Improve Control Performance Research Articles

Chaos synchronization control for stochastic nonlinear systems of interior PMSMs based on fixed-time stability theorem

This paper proposes an enhanced chaos synchronization control scheme of stochastic nonlinear systems for the permanent magnet synchronous motors(PMSMs) applications for dynamic operating mechanism under different working environment. Firstly, a novel fixed-time stochastic stability theorem and convergence for the stochastic nonlinear system is valid through formulating a novel Lyapunov function. Different from what has been achieved on the fixed-time stability, it’s not the usual range but it’s more precise for settling time which improving control performance effectually. Secondly, the chaos synchronization issue of driven-response PMSMs with stochastic noise is firstly investigated based on the proposed Lyapunov theorem synchronizing the complex dynamic systems considering practical significance. Finally, the fixed-time synchronization control scheme is developed via applying the adaptive algorithms, furthermore, through constructing a reasonable synchronization dynamic error system achieving stability in probability of fixed time. A simulation analysis is demonstrated to verify the availability of the proposed control strategy.

Design of droop controller in islanded microgrids using multi‐objective optimisation based on accurate small‐signal model

The inaccuracy of power sharing is a classic problem of droop control when an islanded AC microgrid suffers from high loads and line impedance differences. It degrades system performance and even destroys system stability. This paper originally presents a multi-objective optimisation droop control method to solve such a problem. And three objective functions are presented according to the characteristics of microgrids. First, a complete small-signal model of a microgrid in autonomous operation mode is established. Next, this paper derives constraint conditions for the stable microgrid. Then, this paper originally designs three objective functions for constructing a multi-objective optimisation (MOO) problem to improve control performance. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is used to obtain the Pareto optimal frontier of the MOO problem. The fuzzy affiliation function method is used to choose the best solution from the generated Pareto optimal frontier. Finally, simulations verified the validity and superiority of the presented method.

Disturbance observer-based robust coordination control for unmanned autonomous helicopter slung-load system via coupling analysis method

This work studies the robust coordination control for a strong coupled and under-actuated unmanned autonomous helicopter (UAH) slung-load system subject to external disturbances. Firstly, disturbance observers are designed to obtain the disturbance estimations, and a dynamic coupling matrix is introduced to describe the coupling relationships between the UAH system and the slung-load system. Secondly, based on the proposed coupling matrix, a disturbance observer-based coordination control scheme is designed for the position system of the UAH and the slung-load one by using hierarchical sliding mode control. Thirdly, an improved controller is designed for the attitude system of the UAH by combining the backstepping control, disturbance observer, and dynamic surface control, in which the detrimental coupling is suppressed while the beneficial one is retained to improve control performance. Especially, the stability for the overall closed-loop system is guaranteed based on Lyapunov stability theory. Finally, some simulations are presented to show the effectiveness and advantage of the proposed control scheme.

Exploring the Essence of Servo Pump Control

The electrohydraulic servo variable speed volume pump control system (hereinafter referred to as ESPCS) is integrated with a permanent magnet synchronous motor (hereinafter referred to as servo motor), a fixed-displacement pump, and a hydraulic cylinder. By controlling the servo motor speed, the output flow of the system can be controlled, as can the displacement, force, and speed of the hydraulic cylinder. Compared with the traditional electro-hydraulic servo valve control system, the ESPCS has the advantages of high power-to-weight ratio, energy saving, and environmental friendliness. However, due to the extremely nonlinear flow output of the ESPCS, further improvement of system control performance is greatly hindered. This paper focuses on the nonlinear characteristics of servo motor, fixed-displacement pump, hydraulic cylinder, and other key components in the system. A compensation method based on nonlinear characteristic mapping is proposed. Compared with the traditional PID control method (pressure control accuracy ± 0.12 MPa), the pressure control accuracy of the system is greatly improved (pressure control accuracy ± 0.037 MPa), which opens up a new way to improve the pressure control accuracy of the ESPCS.

