Adaptive PID Controller Research Articles

A Robust Hybrid MRAPID Controller Design for an Underactuated Nonlinear System With Parametric Uncertainty

This paper presents an optimal design and simulation of a novel enhanced model reference adaptive proportional–integral–derivative controller (MRAPIDC) for underactuated nonlinear systems with parametric uncertainty. The controller is applied to a cart‐inverted pendulum mechanical system, a fourth‐order, nonminimum phase system with pronounced nonlinearity and unstable dynamics. This system includes a single input and multiple outputs, specifically the position and velocity of the cart, as well as the angle and angular velocity of the inverted pendulum arm. The main objective of the proposed controller is to maintain the pendulum in an upright position against gravity while moving the cart to a desired displacement regardless of parametric uncertainty, input variation, external disturbances, and random noise. The hybrid controller design is developed by combining the MRAPIDC design with controller gains applied to each output and incorporating the system adaptation error in a specific configuration. Optimal performance is achieved through parameter tuning via the social spider optimization (SSO) algorithm, and MIT (Massachusetts Institute of Technology) rule criteria are employed to ensure system stability. Performance improvements are demonstrated through several error indices, showing enhanced transient and steady‐state responses compared to both the reference model adaptive controller and the conventional proportional–integral–derivative (PID) controller. The proposed controller can effectively achieve system stability with a short settling time of 2.55 s and establishes a steady swinging balance within 2.9 s.

Adaptive PID control based on exponential forgetting recursive least squares for 2DOF robotic manipulator

Adaptive PID control based on exponential forgetting recursive least squares for 2DOF robotic manipulator

Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System

Application of Reinforcement Learning-Based Adaptive PID Controller for Automatic Generation Control of Multi-Area Power System

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.

Neural Network-Based Adaptive PID Controller Design for Over-Frequency Control in Microgrid Using Honey Badger Algorithm

Neural Network-Based Adaptive PID Controller Design for Over-Frequency Control in Microgrid Using Honey Badger Algorithm

FPGA-Based Adaptive PID Controller Using MLP Neural Network for Tracking Motion Systems

FPGA-Based Adaptive PID Controller Using MLP Neural Network for Tracking Motion Systems

BLDC Motor Stability Management Using Adaptive PID (MRAC-PID)

BLDC motors have become popular in various industries such as automotive, consumer, healthcare, industrial automation, and instrumentation due to their optimal performance. To keep the BLDC motor in optimal condition, a control engineering system is required that serves as a controller. A single Proportional Integral Derivative (PID) control system is only suitable for linear conditions, so it cannot produce satisfactory output when there is a change in set point. To overcome this obstacle, an adaptive PID control system known as MRAC-PID control system is applied, which is able to control the stability of the BLDC motor as desired. Testing of this system is done under 4 different conditions using MATLAB software. After testing, the parameter values for the MRAC control system were obtained, namely ???????? = 0.4; ???????? = 50.75; ???????? = 0.0000867. Based on the test, the MRAC control system produces a system success rate of 81.2% to 98.9%.

An Adaptive PID Control System for the Attitude and Altitude Control of a Quadcopter

Abstract In adaptive model-based control systems, determining the appropriate controller gain is a complex and time-consuming task due to noise and external disturbances. Changes in the controller parameters were assumed to be dependent on the quadcopter mass, which was the process variable. A nonlinear model of the plant was used to identify the mass, employing the weighted recursive least squares (WRLS) method for online identification. The identification and control processes involved filtration using differential filters, which provided appropriate derivatives of signals. Proportional integral derivative (PID) controller tuning was performed using the Gauss–Newton optimisation procedure on the plant. Differential filters played a crucial role in all the developed control systems by significantly reducing measurement noise. The results showed that the performance of classical PID controllers can be improved by using differential filters and gain scheduling. The control and identification algorithms were implemented in an National Instruments (NI) myRIO-1900 controller. The nonlinear model of the plant was built based on Newton’s equations.

