Small Steady-state Error Research Articles

Robust control algorithm designed for robotic legs of lunar-based equipment: Modeling and experiment

In lunar exploration, lunar-based equipment assumes the responsibility of mobility and transportation during the construction of unmanned lunar base. Legged robots are an important form of the lunar-based equipment due to their mobility and terrain adaptability. However, the influence of lunar terrain and lunar soil cannot be ignored when it comes to studying the mobile stability of lunar-based equipment. Utilizing the dynamic model of the robotic leg, this study proposes a new robust control algorithm, MBC (Model-Based with Continuous expression) algorithm, for lunar-based equipment’s robotic leg, and its effectiveness is established through experimentation and theoretical validation with the Lyapunov second method. In the design and verification, the contact force of lunar soil and the lunar environment with low gravity are considered, indicating the applicability of the proposed MBC method in special lunar scenarios. Compared to the conventional PD (Proportional-Differential) and MPD (Model-Based Proportional-Differential) techniques, MBC robust algorithm ultimately demonstrates superior stability and rapidity with a smaller steady state error.

Widely linear RLS equalizer with variable forgetting factor and UD factorization for mud pulse telemetry

To address the problems of time variations and high interferences in continuous-wave mud pulse telemetry channels, a widely linear recursive least squares decision feedback equalizer with a variable forgetting factor was proposed, a smaller steady-state error was achieved, and the tracking performance of the equalizer in time-varying mud channels was enhanced. Furthermore, employing UD factorization in the recursive algorithm improves the computational stability and ensures robust performance. The simulation results indicate that the proposed equalizer enhances the convergence speed by 46 %. The experimental findings demonstrate that within a 3000 m mud pulse telemetry channel, the proposed equalizer reduces the steady-state mean squared error by 3 to 4 dB and increases the error-free transmission rate from 3 bps to 6 bps. These advancements significantly enhance the performance of continuous wave mud pulse telemetry systems.

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Flexible Peripheral Nerve Interfacing Electrode for Joint Position Control in Closed-Loop Neuromuscular Stimulation.

Addressing peripheral nerve disorders with electronic medicine poses significant challenges, especially in replicating the dynamic mechanical properties of nerves and understanding their functionality. In the field of electronic medicine, it is crucial to design a system that thoroughly understands the functions of the nervous system and ensures a stable interface with nervous tissue, facilitating autonomous neural adaptation. Herein, we present a novel neural interface platform that modulates the peripheral nervous system using flexible nerve electrodes and advanced neuromodulation techniques. Specifically, we have developed a surface-based inverse recruitment model for effective joint position control via direct electrical nerve stimulation. Utilizing barycentric coordinates, this model constructs a three-dimensional framework that accurately interpolates inverse isometric recruitment values across various joint positions, thereby enhancing control stability during stimulation. Experimental results from rabbit ankle joint control trials demonstrate our model's effectiveness. In combination with a proportional-integral-derivative (PID) controller, it shows superior performance by achieving reduced settling time (less than 1.63 s), faster rising time (less than 0.39 s), and smaller steady-state error (less than 3 degrees) compared to the legacy model. Moreover, the model's compatibility with recent advances in flexible interfacing technologies and its integration into a closed-loop controlled functional neuromuscular stimulation (FNS) system highlight its potential for precise neuroprosthetic applications in joint position control. This approach marks a significant advancement in the management of neurological disorders with advanced neuroprosthetic solutions.

Research on a Sensorless ADRC Vector Control Method for a Permanent Magnet Synchronous Motor Based on the Luenberger Observer

A sensorless vector active disturbance rejection control (ADRC) method for permanent magnet synchronous motors (PMSM) utilizing a Luenberger observer is presented. This method aims to address the challenges associated with weak active disturbances, substantial steady-state speed amplitude fluctuations, and difficulty in achieving a balance between overshoot and speed control in the sensorless PMSM control system. Mathematical models of the Luenberger observer and the ADRC were analyzed, leading to the proposal of a second-order ADRC control method based on the Luenberger observer. A mathematical model of the permanent PMSM has been introduced. Additionally, the necessary conditions for the convergence of the Luenberger observer were derived and examined. The allowable range of error feedback gain values was determined, and rotor position data were acquired using a phase-locked loop. The principle of the ADRC was analyzed, and the ADRC simulation results, along with the PI simulation results, were detailed. When the target speed is 1000 r/min, the steady-state error and load disturbance resistance of the ADRC control method outperform those of the PI control method. Finally, the control method was experimentally tested on an STM32F4 chip, demonstrating the advantages of small steady-state error and strong active disturbance ability.