705 Reduction in Central Line Associated Blood Stream Infection Rate with a Central Line Change-Over Protocol

IntroductionBurn Intensive Care Units (BICU)s traditionally have changed central lines over a wire (COWs) as a method to reduce infection. To date, there is not a standard timeline for this process and every center has their own timeline for this process. We aimed to create a standardized process for this practice to then have a baseline for future study.MethodsThe Change-Over-Wire (COW) process was evaluated with the BICU Infection Control Nurse and Performance Improvement Manager. A protocol was developed that includes step-by-step detail that was modified until it was deemed practical and acceptable to all involved parties as the best practice for maintaining the most sterility as possible. The protocol was provided to the Central Line team, the ICU Advisory Board, and the UNC CLABSI committee for reviewResultsWhile the hospital as a whole does not standardly endorse routine COWs, the protocol was reviewed and accepted as best practice by both the ICU Advisory Board and the UNC CLABSI committee. Additionally, the BICU had an 80% reduction in CLABSI’s from FY 2017 to 2020 with implementation of this protocol (p value = 0091).ConclusionsHaving a standardized method for COWs both allows for best practice and a starting point for study. Endorsement by both hospital committees allows this practice to be the published standard by which future studies can be measured.

Dynamic event-triggered adaptive command filtered control for nonlinear multi-agent systems with input saturation and disturbances

This paper investigates the dynamic event-triggered adaptive command filtered control for the nonlinear high-order multi-agent systems with input saturation and disturbances. By designing a novel dynamic event-triggered adaptive command filtered control, computational burdens have been removed comprehensively. First, a dynamic event-triggered mechanism is designed, which can dynamically adjust the update frequency of controllers to reduce the computational burdens caused by the continuous updates of controllers. Moreover, the proposed approach can save communication resources and improve control performances simultaneously. Second, the command filtered control is studied to remove the computational burdens caused by the repeated differentiation of virtual control signals. Furthermore, input saturation and disturbances are handled concurrently. Finally, simulation results are given to validate the efficacy of the proposed approach.

Real-Time Implementation of Randomized Model Predictive Control for Autonomous Driving

Model predictive control (MPC) using randomized optimization is expected to solve different control problems. However, it still faces various challenges for real-world applications. This paper attempts to solve those challenges and demonstrates a successful implementation of randomized MPC on the autonomous driving using a radio-controlled (RC) car. First of all, a sample generation technique in the frequency domain is discussed. This prevents undesirable randomness which affect the smoothness of the steering operation. Second, the proposed randomized MPC is implemented on a Graphics Processing Unit (GPU). The expected GPU acceleration in calculation speed at various problem sizes is also presented. The results show the improved control performance and computational speed that was not achievable using CPU based implementation. Besides, the selection of parameters for randomized MPC is discussed. The usefulness of the proposed scheme is demonstrated by both simulation and experiments. In the experiments, a 1/10 model RC car is used for collision avoidance task by autonomous driving.

Strategy of Synchronized SVPWM for Dual Three-Phase Machines in Full Modulation Range

In high-power drives, the switching frequency of the inverter is always strictly limited to avoid high switching losses. Synchronized space vector pulsewidth modulation (synchronized SVPWM) is usually adopted in this case to reduce harmonic distortion. In this article, an effective synchronous modulation strategy based on 24-sector SVPWM is proposed for dual three-phase machines in the full modulation range. A more accurate criterion is proposed to select the implementation under different pulse patterns through the inductance characteristics analysis of the dual three-phase machines. A new overmodulation strategy considering harmonics performance is proposed to guarantee a smooth transition to square wave mode under each pulse pattern. Besides, more pulse patterns are created in this article that satisfy voltage symmetry conditions, and it can reduce the sudden drop in switching frequency to improve control performance during the pulse pattern change. The proposed synchronized SVPWM strategy and its implementation method are validly verified by simulation and experimental results in the field-oriented-control system.

Speed-Sensorless Control of Induction Machines with LC Filter for Geothermal Electric Submersible Pumping Systems

A speed-sensorless state-feedback controller for induction machines (IMs) with LC filter is proposed. The speed and state estimation is based on a speed-adaptive observer, requiring only the measurement of the filter input currents. The motor currents are controlled by a state-feedback controller with prefilter and integral control action, in order to achieve fast and asymptotic set point tracking. Observer and controller gains are calculated offline using linear quadratic regulator (LQR) theory and updated online (gain-scheduling) in order to attain stability and improve controller performance in the whole operation range. Implementation aspects, such as discretization of the control system and reduction of computational effort, are taken into account as well. The proposed control scheme is validated by simulations and experimental results, even for critical operating conditions such as speed zero-crossings. It is shown that the overall control system performs very well under various load- and speed conditions; while its tuning remains simple which makes it attractive for industrial application such as geothermal electric submersible pumping (ESP) systems.

Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems

The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Optimal State Estimator with Integral Control (LQOSEIC). In this paper, the nonlinear system TWMR is stabilized utilizing a robust output feedback control called LQOSEIC. This controller allows a linearized model to emulate a model reference for the original nonlinear system. However, it works for a limited range of operations and will fail if the plant characteristics are unknown or uncertain. An adaptive neural network is used to overcome this problem. The adaptive neural controller is trained offline using LQOSEIC to obtain the initial weights of neurons for the network's hidden layers. After finishing the training, the LQOSEIC will be replaced by the adaptive neural controller. The main advantage of a neuro-controller is its ability to update the weights online depending on the error signal. If there are any disturbances or uncertainties that arises within the concerned nonlinear system, the neuro-controller will be able to handle it because of online learning that compensates for the effect of unpredictable conditions. The proposed adaptive neural network improves control performance and ensures the robust stability of the closed-loop control system. Finally, numerical simulations are used to demonstrate the efficacy of the proposed controllers.

Correction to: Improving Control Performance of Unmanned Aerial Vehicles through Shared Experience

Correction to: Improving Control Performance of Unmanned Aerial Vehicles through Shared Experience

Rapid-Erection Backstepping Tracking Control for Electrohydraulic Lifting Mechanisms of Launcher Systems

Uncertainties and disturbances widely exist in electrohydraulic lifting mechanisms of launcher systems, which may worsen the rapid-erection tracking accuracy and even make the system unstable. To deal with the issue, an asymptotic tracking control framework is developed for electrohydraulic lifting mechanisms of launcher systems. Firstly, the dynamic equations and state-space forms of the electrohydraulic lifting mechanism are modeled. Based on the system model, a nonlinear rapid-erection robust controller is constructed to achieve the improvement of the system control performance, in which a nonlinear feedback term is employed to remove the effects of uncertainties and disturbances on tracking performance. Compared to the existing results, the asymptotic tracking stability of the closed-loop system can be assured based on the Lyapunov theory analysis. In the end, the simulation example of an actual electrohydraulic lifting mechanism of the launcher system is done to validate the effectiveness with the proposed controller.

자기 베어링용 동위 와전류 변위 센서

Magnetic bearings use electromagnetic force to support the rotating shaft without any mechanical contact and actively control shaft vibration; hence, there is no mechanical friction and wear due to contact during the operation, and it has a semi-permanent lifespan. Because magnetic bearings are unstable by themselves, a gap sensor is necessary to stably control the position of objects. However, there is a limit to the improvement in control performance because the sensor is installed on one side of the bearing and is not aligned with the electromagnet. This paper presents a newly developed collocated eddy-current PCB displacement sensor for magnetic bearings. The PCB sensor is designed and built to install between the poles of a magnetic bearing and to minimize the electromagnetic interference. A sensor calibration test is performed to evaluate the sensitivity and noise of the collocated PCB sensor. In addition, the control performance of the collocated PCB sensor is evaluated by measuring the closed-loop sensitivity function of a 1 DOF magnetic suspension test rig. The collocated PCB sensor has noise within ±1 μm and excellent vibration suppression performance.

Efficient Finite-Set Model Predictive Voltage Control of Islanded-Mode Grid-Forming Inverters With Current Constraints

A grid-forming inverter (GFI) plays an important role in providing voltage and frequency support for island microgrids. This paper proposes a computationally efficient finite-set model predictive voltage control (EFS-MPVC) scheme for GFIs. To eliminate the state prediction process, a hybrid voltage-vector reference is first synthesized using the deadbeat principle, which enhances the steady-state performance and robustness to model mismatches. Then, a voltage-vector-based cost function is designed, and a fast optimal-voltage-vector (OVV) selection scheme is built to avoid multiple enumerations. Further, to reduce the additional computing efforts caused by current constraints, an offline lookup table for fast search of OVV is established. Experimental results verify that the proposed EFS-MPVC features reduced computational amount and improved control performance in comparison to conventional MPVC schemes.

