Control Loop Research Articles

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively.

Performance Comparison of a DC–DC Boost Converter With Conventional and Non-linear SMC Controller in Dual Loop Structure

Generally, the control system confronts various control techniques in the dual-loop control concept. Most of the studies deal with two different controllers in their loops, which increases the problem of designing two distinct procedures with lengthened calculations. In this article, different control techniques like conventional proportional integral (PI), proportional integral derivative (PID), and nonlinear sliding mode control (SMC) have been discussed, and the implementation of SMC in the Dual loop control of a DC–DC Boost converter is presented. The converter’s dynamic behavior is observed by driving their analytical equations. The effective controller for the proposed converter is ascertained by their comparative analysis. The proposed concept is validated with MATLAB/Simulink and a hardware prototype. The controller’s response is compared for various disturbances. Here, dual-loop SMC is put forward in both loops and its competence is confirmed with a minimal settling period of 0.005 sec and a percentage overshoot of 0.2% during the startup conditions, whereas during the line disturbances a minimum peak time of 0.0001 sec is achieved with a settling time of 0.025 sec. Additionally, a nil percentage overshoot and a settling time of 0.02 sec with a 0.001 sec peak time are attained during the load variations. Furthermore, a settling time of 0.01 sec and a percentage overshoot of 0.8% is reached during the reference voltage variations. From the numerical analysis, it is contemplated that an appropriate selection of SMC controllers in both the outer and inner loop results in optimal performance of the converter during the closed loop operation. Finally, the SMC + SMC controller exhibits superior controlling action compared to PI + PI and PID + PID controllers.

Investigation of Legendre polynomials expanded adaptive controller with inverse compensation for high frequency tracking of electro-hydraulic force systems

Accurate force tracking of an electro-hydraulic force system (EHFS) is of great significance in various industrial applications. As the EHFS is inevitably confronted with hydraulic nonlinearities, dynamic variations and uncertain disturbances, the real-time force tracking performance is generally unsatisfactory, especially for high frequency command force signals. For reducing the force tracking error with relatively high frequency inputs, a Legendre polynomials expanded adaptive controller with inverse compensation is proposed in this paper. The proposed controller is constructed by introducing an offline designed inverse compensation (IC) controller and an online adaptive controller to the EHFS governed by traditional Proportional Integral (PI) controller, in which the former IC controller is acting as the inner loop controller and the latter adaptive controller is regarded as an outer controller. The inner loop IC controller with fixed control parameters is offline designed on the basis of traditional PI controller by means of the generalized projection identification algorithm and the zero magnitude error tracking technology, focusing on extending the real-time force tracking bandwidth of the EHFS. The outer loop online adaptive controller is developed by Legendre polynomials expanded adaptive filters with nonlinear mapping ability, whose weights are online updated by an adaptive tuning algorithm with the aim of handling system’s dynamic variations, nonlinearities and uncertain disturbances. The proposed controller is implemented on a real EHFS test rig by the xPC/Target rapid prototyping technique, and comparative experimental results demonstrate that the proposed controller can achieve much higher high frequency tracking performance than the traditional PI and IC controller commonly used in industry.

On the issue of ensuring the safety of objects with “smart habitat”

The problems of ensuring the security of the smart ecosystem are considered, an analysis of modern Internet of things (IoT devices) used in the control loop of the smart habitat is carried out from the point of view of the possibility of protecting and depersonalizing the information circulating in it. The article pays special attention to the issue of integrating a specialized software platform into the smart environment infrastructure, which allows for a high level of not only IT security, but also the overall security of the facility. It made an analysis of economic development and subsequent application of the integrated STB platform. Practical recommendations of the organization to improve the security of using the Internet of things in ODR are considered.

FAK-LINC01089 negative regulatory loop controls chemoresistance and progression of small cell lung cancer.

