PID Feedback Control Research Articles

Smith GA-PID feedback control of hot strip final rolling temperature

Abstract At present, the hot strip finishing temperature control system generally adopts a computer model or mechanism model to predict the strip’s final rolling temperature, which is open-loop control. To realize the precise control of the final rolling temperature of high-performance steel, this paper introduces the PID feedback control strategy at the end of the finishing zone, to realize the high-precision temperature control of the strip steel. The Smith compensator is used to predict the compensation for the large hysteresis problem of the final rolling temperature, and various intelligent algorithms are used to optimize the three parameters of the PID controller, eliminate the nonlinear problem, and obtain better dynamic performance. After the experiment, it is concluded that the Smith GA-PID optimization control strategy system has a small overshoot, the peak time and regulation time are the shortest, and the dynamic response is the fastest. Compared with the traditional PID control method, the overshoot is reduced by 97.6%, and the required regulation time is reduced by 7.85 s.

Adaptive fuzzy controller design for an air handling unit

An adaptive control method based on a fuzzy state observer is designed for air handling unit (AHU) systems, where the intensity of humidity source and heat load are considered to be uncertain, and approximated by fuzzy logics. Besides, the supply air temperature is unmeasurable, which is observed by a state observer. To avoid the system equilibrium point offset, the prediction errors generated by the system and serial-parallel estimation models are introduced into the controller design based on backstepping. Finally, the superiority of proposed adaptive fuzzy controller is analyzed by simulation comparison with feedback linearization and PID controllers in two cases.

Using a Flywheel to Stabilize a Self-Balancing Bicycle

The designs of two linear control systems approach to stabilize the balance of an unmanned bicycle system are presented. Both approaches are based on the use of a reaction wheel or flywheel to balance the bicycle. The two linear control approaches, based on the linearization of a nonlinear model obtained using Lagrange formalism, are the classic linear controllers, PID and State Feedback control. The performance of both controllers is verified by digital simulation and real-time experimental results.

Precision positioning based on temperature dependence self-sensing magnetostrictive actuation mechanism

The self-sensing actuator that integrates both actuation and sensing functions is an effective way to simplify the structure of electromechanical systems. This paper proposes a temperature-dependent self-sensing actuation strategy based on a self-sensing giant magnetostrictive actuator (SSGMA) by sensing online stiffness. Utilizing the developed model considering temperature-dependent hysteresis nonlinearity, the self-sensing output characteristics of SSGMA are first evaluated. The self-sensing based control system is then designed in detail. The relationship between the self-sensing signal and the output displacement of SSGMA at different temperatures is constructed by General Regression Neural Network (GRNN) model for fast recognition by the controller. On this basis, a composite control scheme that takes into account rate-dependent asymmetric hysteresis nonlinearities and combines an adaptive feedforward controller with PID feedback controller is proposed for precision actuation of SSGMA. Finally, a temperature-controlled experimental platform is assembled for verification. Experimental results demonstrate that, based on the developed model, the SSGMA is capable of accurate self-sensing output at temperatures of 22 °C, 40 °C, and 70 °C, respectively. The SSGMA with the proposed control method enables the synchronous and precise self-sensing positioning of step and multiple-frequency sinusoidal expectations at different temperatures.

On designing a configurable UAV autopilot for unmanned quadrotors.

Unmanned Aerial Vehicles (UAVs) and quadrotors are being used in an increasing number of applications. The detection and management of forest fires is continually improved by the incorporation of new economical technologies in order to prevent ecological degradation and disasters. Using an inner-outer loop design, this paper discusses an attitude and altitude controller for a quadrotor. As a highly nonlinear system, quadrotor dynamics can be simplified by assuming several assumptions. Quadrotor autopilot is developed using nonlinear feedback linearization technique, LQR, SMC, PD, and PID controllers. Often, these approaches are used to improve control and to reject disturbances. PD-PID controllers are also deployed in the tracking and surveillance of smoke or fire by intelligent algorithms. In this paper, the efficiency using a combined PD-PID controllers with adjustable parameters have been studied. The performance was assessed by simulation using matlab Simulink. The computational study conducted to assess the proposed approach showed that the PD-PID combination presented in this paper yields promising outcomes.