Improved Adaptive CCS-MPCC for Distorted Model Parameters Mitigation of IPMSM Drives

In this study, a robust adaptive model continuous-control-set model predictive current control (CCS-MPCC) method is proposed for interior permanent magnet synchronous motors (IPMSMs) considering the existing disturbances, unmodeled dynamics, and parameter variations. The novel aspect of this research is that it merges the improved dynamic, useful properties of model predictive control (MPC), which includes easy implementation and fast dynamic response, together with the most effective criterion of an adaptive controller (i.e., robustness to model uncertainties), that result in enhanced dynamic and steady-state control performance despite unknown and changing disturbances. Unlike the conventional CCS-MPCC approach that heavily depends on the accurate knowledge of the system model to attain improved control performance, the proposed method achieves satisfactory control performance (e.g., fast dynamic response, smaller steady-state error, and low stator current THD) by stabilizing the state errors to approach zero and compensating for variations in the model parameters using the designed feedback control terms and adaptive control terms, respectively. The stability of the proposed control method is ensured by the state errors asymptotically decaying to zero. The proposed approach was simulated and then implemented on a PSIM simulation tool and a prototype IPMSM test bench using TI TMS320F28335 DSP, respectively, to confirm its feasibility.

Predictive controllers for synchronous reluctance motor drive systems

This paper proposes the design and implementation of predictive controllers for synchronous reluctance motor drive systems to enhance their dynamic responses. The predictive speed and current controllers in this paper are designed in systematic procedures. The predictive speed controller is implemented by using Laguerre function procedure. The Laguerre function is used to simplify the algorithm and to minimize the execution time of the digital signal processor. For predictive current controller, a finite control set method improves the current tracking ability. The measured currents are used to predict the future phase-current based on the motor model. The optimal control inputs of both predictive controllers are determined by using a cost function minimization method. Experimental results show the proposed drive system provides a wide adjustable speed range, from 2 r/min to 1800 r/min. It has better performance than a proportional-integral (PI) controller including fast rise time, which is 0.9 second, small steady state error, which is 0.32 r/min, and small current ripples. A 32-bit floating-point digital signal processor, TMS-320-F-28335 DSP, is employed to implement the control algorithms.

Development of a Position Control System for Wheeled Humanoid Robot Movement Using the Swerve Drive Method Based on Fuzzy Logic Type-2

A humanoid robot is capable of mimicking human movements, which poses a challenge for researchers. This has led some to utilise wheels to facilitate its motion. However, achieving smooth and accurate movements at desired positions remains a challenge, necessitating the development of an optimal control system and movement method. In this study, solutions to address these challenges include the use of type-2 fuzzy logic controller (FLC) and the swerve drive method. During the steering rotation movement testing, type-1 FLC exhibits the fastest response time of 0.8 seconds, but oscillations occur, reaching up to 117 degrees to achieve the set point of 90 degrees. Additionally, type-1 FLC cannot reach the set point of -90 degrees. On the contrary, type-2 FLC aligns successfully with both set points of 90 and -90 degrees. In coordinate movement testing, type-1 FLC still shows an error between 1 cm and 2 cm compared to type-2 FLC, particularly with 3 and 5 members, which are equal to the given set point. The results of the tests indicate that type-2 FLC is reliable, showing a small steady-state error, stability, and no overshoot, despite its longer response time and processing duration compared to type-1 FLC.