The focal adhesion kinase (FAK) tyrosine kinase is activated and upregulated in multiple cancer types including small cell lung cancer (SCLC). However, FAK inhibitors have shown limited efficacy in clinical trials for cancer treatment. With the aim of identifying potential therapeutic strategies to inhibit FAK for cancer treatment, we investigated long non-coding RNAs (lncRNAs) that potentially regulate FAK in SCLC. In this study, we identified a long non-coding RNA LINC01089 that binds and inhibits FAK phosphorylation (activation). Expression analysis revealed that LINC01089 was downregulated in SCLC tissues and negatively correlated with chemoresistance and survival in SCLC patients. Functionally, LINC01089 inhibited chemoresistance and progression of SCLC in vitro and in vivo. Mechanistically, LINC01089 inhibits FAK activation by blocking binding with Src and talin kinases, while FAK negatively regulates LINC01089 transcription by activating the ERK signaling pathway to recruit the REST transcription factor. Furthermore, LINC01089-FAK axis mediates the expression of drug resist-related genes by modulating YBX1 phosphorylation, leading to drug resistance in SCLC. Intriguingly, the FAK-LINC01089 interaction depends on the co-occurrence of the novel FAK variant and the non-conserved region of LINC01089 in primates. In Conclusion, our results indicated that LINC01089 may serve as a novel high-efficiency FAK inhibitor and the FAK-LINC01089 axis represents a valuable prognostic biomarker and potential therapeutic target in SCLC.

Design of two-degree-of-freedom position tracking controller for hypergravity unidirectional airborne shaking table

Hypergravity centrifugal shaking table is widely used in the field of civil engineering and is considered to be the most effective means to study geotechnical earthquake disaster effect in the world today. Here, the structure of hypergravity centrifugal shaking table system is briefly introduced, and then the necessity and difficulty of the precise control of the position ring of the airborne shaking table in the hypergravity centrifugal shaking table are explained. On this premise, the system modeling and analysis of the unidirectional airborne shaking table with hypergravity are carried out in this paper. A new two-degree-of-freedom controller of the unidirectional airborne shaking table with hypergravity is designed by combining the [Formula: see text] control and an improved internal model control (IIMC) strategy, and the designed two-degree-of-freedom controller is experimentally verified. The experimental results show that compared with the traditional PID control strategy and the single [Formula: see text] control strategy, the two-degree-of-freedom controller designed in this paper can make the position loop control of the unidirectional airborne shaking table with hypergravity have higher control accuracy and anti-interference ability.

Seam tracking and gap bridging during robotic laser beam welding via grayscale imaging and wobbling

The use of laser beam welding with robotic manipulators is expanding towards wider industrial applications as the system availability increases with reduced capital costs. Conventionally, laser welding requires high positioning and coupling accuracy. Due to the variability in the part geometry and positioning, as well as the thermal deformation that may occur during the process, joint position and fit-up are not always acceptable nor predictable a-priori if simple fixtures are used. This makes the passage from virtual CAD/CAM environment to real production site not trivial, limiting applications where short part preparations are a need like small-batch productions. Solutions that render the laser welding operations feasible for production series with non-stringent tolerances are required to serve a wider range of industrial applications. Such solutions should be able to track the seam as well as tolerating variable gaps formed between the parts to be joined. In this work, an online correction for robot trajectory based on a greyscale coaxial vision system with external illumination and an adaptive wobbling strategy are proposed as means to increase the overall flexibility of a manufacturing plant. The underlying vision algorithm and control architectures are presented; the robustness of the system to poor illumination conditions and variable reflection conditions is also discussed. The developed solution employed two control loops: the first is able to change the robot pose to follow varying trajectories; the second, able to vary the amplitude of circular wobbling as a function of the gap formed in butt-joint welds. Demonstrator cases on butt-joint welds with AISI 301 stainless steel with increased complexity were used to test the efficacy of the solution. The system was successfully tested on 2 mm thick, planar stainless-steel sheets at a maximum welding speed of 25 mm/s and yielded a maximum positioning and yaw-orientation errors of respectively 0.325 mm and 4.5°. Continuous welds could be achieved with up to 1 mm gaps and variable seam position with the developed control method. The acceptable weld quality could be maintained up to 0.6 mm gap in the employed autogenous welding configuration.

Modified Universal Integral Regulator for Quadrotor UAV in the Presence of External Disturbances

Universal Integral Regulator (UIR) is a widely used control law for control problems, it is based on sliding mode control with the inclusion of a Conditional Integrator (CI). In the original approach of this control law, the main gain is considered a constant. The purpose of this paper is to solve a quadrotor tracking problem using a new nonlinear control law based on the quadrotor tracking problem using a new nonlinear control law based on the UIR, considering the main gain dependent on the tracking error (variable gain), the control law is called in the present work as Modified Universal Integral Regulator (MUIR). It is expected that the MUIR can improve the transient response and reduce the control demand when compared to previous approaches of similar controllers. The adopted Newton-Euler quadrotor model and the controller design are treated separately in two subsystems, attitude and position control loops. The stability of MUIR is demonstrated by the use of a candidate Lyapunov function. Finally, in order to validate the robustness and choice of the proposed controllers, several numerical simulations were developed in the presence of external disturbances. Less error and control activity during transient response were observed when compared to the original Universal Integral Regulator controller.