Compensation control strategy of hybrid driven three-dimensional elliptical vibration assisted cutting system based on piezoelectric hysteresis model

Three-dimensional elliptical vibration assisted cutting (3D-EVC) technology has been widely used in many high-precision technical fields due to its high-efficiency processing characteristics. However, the hysteresis and nonlinearity caused by the piezoelectric drive in the 3D-EVC system will impact the system control accuracy. This paper mainly studies the hysteresis and nonlinearity of the system, the feedforward-gray predicted fuzzy PID compound controller based on the generalized Bouc-Wen hysteresis nonlinear model and it is designed to realize the hysteresis compensation of the system. In this paper, input voltage and output displacement are represented by a mathematical relationship, and this relationship of the 3D-EVC system will be described by the generalized Bouc-Wen model. The improved flower pollination algorithm (IFPASO) is adopted in the identification process of parameters. A compound control strategy is formed based on traditional feed-forward control combined with fuzzy PID feedback control to compensate for hysteresis and nonlinearity, and an improved gray prediction model is introduced into the feedback loop. The 3D-EVC system tracking experiment verifies the effectiveness of the designed compound controller. Experiments have proved that the hysteresis component of the system is significantly reduced after the use of the compound controller for hysteresis compensation, and the system has a higher degree of stability.

Development of a Conveyor Cart with Magnetic Levitation Mechanism Based on Multi Control Strategies

This paper presents the experimental magnetic levitation control development of Sanki Engineering airport luggage conveyor carts which have four magnetic levitation units working synchronously. With the PID controller, the state feedback controller and the zero-power controller refined by PID controller were implemented in the one magnetic levitation unit system and four-unit magnetic levitation system, and the displacement and the current were verified in a real-time system. The magnetic levitation unit had a fast response, and the control algorithms were easily implemented. The change of current and displacement were compared. In the one-unit system, the PID and state feedback controller react to the disturbance at the same speed and have similar power consumptions. For a disturbance on the zero-power controller, the system generates a transient current to deal with the load disturbance and finally settles to 0 A. The PID control for four magnetic levitation units of the conveyor cart has a better stable performance during synchronous operation. Under the control of state feedback controller, they can keep the cart statically stable with some oscillation. These characteristics are experimentally confirmed.

A Neural Network and Generalized Predictive Control Framework for Urban Traffic System Optimal Perimeter Control

Optimal perimeter control is one of the effective control technologies to alleviate urban congestion, which is based on macroscopic fundamental diagrams (MFDs). However, most previous optimal perimeter control methods used a linearization model at the desired point to approximate a nonlinear MFDs system, which is vulnerable to causing a potential mismatch between the linearization model and the environmental dynamics. However, this mismatch often leads to performances degradation. To solve this problem, this paper uses a neural network trained from data to approximate the complex nonlinear urban traffic system globally. To facilitate the design and control, this framework linearizes the nonlinear neural network of the traffic system to an instantaneous linearization model. The optimal perimeter control problem is finally solved by generalized predictive control (GPC) with the instantaneous linearization model. A key advantage of the proposed framework is that the global instantaneous linearization model is more accurate than the linearization model around the desired point. Simulation results show that the proposed framework significantly alleviates congestion and reduces the total travel time spent (TTS) in the traffic system compared with no control, “greedy” feedback control and PID control.

Using dynamic data reconciliation to improve the performance of PID feedback control systems with Gaussian/non-Gaussian distributed disturbance and measurement noise

For a stochastic PID feedback control system, the uncertainty of the working environment often leads to the unsatisfied performance of the system, which does not meet the profit requirements. The working environment generally includes external disturbance and measurement noise, etc. Gaussian distributed measurement noise and disturbances are widely considered while non-Gaussian distributed measurement noise and disturbances are rarely considered. In this paper, the performance degradation of Gaussian/non-Gaussian disturbances and measurement noise on a stochastic PID feedback system is considered and analyzed. An efficient method, dynamic data reconciliation (DDR) is developed to filter measurement noise and disturbances and improve the performance of the stochastic PID feedback control system. By utilizing model-based and measured information, DDR avoids time delays in output estimation. With the detailed theoretical analysis and simulation verification, the effectiveness of the proposed DDR technology on the stochastic PID feedback control system is verified. Compared with conventional exponential filters, DDR can achieve better control performance. The proposed DDR is also used for the control system of the DC–AC​ converter. The improved effect of DDR on the output quality is demonstrated by the results.