Zinc roasting temperature field control with CFD model and reinforcement learning

As the most critical variable in the zinc roasting process, the roasting temperature has been heavily researched for stable control through its average value. However, relying solely on the average temperature cannot convey the entire temperature field information necessary to achieve the optimal production state. To address this, This paper initially proposes a control scheme for the zinc roasting temperature field. First, a computational fluid dynamics (CFD) temperature field model was established through the mechanism of the roasting process. The influence of the feeding position on the temperature field was incorporated into the mechanism model, which provided the basis for the subsequent real-time control. Second, a convolutional Q-learning network (CQLN) is proposed to learn the mapping from state and action to Q value. CQLN can fully mine the spatial information of the temperature field. Then, the feed rate and feed location are adjusted in real-time to obtain the optimal roasting temperature field. Finally, extensive comparative experiments were conducted. Experimental results show that control performance of the proposed method is better than that of the comparison methods, with more uniform temperature distribution and smaller steady-state error.

Multilevel variable control for impulse-discharge thermochemical truing of diamond cutting edges in precision grinding

Precision grinding depends on the diamond cutting edges with good protrusion uniformity that are acquired by impulse-discharge thermochemical truing. However, the impulse-discharge thermal is unstable due to the uncertain discharge gap derived from irregularly changed grain top heights, leading to random diamond thermochemical removal. Under the uncertainty, a multilevel fuzzy-based control of kinematic and electrical variables is proposed to stabilize the impulse-discharge power during truing. The objective is to realize the cooperation of impulse-discharge, thermochemical and mechanical processes for diamond truing effect trace. First, the impulse-discharge signal was collected to acquire the transfer functions of impulse-discharge power. Then, an on-site experiment with small sample was processed to determine the discharge voltage sensitivities of kinematic and electrical variations. In order to improve the control performance, the control process with fuzzy logic was modeled to tune the output ratios of kinematic and electrical variations by particle swarm algorithm. Finally, the designed controller was experimentally applied to the impulse-discharge thermochemical truing, followed with dry plunge grinding of hardened steel. It is shown that the kinematic and electrical variables for the uncertainty compensation are linearly related to the spark-discharge voltage. Accordingly, the fuzzy-based control may be applied to the multilevel variable adjustment. The controller with tuned output ratios reaches small steady-state error, few trigger times and no overshoot to regulate the impulse-discharge power within 5 % error in actual truing. By integrating physical behavior and experiential knowledge rather than big data driving, the controllable impulse-discharge power for required diamond cutting edges advances the stable smooth surface grinding.

Ice Identification with Error-Accumulation Enhanced Neural Dynamics in Optical Remote Sensing Images

Arctic sea ice plays an important role in Arctic-related research. Therefore, how to identify Arctic sea ice from remote sensing images with high quality in an unavoidable noise environment is an urgent challenge to be solved. In this paper, a constrained energy minimization (CEM) method is applied for Arctic sea ice identification, which only requires the target spectrum. Moreover, an error-accumulation enhanced neural dynamics (EAEND) model with strong noise immunity and high computing accuracy is proposed to aid with the CEM method for Arctic sea ice identification. With the theoretical analysis, the proposed EAEND model possesses a small steady-state error in noisy environments. Finally, compared with other existing models, the proposed EAEND model can not only complete sea ice identification in excellent fashion, but also has the advantages of high efficiency and noise immunity.

A Passivity-based Control Combined with Sliding Mode Control for a DC-DC Boost Power Converter

In this paper, a passivity-based control combined with sliding mode control for a DC-DC boost power converter is proposed. Moreover, a passivity-based control for a DC-DC boost power converter is also proposed. Using a co-ordinate transformation of state variables and control input, a DC-DC boost power converter is passive. A new plant is zero-state observable and the equilibrium point at origin of this plant is asymptotically stable. Then, a passivity-based control is applied to this plant such that the capacitor voltage is equal to the desired voltage. Additionally, the sliding mode control law is chosen such that the derivative of Lyapunov function is negative semidefinite. Finally, a passivity-based control combined with sliding mode control law is applied to this plant such that the capacitor voltage is equal to the desired voltage. The simulation results of the passivity-based control, the sliding mode control and the passivity-based control combined with sliding mode control demonstrate the effectiveness and show that the capacitor voltage is kept at the desired voltage when the desired voltage, the input voltage E and the load resistor R are changed. The results show that compared with the passivity-based control, the passivity-based control combined with sliding mode control has better performance such as shorter settling time, 8.5 ms when R changes and it has smaller steady-state error, which is indicated by the value of integral absolute error (IAE), 0.0679 when the desired voltage changes. The paper has limitations such as the assumed circuit parameters.