Deep reinforcement learning for PMSG wind turbine control via twin delayed deep deterministic policy gradient (TD3)

AbstractThis article proposes the use of a deep reinforcement learning method—and precisely a variant of the deep deterministic policy gradient (DDPG) method known as twin delayed DDPG, or TD3—for maximum power point tracking in wind energy conversion systems that use permanent magnet synchronous generators (PMSGs). An overview of the TD3 algorithm is provided, together with a detailed description of its implementation and training for the considered application. Simulation results are provided, also including a comparison with a model‐based control method based on feedback linearization and linear‐quadratic regulation. The proposed TD3‐based controller achieves a satisfactory control performance and is more robust to PMSG parameter variations as compared to the presented model‐based method. To the best of the authors' knowledge, this article presents for the first time an approach for generating both speed and current control loops using DRL for wind energy conversion systems.

Adaptive hybrid-mode assist-as-needed control of upper limb exoskeleton for rehabilitation training

In this paper, a novel adaptive hybrid-mode assist-as-needed (AHMAAN) control algorithm is designed for the exoskeleton-assisted upper limb rehabilitation training. The overall control framework includes an outer control loop to calculate the required interaction force, and an inner control loop to drive the exoskeleton track subject’s motion, and provide specific target interaction force obtained from the outer control loop. In the outer control loop, a hybrid control mode is proposed, which consists of resistive mode and assistive mode. In this regard, a virtual tunnel is firstly established around the defined training task path, and the training mode is switched according to the deviation between the subject’s position and the boundary of the virtual tunnel. Furthermore, for tuning the strength of the resistance or assistance to the subjects with different motor capabilities, two adjustable gain factors are designed, whose values are adaptively adjusted according to the subject’s training performance by using a fuzzy logic. Then, for the inner control loop, a barrier Lyapunov function-based controller is designed to constrain exoskeleton tracking errors within the defined boundary. Meanwhile, time delay estimation (TDE) technology is used to estimate the uncertain terms of the system, and a robust adaption law is developed to compensate TDE error. Experimental tests have been performed on an upper limb exoskeleton with three healthy subjects to evaluate the effectiveness of the developed method. The results show that the proposed control scheme can be effectively applied in a variety of rehabilitation requirements and achieves better training performance than classical hybrid-mode assist-as-needed control method.

Modified fractional order PID structure for non‐integer model bioreactor control

AbstractTemperature control for fermentation is crucial as it directly affects microorganism growth, productivity, and metabolic activity. This work proposes a modified fractional order proportional‐integral‐derivative (FOPID‐DF) controller that incorporates the system model in the control loop configuration utilizing a differential filter for precise temperature control within a narrow operating range. PID, fractional order PID (FOPID), modified fractional order PID (MFOPID), and fractional order internal model control (IMC) controllers were also designed for comparative analysis. The simulation results demonstrated that the proposed FOPID‐DF controller outperforms the other designed controllers, shown by a 22.33% reduction in integral absolute error (IAE) and a 29.06% decrease in integral square error (ISE) compared to the FOPID controller and various other improved performance indicators. Parameter variation and noise analysis highlighted the ability of the controller to maintain stability and performance under changing conditions. The simulation outcomes suggested that the FOPID‐DF controller excels in temperature control, ensuring optimal microorganism growth and metabolic activity.

Surrogate Optimal Fractional Control for Constrained Operational Service of UAV Systems

In the expeditiously evolving discipline of autonomous aerial robotics, the efficiency and precision of drone control deliveries have become predominant. Different control strategies for UAV systems have been thoroughly investigated, yet PID controllers still receive significant consideration at various levels in the control loop. Although fractional-order PID controllers (FOPID) have greater flexibility than integer-order PID (IOPID) controllers, they are approached with caution and hesitance. This is due to the fact that FOPID controllers are more computationally intensive to tune, as well as being more challenging to implement accurately in real time. In this paper, we address this problem by developing and implementing a surrogate-based analysis and optimization (SBAO) of a relatively high-order approximation of FOPID controllers. The proposed approach was verified through two case studies; a simulation quadrotor benchmark model for waypoint navigation, and a real-time twin-rotor copter system. The obtained results validated and favored the SBAO approach over other classical heuristic methods for IOPID and FOPID.