Energy-Optimal Unmanned Aerial Vehicles Motion Planning and Control Based on Integrated System Physical Dynamics

Abstract Electric vertical-take-off-and-landing multirotor aircraft has been emerging as a revolutionary transportation mode for both manned and unmanned applications, but this technology is limited by flight time and range restrictions. In this work, an energy-efficient model-based trajectory planning and feedback control framework is developed to improve the energy performance of a multirotor unmanned aerial vehicle. Target vehicle trajectories are planned by solving a formulated energy consumption optimization problem based on a system-level model, which accommodates the integrated dynamics of key vehicle subsystems. In order to implement the generated target trajectories, the framework also includes a PID feedback control architecture for real-time trajectory following. The framework is first verified under simulation, and shows an average reduction of 10.7% in energy consumption over a range of typical hover-to-hover operations, compared to the commonly used baseline flight control architecture. Through model-based analysis, key relationships that contribute to the improvements are identified and analyzed. These results demonstrate the importance of considering and coordinating all relevant system dynamics for efficient and holistic trajectory planning and control, which is absent in existing literature. The framework also demonstrates similar performance improvement under experimental validation, with an average energy reduction of 10.2% over the baseline controller despite the presence of significant real-world disturbances including wind effect.

Reduced Order Based Active Disturbance Rejection Controller Design with Applications

Active disturbance rejection control (ADRC) has emerged as a well-addressed controller design technique in recent years. It is a suitable replacement for the error-based feedback PID controller design approaches. The ADRC constitutes two controller design techniques: linear active disturbance rejection control (LADRC) and generalized active disturbance rejection control (GADRC). The LADRC design approach requires minimal information about the plant, while in the GADRC approach, detailed information about the plant is needed. For higher-order plants, the design of ADRC controllers and extended state observers may be pretty complex and costlier. So, the performance analysis of the higher order plant becomes difficult. Therefore, to make the controller simpler, it is always advantageous to reduce the controller size. This paper proposes reduced order linear active disturbance rejection control (ROLADRC), and reduced order generalized active disturbance rejection control (ROGADRC) techniques instead of full-order LADRC and GADRC approaches. The stability equation method (SEM) and other widespread model order reduction (MOR) methods are utilized to reduce the order of the plant. Further, ROLADRC and ROGADRC are compared with existing control techniques in the literature. The effectiveness of the proposed scheme is tested on the sun tracker system (STS) for position control and power systems for load frequency control (LFC).

Development and Analysis of a Novel Magnetic Levitation System with a Feedback Controller for Additive Manufacturing Applications

The primary goal of this study is to create a magnetic levitation system for additive manufacturing (AM) applications. The emphasis of this research is placed on Laser Directed Energy Deposition via Powder Feeding (LDED-PF). The primary benefit of using a magnetic levitation system for AM applications is that the levitated geometry is expected to be a portion of the final part manufactured, thus eliminating the need for a substrate and reducing the post-processing operation requirement. Two novel levitation systems were designed, optimized, and manufactured. The design, optimization, and analysis were first conducted in the simulation environment using ANSYS Maxwell and then tested with experiments. The newly developed systems depicted a much-improved performance compared to the first prototype developed in a previous article written by the authors. The newly developed systems had an increase in levitation height, the surface area for powder deposition activities, the time available for AM operations, and the ability to support additional mass within the limits of allowable inputs. The compatibility of the levitation system with AM applications was also verified by testing the impact of powder deposition and the ability of the levitated disc to support added mass as a function of time with minimal loss in performance. This article also highlights the development of a novel feedback PID controller for the levitation system. To improve the overall performance of the controller, a feedforward controller was added in conjunction with the PID controller. Finally, the levitation system was shown to highlight control over levitation height and maintain constant levitation height with the addition of an added mass using the feedback controller.

An active fault management for microgrids resilience safety-assurance

Active fault management (AFM) based on federated learning is established to realize ultra-integration of hundreds of microgrids, enabling them to output reference values fast enough during fault ride through (see the Figure, which is reprinted with permission from ref. iEnergy, 4: 453–462, 2022 © 2022 The Author(s)). AFM is first formulated as a distributed optimization problem. Then, federated is used to learning to train each microgrid's neural network. One concern for integrating optimization into power grid fault management and dynamic control is real-time performance because optimization usually takes more time to get reference values than widely used PID feedback control. To address this concern, controller hardware-in-the-loop (HIP) simulation with RTDS simulators is used to demonstrate the real-time performance of distributed-optimization-based fault management algorithms. In the hardware setup, one individual computer exclusively runs one microgrid or PV farm's control algorithm. Real-time simulation results demonstrate that the algorithms can output reference values within 100 ms, which can be considered well enough for fault management and dynamic control.