MTPA Control for PMSM Velocity-specified Position Tracking Based on the Principle of Backstepping Control

In this paper, a control method based on the backstepping control is proposed to address the issues of uncontrollable speed and complex PI parameter tuning in the traditional PID position control of PMSM. By comparing with id=0 control, MTPA control enables the IPMSM to maximize the utilization of reluctance torque. The dq current relationship under MTPA control is established using a formulaic method. Based on this, a position, speed, and current backstepping controller is designed, which adds a degree of freedom to the speed control during the position control process while ensuring the stability of the control system. Simulation results demonstrate fast dynamic response, small steady-state error, and minimal parameter adjustments, thus verifying the correctness and effectiveness of this method.

Adaptive Satellite Navigation Anti-Interference Algorithm Based on Inverse Cosine Function

Contrasting the dilemma that the traditional time-domain least mean square (LMS) algorithm in the existing satellite navigation receiver anti-interference system cannot satisfy the convergence time is short and maintain a low level of steady-state error at the same time, an inverse cosine variable step size LMS algorithm (ICVS-LMS) is proposed. To begin with, the LMS algorithm, with a fixed step size focuses on its effectiveness in attenuating and suppressing interference signals, is analyzed, and then the proposed ICVS-LMS algorithm is analyzed. In conclusion, both the ICVS-LMS algorithm and the traditional algorithm are simulated and compared in terms of their effectiveness in suppressing interference in satellite navigation signals. The experimental results demonstrate that the improved algorithm significantly reduces convergence time while maintaining a small steady-state error. The improved algorithm demonstrates high robustness and an obvious suppression effect on interference signals. The anti-interference performance is 8.41–12.22% higher than that of the proposed algorithm.

An active noise control algorithm based on an improved convex combination loop

The conflict between a faster convergence using a Filtered-X Least Mean Square (FXLMS) algorithm and the requirement of a small steady-state error of an adaptive active control (ANC) system which limits its performance in terms of computation efficiency and the control error. The problem can be solved by using two paralleling adaptive filters to form a convex combination loop in the control system. However, the exponential weight function used in the original convex combination loop design can consume a large computer power which has limited its application. As a result, a new weight function is proposed to replace the original exponential function in the original convex combination loop to enhance the convergence rate of the adaptive ANC system. Furthermore, a MRFXLMS algorithm is used to replace the FXLMS algorithm in the ANC system so that it can be used for impulse noise control. The result shows that the proposed algorithm performs well in the control of input impulse noise with different intensity.

Speed Control of PMSM Based on Fuzzy Active Disturbance Rejection Control under Small Disturbances

Permanent Magnet Synchronous Motors (PMSMs), with their simple design, small size, and high-power factor, are ideally suited for realizing high-power AC drives and are widely used in various industries. In this study, Fuzzy Active Disturbance Rejection Control (Fuzzy-ADRC) is used to control the speed of the PMSM. When a slight external disturbance occurs, this control strategy maintains the suppression characteristics of the self-excited control for the disturbance and enhances its ability to compensate for the disturbance. First, a mathematical model was developed to study the surface mount PMSM. Then, a motor control simulation model was created using PI control, vector control, and other control methods. The verification results indicate that the improved Fuzzy-ADRC system performs well under both internal and external minor disturbances. It exhibits a faster dynamic response and reduced regulation time (0.026 s to 0.017 s) compared to the traditional ADRC system. Furthermore, it shows less overshoot (reduced from 70% to 2.9%) compared to the Sliding Mode Observer (SMO). Taken together, the improved Fuzzy-ADRC system is characterized by small steady-state error, high load capacity, and control accuracy. With the assistance of this control strategy, the system can track speed with high accuracy and possesses stronger anti-interference capability to mitigate load disturbances.