Optimizing the series cascade control structure for nonminimum phase system regulation

AbstractThis work elucidates the control of integrating a nonminimum phase system via a series cascade scheme with fractional‐order P.I. (Proportional–Integral) plus D (Derivative) controller. The traditional Internal Model Control (IMC) is adopted for inner loop controller design. The feedback D controller is synthesized with the outer loop process model, showing the proposed work's universality. The outer loop controller is suggested in the IMC framework after the accountability of fractional‐filter and inverse response compensator. This combination is revealed to enhance performance without compromising robustness. The Riemann sheet principle is explored to compute the stability of the suggested controller. The sensitivity analysis has asserted the robustness. More importantly, the optimal value of controller settings is achieved via the Teaching Learning Based Optimization (TLBO) algorithm. This TLBO algorithm uses an objective function that minimizes Integral Square Error. Two illustrative problems are utilized to examine the recommended control structure's virtue.

PI Current Controller Design for DC Drive System using 4- Quadrant DC-DC Converter and 3- Phase Rectifier

DC Motors is considered one of the important machines in control systems such as industrial robots, vehicles, and process control. Current control of a DC drive is very desirable because by controlling the current, the torque is controlled. Moreover, current control can be used to prevent large damaging armature currents during start-up. A good current loop is very important when setting up a DC drive. When the control of the current loop is good, the steady state motor current should respond exactly with the reference current, and the transient response to the step change in the reference current should be fast and well-damped. This paper presents two different types of current controller converters that are commonly used in DC motor drives. The first converter is a 4-quadratic switch-mode DC-DC converter, while the second controller is a 3-phase controller rectifier. Both current converters were simulated using Matlab Simulink, where a PI current controller for the inner loop current control DC motor controls these converters. The PI controller was designed based on the Bode plot frequency response method. In this research, the design procedure was based on the small signal model and then, verified using a large signal model. The simulation result showed that the performance using a 4-quadrant DC-DC converter gave better performance than a 3-phase rectifier.

Hassle - Free Load Carrying Using Automated Guided Vehicle

In today's rapidly evolving world, where efficiency and sustainability are paramount, businesses are seeking technologies that not only save time and drive profits but also align with their sustainable goals. In response to this demand, a prototype of an Automated Guided Vehicle (AGV) is developed.AGVs are versatile robotic platforms designed to navigate predefined paths while handling heavy loads of materials. The AGV system addresses these needs by utilizing cost-effective RFID tags and an RFID reader for efficient load tracking and navigation. Additionally, it incorporates two infrared (IR) sensors for path detection, an L298N motor driver for precise motor control, an Arduino microcontroller as the central processing unit, and an HC-05 Bluetooth module for wireless communication.By integrating these components, along with feedback control loops and path planning algorithms, The AGV is observed to be an efficient load carrier and can navigate reliably in dynamic environments. Above all, this solution not only saves time and enhances profitability but also promotes sustainability by optimizing resource utilization and minimizing manual intervention. Key Words: AGV(Automated Guided Vehicle),RFID(Radio Frequency Identification) tags, RFID (Radio Frequency Identification)reader, IR sensors, L298N Motor driver, Microcontroller.

Adaptive Online Neural Predictive Control of Hydraulic Actuator Using FPGA Acceleration

Hydraulic actuators have recently been widely used in control systems as efficient output devices. However, high-precision control of hydraulic actuators in industrial applications remains challenging due to the conflict between controllers' complexity, especially in theoretical modeling, and their generalization abilities, including adaptability to parametric uncertainties and versatility for various working conditions. To address this issue, we propose an adaptive online neural predictive control (AONPC) scheme, which is capable of handling unmodeled nonlinearities and uncertainties with a simple structure and low demand for system modeling. Specifically, an artificial neural network (ANN) is responsible for data-driven system identification and predictive control, while upper confidence bound (UCB) algorithm is introduced for hyperparameter self-adaptation. The ANN structure and the iteration number for solving the optimization problem are determined through a simulation study. Then, to complete the whole control loop within a short and fixed tracking interval, the proposed controller is implemented on a field programmable gate array (FPGA), which can effectively improve the tracking precision. Finally, extensive experimental results on a realistic hydraulic actuator device demonstrate the superiority of the proposed controller.