Sensing Design, Trajectory Planning, and Motion Control of a Cable-Driven Redundant Manipulator Composed of Quaternion Joints

Abstract A cable-driven redundant manipulator (CDRM) composed of quaternion joints has important applications in confined space, including minimal invasive surgery, aircraft parts assembly, environment exploring, and so on. Benefitting from the unique joint characteristic and cable routing, it can achieve a larger workspace with fewer driving modules than traditional universal joint CDRM. However, the positioning accuracy of the end-effector suffers from the lack of joint feedback information and the delay effect of cable driving mechanisms. In this paper, we propose an equivalent sensing method and design corresponding sensors for each quaternion joint, and develop a high precision controller for the whole manipulator. The motion sensing of the quaternion joint is achieved by establishing the kinematics between its bending pose and middle limb joints. To realize real-time estimation, the fitting technique is adopted. To improve the efficiency of path planning, a geometric iterative inverse kinematics approach for quaternion joint CDRM is proposed based on the isosceles trapezoid simplified model. Furthermore, an accurate controller is designed by combining the feedforward gain and modified PID feedback control. Finally, an 8DOF CDRM prototype with four quaternion joints is developed, and the experiment verifies the effectiveness of the proposed method.

Comparison of control strategies for hysteresis attenuation in electromechanical actuators subject to dispersion

This paper addresses the difficulties of designing highly efficient robust controllers for a class of systems exhibiting high hysteresis with parameters dispersion that limits control accuracy and performance homogeneity over the parametric uncertainties range. Two control strategies to solve the problem are assessed. First, a Reference Model Sliding Mode Control (RMSMC) feedback controller known to be robust to parametric uncertainty is designed to compensate hysteresis, regardless of the hysteresis quantity. Secondly, a strategy based on a feedforward controller with a Neural Network inverse model and a PID feedback controller is proposed. In this case, hysteresis dispersion is addressed through the integration of a backlash estimator for computing the Neural Network inverse model. The control strategies are implemented for position control of a Limited-Angle Torque Motor (LATM) exhibiting uncertain hysteresis. Experimental tests demonstrated the very good accuracy and robustness of the Neural Network inverse model and the PID controller for position tracking when the LATM is subject to dispersion and the benefits of the Reference Model Sliding Mode Control (RMSMC) feedback controller for the rejection of external disturbances.

Application of Least-Squares Support-Vector Machine Based on Hysteresis Operators and Particle Swarm Optimization for Modeling and Control of Hysteresis in Piezoelectric Actuators

Nanopositioning systems driven by piezoelectric actuators are widely used in different fields. However, the hysteresis phenomenon is a major factor in reducing the positioning accuracy of piezoelectric actuators. This effect makes the task of accurate modeling and position control of piezoelectric actuators challenging. In this paper, the learning and generalization capabilities of the model are efficiently enhanced to describe and compensate for the rate-independent and rate-dependent hysteresis using a kernel-based learning method. The proposed model is inspired by the classical Preisach hysteresis model, in which a set of hysteresis operators is used to address the problem of mapping, and then least-squares support-vector machines (LSSVM) combined with a particle swarm optimization (PSO) algorithm are used for identification. Two control schemes are proposed for hysteresis compensation, and their performance is evaluated through real-time experiments on a nanopositioning platform. First, an inverse model-based feedforward controller is designed based on the LSSVM model, and then a combined feedback/feedforward control scheme is designed using a classical control strategy (PID) to further enhance the tracking performance. For performance evaluation, different datasets with a variety of hysteresis loops are used during the simulation and experimental procedures. The results show that the proposed method is successful in enhancing the generalization capabilities of LSSVM training and achieving the best tracking performance based on the combination of feedforward control and PID feedback control. The proposed control scheme outperformed the inverse Preisach model-based control scheme in terms of both positioning accuracy and execution time. The control scheme that uses the LSSVM based on nonlinear autoregressive exogenous (NARX) models has significantly less computational complexity compared to our control scheme but at the expense of accuracy.