Performance comparison between LQR and PID controllers for two-wheeled self-balancing vehicle

The two-wheeled balancing vehicle is an energy-efficient and eco-friendly transportation tool used for short distances. Both motors of the vehicle are required both motors to be regulated to maintain stability, and modern control methods have been applied to the system. In this paper, the effectiveness of traditional PID control and modern LQR control in the context of a two-wheeled balancing vehicle is evaluated. The motion principle of the vehicle is analyzed, and a corresponding dynamic model is established. PID and LQR control schemes are designed according to the model and the performance of the angle and speed of the vehicle is compared in MATLAB simulation. The two kinds of control performance are evaluated, offering theoretical feasibility verification for the application of two-wheeled balancing vehicles. It has been found that the LQR controller has better performance than the PID controller. LQR has a shorter response time and smaller steady-state error in controlling speed, and there is still room for improvement for both controllers.

Non-fragile guaranteed cost control of microbial fuel cells

A microbial fuel cell (MFC), which is a new type of energy source, utilises electrogenic bacteria in sewage or soil to convert chemical energy into electrical energy. MFCs typically require an external controller to provide a stable output voltage to the external load. This study develops a non-fragile guaranteed cost (NFGC) controller to suppress the interference of the controller of an MFC and ensure that the quadratic cost function of the system satisfies certain performance indexes. First, for the convenience of controller design, a Takagi–Sugeno fuzzy model is established to approximate a single-chamber single-population MFC model. Subsequently, the linear matrix inequality method is used to design the NFGC controller. This control scheme can reduce the influence of controller disturbances on the system and ensure asymptotic stability of the closed-loop system under the specified upper bound of the provided cost function. The simulation results demonstrate that the developed control method has a shorter adjustment time and smaller steady-state error than traditional control methods such as sliding mode control (SMC), backstepping control, and fuzzy SMC

Analysis and Control of PWM Buck-Boost AC Chopper Fed Single-Phase Capacitor Run Induction Motor

Single phase capacitor-run induction motors (IMs) are used in various applications such as home appliances and machine tools; they are affected by the sags or swells and any fault that can lead to disturb the supply and make it produce rms voltage below or above the rated motor voltage, which is 220V. A control system is designed to regulate the output voltage of the converter irrespective to the variation of the load and within a specific range of supply voltage variation. The steady-state equivalent circuit of the Buck-Boost chopper type AC voltage regulator, as well as the analysis of this circuit are presented in this paper. Switching device for the regulator is an IGBT Module. The proposed chopper uses pulse width modulation (PWM) control technique to chop the input voltage into segments in order to guarantee rated rms voltage supplied to the load, which is capacitor-run induction motor. Proportional integral (PI) controller is used to obtain very small steady state error, stable and fast dynamic response, and robustness against variations in the line voltage. The complete system is simulated using software package, and the results are obtained to verify the proposed control method.

Dynamic Response Analysis of the Bi-Tandem Axial Piston Pump with Dual-Loop Positive Flow Control under Pressure Disturbance

The bi-tandem axial piston pump is an indispensable powerhouse in high-pressure and high-power engineering hydraulic systems, with its output flow response characteristics under pressure disturbance exerting a significant influence on the working process of double pumps. Unfortunately, the stability of the original single-loop mechanical–hydraulic servo control system is sensitive to unpredictable interference. To alleviate this quandary, this paper proposes a dual-loop positive flow control method for the flow control of the bi-tandem axial piston pump, establishes a mathematical model of the bi-tandem axial piston pump with dual-loop positive flow control, and establishes a simulation model based on Simulink. The validity of the model is verified by experiments. The performance advantages of the dual-loop positive flow control method relative to the single-loop positive flow control method are analyzed. The results show a faster response speed and smaller steady-state error with the dual-loop method, which performs better than the original single-loop positive flow control. Furthermore, the study examines the influence of different forms, degrees, and directions of pressure disturbance on the dynamic response characteristics of the bi-tandem axial piston pump. Symmetric pressure disturbance results in an increase in the maximum relative error of the output flow proportional to its degree. Notably, the influence of asymmetric pressure disturbance on the output flow of the double pumps possesses characteristics of a superimposable nature, and the steady-state value of the output flow is highly dependent on superimposed pressure disturbance and less affected by the action time point of asymmetric pressure disturbance. Further, the unloading pressure disturbance exerts less influence on the system compared to the loading pressure disturbance. This paper provides valuable insights into improving the response speed and control accuracy of bi-tandem axial piston pumps equipped with positive flow control.