Passive Model-Predictive Impedance Control for Safe Physical Human–Robot Interaction

Various cognitive systems have been designed to model the position and stiffness profiles of human behavior and then to drive robots by mimicking the human’s behavior to accomplish physical human-robot interaction tasks through a properly designed impedance controller. However, some studies have shown that variable stiffness parameters of the impedance controller can cause the violation of the passivity constraint of the robot states, and make the robot’s stored energy exceed the external energy injected from the human user, thus leading to the unsafe human-robot interaction. To solve this problem, this paper proposes a novel passive model predictive impedance control method including two control loops. In the bottom-loop of the proposed controller, the robot is driven by a variable impedance controller to achieve the desired compliant interaction behavior. In the top-loop of the proposed controller, the model predictive control (MPC) is used to ensure that the robot states satisfy the passivity constraint by calculating a complementary torque to limit the stored energy of the robot. The passivity of the closed-loop robot system and the feasibility of MPC are guaranteed by theoretical analysis, ensuring the safety of the robotic movement in the human-robot interaction. The effectiveness of the proposed method is demonstrated by the simulation and experiment on the Franka Emika Panda robot.

Automatic Re-tuning of Poor-Performing PI-based Control Systems

The goal of the work is to create a new method for automatic re-tuning of PI controllers. It is achieved by solving the following problems. The first problem is to develop a method to identify a FOPDT estimation model by analyzing the dynamics of a control loop with a PI controller. The second problem is to demonstrate that the use of the estimated model allows to obtain better processes in control systems with PI controllers by comparing the resulting FOPDT model with a number of reduced models. The third problem is to develop a five-stage algorithm for automatic re-tuning of PI controllers. The fourth problem is to verify the software implementation of the developed algorithm. The most significant result was that the basis for estimating the FOPDT model in a loop with a Ziegler-Nichols tuned PI controller was the estimation the ratio of the delay to the time constant of the FOPDT model based on an assessment of the process overshoot. When the real control plant dynamics did not match with the dynamics of the FOPDT model this way was found effective for finding the optimal tuning of the PI controller. The significance of the results obtained was a structural robustness in case of estimated FOPDT model usage. The effectiveness of the procedure has been demonstrated on both linear and nonlinear models. The developed procedure is proposed to be used to automate the procedure of re-tuning SISO control systems when the control loop performance degradation has been detected.

Intelligent robotic arm for human pose recognition based on teleoperation system

With the rapid development of skeleton recognition, machine learning and other technologies, we will find that there are great drawbacks in the control of manipulators. Robot teleoperation refers to the inclusion of human operation in the control loop of robot control. When robots deal with complex perception and a large number of tasks, teleoperation is far superior to intelligent programming when making decisions quickly and dealing with extreme situations. The goal of this paper is to build a robotic arm teleoperation system for human motion capture, so as to solve the problems that the control accuracy of the end of the robotic arm is not high and the motion of the robotic arm is greatly affected by the difference between the human arm in the current related research, the master-slave human motion mapping algorithm is designed and extended with machine learning algorithms. We use inertial motion capture to realize teleoperation, so as to avoid the use of the terminal position and orientation control method of the hand controller to form the control command of the remote robot after tedious calculation, and it is convenient for the operator to complete the attitude tracking task in real time. The obtained attitude information has a larger range, higher sensitivity and better dynamic performance.

Implementation of an adaptive control approach in a single‐phase grid‐tied solar photovoltaic system for power quality improvement

SummaryThis work focuses on a novel single‐phase complex band‐pass filter‐based frequency‐locked loop (1ϕ‐CBF‐FLL) control for a grid‐tied solar photovoltaic array (SPVA)‐based energy generating system. Among the novel characteristics of this 1ϕ‐CBF‐FLL control algorithm are as follows: (1) It offers a quick convergence rate; (2) it has the ability to mitigate harmonics and DC offset; and (3) it has simplicity in execution. A voltage source converter (VSC), boost converter, 1‐ϕ grid, ripple filter, and nonlinear loads are all part of the system. Feeding 1‐ϕ nonlinear loads and the grid with active power from the solar array is managed by the VSC. Furthermore, grid current quality is enhanced by the use of VSC. Between the solar array and DC‐link capacitor, there is a boost converter that uses the perturb and observe (P&O) technique to track the maximum power from the SPVA. The primary aim of the proposed system is to remove power quality (PQ) issues brought on by nonlinear and imbalanced loads and to supply sinusoidal grid currents during solar irradiance fluctuations. Additionally, a comparison between 1ϕ‐CBF‐FLL control and other control algorithms is presented. The effectiveness of this 1ϕ‐CBF‐FLL control is examined under varying nonlinear loads and in various environmental settings. Test results are used to confirm the simulated results of the proposed system. According to the IEEE‐519 standard, the recommended control may achieve a source current of total harmonic distortion (THD) of 4.02%, which is lower than 5%, a source voltage of THD of 3.21%, and a load current of THD of 24.54%.