Nonlinear Modeling and Control of Polyvinyl Chloride (PVC) Gel Actuators

Polyvinyl chloride (PVC) gels represent a novel type of electroactive polymer actuators with a number of appealing characteristics, including low cost, high compliance, large strain, medium-to-high stress output, fast response, and thermal stability. Despite their vast potential in a variety of applications, modeling and control of nonlinear dynamics of PVC actuators has received little attention. In this article, we first present a data-driven approach to modeling nonlinear dynamics of PVC gel actuators. A Hammerstein model, consisting of a nonlinear module cascaded with linear dynamics, is proposed to capture the pronounced dependence of the voltage input–displacement output frequency response on the input amplitude and bias. A control scheme is then designed based on the model, where an inverse compensator for the nonlinear element is combined with a PID feedback controller. A disturbance observer is further introduced for the estimation and rejection of the influences from imperfect inverse compensation and model uncertainties. Experimental results are presented to support the efficacy of the proposed modeling and control approach. In particular, for a number of reference trajectories, the proposed control scheme results in over 80% reduction of tracking error in comparison with a well-tuned PID controller.

Development and Characteristic Investigation of a Multidimensional Discrete Magnetostrictive Actuator

Active combustion control (ACC) has been proved to be an effective method for the suppression of pressure oscillation in aero engine combustion chambers, which will seriously endanger flight safety of aircrafts. However, the actuators for ACC need to work with a bandwidth of 1000 Hz, a stroke up to submillimeter level and a high power density simultaneously. Existing electromagnetic actuators, or smart material actuators, are still unable to meet these requirements at the same time. To address this issue, in this article, a multidimensional discrete magnetostrictive actuator (MDMA) was developed by adopting the multidimensional discrete configuration. In order to obtain large output displacement while maintaining the characteristics of high bandwidth and high power density of magnetostrictive materials, tubular and cylindrical magnetostrictive rods are nested axially and radially through sleeves to form a discrete magnetostrictive stack; independent excitation coils and permanent magnet rings are integrated axially to form a discrete electromagnetic excitation system. Then, a magnetic equivalent circuit (MEC) model was developed to analyze the magnetic field distribution characteristic of MDMA components. Next, a multiphysics comprehensive dynamic model was established to describe the complex dynamic properties of multidimensional discrete structures. Subsequently, a prototype of the MDMA was fabricated, which is only 56 mm in diameter and only 71.5 mm high. Experimental results indicate that, the proposed MDMA reaches a stoke of 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> m, and the working bandwidth can exceed 1000 Hz. Furthermore, closed-loop control tests were conducted and the results showed that the MDMA can effectively track sinusoidal references of different amplitudes under low frequency with a feedback PID controller.

Exoskeleton for ankle joint flexion/extension rehabilitation

This work presents the modelling, design, construction, and control of an exoskeleton for ankle joint flexion/extension rehabilitation. The dynamic model of the ankle flexion/extension is obtained through Euler-Lagrange formulation and is built in Simulink of MATLAB using the non-linear differential equation derived from the dynamic analysis. An angular displacement feedback PID controller, representing the human neuromusculoskeletal control, is implemented in the dynamic model to estimate the joint torque required during ankle movements. Simulations are carried out in the model for the ankle flexion/extension range of motion (ROM), and the results are used to select the most suitable actuators for the exoskeleton. The ankle rehabilitation exoskeleton is designed in SolidWorks CAD software, built through 3D printing in polylactic acid (PLA), powered by two on-board servomotors that deliver together a maximum continuous torque of 22 [kg cm], and controlled by an Arduino board that establishes Bluetooth communication with a mobile app developed in MIT App Inventor for programming the parameters of the rehabilitation therapies. The result of this work is a lightweight ankle exoskeleton, with a total mass of 0.85 [kg] including actuators (servomotors) and electronics (microcontroller and batteries), which can be used in telerehabilitation practices guaranteeing angular displacement tracking errors under 10%.

Design and Validation of Reversing Assistant Based on Extreme Learning Machine

As an important function of the advanced driver assistance system (ADAS), the reversing assistant (RA) achieves trajectory retracing by applying accurate position estimation and tracking control. To overcome the problem of the modeling complexity in dead reckoning for the reversing assistant function, the heading angular rate is compensated by using the extreme learning machine (ELM) to improve the positioning accuracy. In addition, considering the time delay of the steering system, a tracking controller with a feed-forward of the recorded steering angle and a self-tuning PID feedback controller is designed based on the preview-and-following scheme. Vehicle experiments under various reversing scenarios prove that the proposed positioning method and tracking control scheme are effective, the overall lateral error is less than 10 cm, and the heading angle error is less than 1°, which meets the requirements of performance indicators.