Enhancing the transient stability of interconnected power systems by designing an adaptive fuzzy-based fractional order PID controller

The reliable and efficient operation of the electrical energy transmission system heavily relies on maintaining stability as a key requirement. Interconnected power systems often experience low-frequency oscillations (LFOs), which have the potential to initiate instability and, subsequently, necessitate careful attention and investigation. To tackle this issue, researchers have focused on various controllers, including Fractional-Order Proportional-Integral-Derivative (FOPID) controllers. The FOPID controller, with its increased number of design parameters, has proven to be more successful than the traditional PID controller in various engineering applications. However, determining the optimal parameters for the controller becomes more challenging due to the increased design space. Moreover, the performance of fixed-gain FOPID controllers may be degraded when confronted with highly nonlinear and uncertain controlled objects such as power systems. This issue is primarily attributed to the static nature of fixed-gain FOPID controllers. To overcome these challenges associated with fixed-gain FOPID controllers and to instill an adaptive quality, this article introduces a novel power system control methodology. This adaptive approach combines a FOPID control strategy with a fuzzy logic system and is denoted as EOA-AFFOPID. The proposed adaptive controller dynamically tunes its coefficients through a fuzzy block, which significantly improves the overall performance of the control system. To establish a performance benchmark, we have also created an alternative controller, named EOA-FOPID, a version of the optima FOPID controller with fixed coefficients. The proposed methodology is applied to multi-machine interconnected power systems equipped with Static Synchronous Series Compensators (SSSCs), and comparative evaluations are conducted with other popular recent control strategies. The comparisons demonstrate the effectiveness of the EOA-AFFOPID control strategy in damping system fluctuations and achieving significant performance improvements in terms of different metrics including overshoot, undershoot, and settling time.

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.

A Novel Model-Free Adaptive Proportional–Integral–Derivative Control Method for Speed-Tracking Systems of Electric Balanced Forklifts

Similar to many complex systems, the operation process of electric balanced forklifts has characteristics such as time-varying model parameters and nonlinearity. Establishing an accurate mathematical model becomes challenging, making it difficult to apply model-based control methods in engineering practice. Aiming at the longitudinal control system of electric forklifts containing external disturbances, this paper proposes an improved full-format dynamic linearization model-free adaptive PID control (iFFDL-MFA-PID) method. Firstly, the full-format dynamic linearization (FFDL) method is employed to transform the operating system of the electric balanced forklift into a virtual equivalent linear data model. Secondly, the nonlinear residual term and pseudo-gradient (PG) of the data model are estimated using the difference estimation algorithm and the optimal criterion function, respectively. Furthermore, in order to enhance the robustness of the system, the idea of intelligent PID (iPID) is introduced and the principle of equivalent feedback is utilized to derive the iFFDL-MFA-PID control scheme. The design process of this scheme only requires the use of the input and output data of the system, without relying on the mathematical model of the system. Finally, the iFFDL-MFA-PID method proposed in this paper is simulated and tested with the EFG-BC/320 counterbalanced forklift equipped in the Special Equipment Testing Center and compared with the model-free adaptive control method (FFDL-MFAC) and the PID control method. Simulation results show that the speed-tracking error of the electric forklift truck under the action of the iFFDL-MFA-PID algorithm is maintained within ±0.132 m/s throughout the process, achieving higher tracking accuracy and better robustness compared to the MFAC and PID methods.

Adaptive PID Controller for Active Suspension Using Radial Basis Function Neural Networks

Suspension systems are critical parts of modern cars. In this study, a radial basis function neural networks-based adaptive PID optimal method is presented for vehicle suspension systems. To avoid the shortcoming that the parameters of PID control are determined by experience in the traditional method, to avoid the local optimality problem and the slow rate of convergence in the modern intelligence method, radial basis function neural networks are applied in this paper. First, a quarter-car suspension is presented. Then, the radial basis function neural networks are employed to obtain the parameters of proportional, integral, and derivate components that are used in PID control. The simulation is conducted later. Next, a comparison of the progress between uncontrolled suspension, the radial basis function-based PID control, the H∞ control method, and the FPM control method is presented. According to the simulation results, the proposed control method performs better than the others. This contrast reveals the superior characteristics of the suggested control strategy.

Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot

High-precision and low-overshoot force control are important to guarantee the material removal rate and surface quality of robot grinding. However, traditional force control methods are subjected to positional disturbance, stiffness disturbance, contact process nonlinearity, and force-position coupling, leading to difficulties in robot constant force control. Therefore, how to achieve smooth, stable, and high-precision constant force control is an urgent problem. To address this problem, a dual PID adaptive variable impedance control is established (DPAVIC). Firstly, PD control is used to compensate for the force error, and PID is used to update the damping parameters to compensate for the disturbance. Secondly, a nonlinear tracking differentiator is used to smooth the desired force and reduce the contact force overshoot. Then, the stability, convergence, and effectiveness of the force control algorithm are verified via theoretical analysis, simulations, and experiments. The force tracking error and overshoot of a conventional impedance controller (CIC), adaptive variable impedance control (AVIC), and DPAVIC are analyzed. Finally, the algorithm is used in grinding experiments on a thin-walled workpiece. The force tracking error is controlled within ±0.2 N, and the surface roughness of the workpiece is improved to Ra 0.218 μm.

Design model-free adaptive PID controller based on lazy learning algorithm

Abstract The nonlinear system is difficult to achieve the desired effect by using traditional proportional integral derivative (PID) or linear controller. First, this study presents an improved lazy learning algorithm based on k-vector nearest neighbors, which not only considers the matching of input and output data, but also considers the consistency of the model. Based on the optimization index of an additional penalty function, the optimal solution of the lazy learning is obtained by the iterative least-square method. Second, based on the improved lazy learning, an adaptive PID control algorithm is proposed. Finally, the control effect under the condition of complete data and incomplete data is compared by simulation experiment.

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>

Current status and prospects of adaptive PID control in spacecraft attitude control

Spacecraft attitude control is a precise aspect that requires accurate operation. Adaptive PID control, in this case, could be a promising approach for improving the performance. In order to provide a better comprehension about the adaptive PID control in spacecraft attitude control, This paper will mainly discuss the aspect in current solutions, challenges, and future directions through providing and interpreting the scientific and reliable essays in this area. Several kinds of classic adaptive PID control will be introduced in the paper for their feasibility and applicability. Then, paper will discuss several kinds of limitations faced by traditional adaptive PID control in reality such as the complexity of spacecraft dynamics. To overcome these limitations, this paper will also discuss a couple of machine learning based adaptive PID control proposed by researchers such as fuzzy logic systems. These adaptive schemes can adjust the controller parameters through advanced algorithms to ameliorate the control performance of the spacecraft.

Adaptive PI Controller Based on a Reinforcement Learning Algorithm for Speed Control of a DC Motor.

Automated industrial processes require a controller to obtain an output signal similar to the reference indicated by the user. There are controllers such as PIDs, which are efficient if the system does not change its initial conditions. However, if this is not the case, the controller must be retuned, affecting production times. In this work, an adaptive PID controller is developed for a DC motor speed plant using an artificial intelligence algorithm based on reinforcement learning. This algorithm uses an actor-critic agent, where its objective is to optimize the actor's policy and train a critic for rewards. This will generate the appropriate gains without the need to know the system. The Deep Deterministic Policy Gradient with Twin Delayed (DDPG TD3) was used, with a network composed of 300 neurons for the agent's learning. Finally, the performance of the obtained controller is compared with a classical control one using a cost function.

Adaptive Impedance Control for Force Tracking in Manipulators Based on Fractional-Order PID

Force tracking control in robot arms has been widely used in many industrial applications, particularly in tasks involving end effectors and environmental contact, such as grinding, polishing, and other similar operations. However, these environments are not always precisely known. In order to address the force tracking control problem in unknown environments, this paper proposes a fractional-order PID adaptive impedance control strategy based on traditional impedance control. The unknown environmental information is estimated online using the adaptive impedance control algorithm, and the estimated parameters are used to generate reference trajectories to reduce force tracking errors. Fractional-order PID control is then introduced into the system to improve the control performance of the system model, and the theoretical proof of strategy stability is conducted. Finally, a comparison of four strategies was conducted through simulations: traditional impedance control, adaptive hybrid impedance control, adaptive variable impedance control, and the fractional-order PID impedance control proposed in this paper. The simulation results demonstrate that the strategy proposed in this paper exhibits robustness, virtually eliminates overshoot, and enhances response speed. In contrast, both adaptive hybrid impedance control and adaptive variable impedance control exhibit approximately 30% to 45% overshoot during interactions with the environment. Furthermore, in terms of force tracking error, the proposed strategy in this paper outperforms the above two strategies by approximately 29% to 60%, achieving excellent force tracking control performance.

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.