless reptile, are designed with ultiple connected segments equipped with actuators and sensors, enabling them to navigate complex environments. The study aims to optimize the ground crawling behavior and obstacle avoidance f the snake-like robot using MATLAB simulation software. Initially, the robot’s joints were modeled in SolidWorks, and their degrees of freedom were programmed in MATLAB’s Simulink to enable controlled rotation. The PID control system was added to manage the robot’s motion, ensuring smooth and accurate crawling.

Research on obstacle avoidance of underactuated autonomous underwater vehicle based on offline reinforcement learning

Abstract The autonomous navigation and obstacle avoidance capabilities of autonomous underwater vehicles (AUVs) are essential for ensuring their safe navigation and long-term, efficient operation. However, the complexity of the marine environment poses significant challenges to safe and effective obstacle avoidance. To address this issue, this study proposes an AUV obstacle avoidance control algorithm based on offline reinforcement learning. This method adopts the Conservative Q-learning (CQL) algorithm, which is based on the Soft Actor-Critic (SAC) framework. It learns from obtained historical obstacle avoidance data and ultimately achieves a favorable obstacle avoidance control strategy. In this method, PID and SAC control algorithms are utilized to generate expert obstacle avoidance data to construct a diversified offline database. Additionally, based on the line-of-sight (LOS) guidance method and artificial potential field (APF) method, information regarding the distance and orientation of targets and obstacles is incorporated into the state space, and heading and obstacle avoidance reward terms are integrated into the reward function design. The algorithm successfully guides the AUV in autonomous navigation and dynamic obstacle avoidance in three-dimensional space. Furthermore, the algorithm exhibits a certain degree of anti-interference capability against uncertain disturbances and ocean currents, enhancing the safety and robustness of the AUV system. Simulation results fully demonstrate the feasibility and effectiveness of the intelligent obstacle avoidance method based on offline reinforcement learning. This study highlights the profound significance of offline reinforcement learning in enabling robust and reliable control systems for AUVs, paving the way for enhanced operational capabilities in challenging marine environments.

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.

Chaotic-Based Improved Henry Gas Solubility Optimization Algorithm: Application to Electric Motor Control

This study presents a novel meta-heuristic optimization method that combines the Henry Gas Solubility Optimization (HGSO) technique with symmetric chaotic systems. By leveraging the randomness of chaotic systems, the parameters of the HGSO algorithm that require random generation are produced through chaotic processes, allowing the algorithm to exhibit chaotic behavior in its pursuit of optimal values. This innovative approach is termed Chaotic Henry Gas Solubility Optimization (CHGSO), with the primary objective of enhancing the performance of the HGSO method. The randomness of the data obtained from chaotic systems was validated using NIST-800-22 tests. The CHGSO method was applied to both 47 benchmark functions and the optimization of parameters for a PID controller utilized in the speed control of a DC motor. To evaluate the effectiveness of the proposed method, it was compared with several widely recognized algorithms in the literature, including PSO, WOA, GWO, EA, SA, and the original HGSO algorithm. The results demonstrate that the proposed method achieved the best performance in 43 of the benchmark functions, outperforming the other algorithms. In the context of controller design, the PID parameters were optimized using the error-based ITSE objective function. According to the controller responses, the proposed method has achieved the best results in the simulation studies, with a settling time of 0.035 and a rise time of 0.014 without overshooting, and in the experimental studies, with a settling time of 0.15 and a settling time of 1.4%. When the results are examined, it is observed that it has achieved successful results in the controller design problem.