Dual Control by Reinforcement Learning Using Deep Hyperstate Transition Models

In dual control, the manipulated variables are used to both regulate the system and identify unknown parameters. The joint probability distribution of the system state and the parameters is known as the hyperstate. The paper proposes a method to perform dual control using a deep reinforcement learning algorithm in combination with a neural network model trained to represent hyperstate transitions. The hyperstate is compactly represented as the parameters of a mixture model that is fitted to Monte Carlo samples of the hyperstate. The representation is used to train a hyperstate transition model, which is used by a standard reinforcement learning algorithm to find a dual control policy. The method is evaluated on a simple nonlinear system, which illustrates a situation where probing is needed, but it can also scale to high-dimensional systems. The method is demonstrated to be able to learn a probing technique that reduces the uncertainty of the hyperstate, resulting in improved control performance.

Co-Design Secure Control Based on Image Attack Detection and Data Compensation for Networked Visual Control Systems

The incomplete and untrue data caused by cyber attacks (e.g., image information leakage and tampering) will affect control performance and even lead to system instability. To address this problem, a novel co-design secure control method based on image attack detection and data compensation for networked visual control systems (NVCSs) is proposed. Firstly, the existing problems of NVCSs under image attacks are analysed, and a co-design secure control method including image encryption, watermarking-based attack detection and online data compensation is presented. Then, a detector based on double-layer detection mechanism of timeout and digital watermarking is designed for real-time, integrity and authenticity discrimination of the images. Furthermore, according to the detection results, an online compensation scheme based on cubic spline interpolation and post-prediction update is proposed to reduce the effect of cumulative errors and improve control performance. Finally, the online compensation scheme is optimized by considering the characters of networked inverted pendulum visual control systems, and experimental results demonstrate the feasibility and effectiveness of the proposed detection and control method.

Static pressure reset control strategy for roof fans in centralized cooking exhaust systems

Excessive static pressure and inadequate flow rate at each terminal of the centralized cooking exhaust systems contribute to poor indoor air quality in kitchens. Roof fans can improve the ventilation performance of centralized cooking exhaust system, but the optimal control strategy for roof fans is currently unavailable. This paper proposes a static pressure reset control strategy for variable frequency operation of roof fans that follows the law of normal distribution of simultaneous operating rate. The effect of the roof fan on the static pressure and flow rate distribution in a centralized cooking exhaust system with multiple power sources of range hoods is investigated. The relationship between simultaneous cooking rate and the appropriate set point of the roof fan is determined using experimental and simulation methods. The results show that the proposed static pressure reset control strategy for roof fans has improved control performance and significant potential for energy savings.

Control Strategy of Three-Level NPC Inverter Based on Variable Coefficient Virtual Vector Model Predictive Control

The Conventional model predictive control (C-MPC) uses only a single voltage vector in each control period, resulting in poor control performance. In addition, the computational burden in discrete space state generated by three-level inverter cannot be ignored. To improve the control performance, this paper proposes a variable coefficient virtual vector MPC (VC-VV-MPC) method, a five-level virtual space voltage vector is constructed to improve the control performance, and a scaling coefficient is used to further improve the control performance. In addition, a voltage vector pre-selection method is used to reduce the computational burden. The simulation and experiment results shown that, compare with C-MPC and adjacent vector MPC (AV-MPC), the proposed VC-VV-MPC can achieve the improvement of control performance under lower computational burden.

Model-Free Adaptive Control of the Failing Heart Managed by Mechanical Supporting Devices

Left ventricular assist devices (LVADs)—mechanical circulatory pumps—have been used as a therapeutic option for patients who have progressed to the advanced stage of left-sided heart failure (HF), which is a chronic condition that the heart is unable to pump sufficient blood out of the left ventricle as it should. This surgically implanted device supports the native heart to pump blood from the heart to the remainder of the body to keep the patient alive as a temporary therapy until a donor's heart is available or as a destination therapy. However, the pump speed of the LVAD is typically set in a constant mode and cannot be freely changed. To promote the clinical use of LVADs as a long-term treatment option, a physiological control system is required to adaptively adjust the pump speed in response to time-varying blood demand. To this end, a model-free adaptive control (MFAC) algorithm is developed in this work, which estimates the control parameter over time using the data of manipulated and controlled variables (i.e., end-diastolic pressure of left ventricle and pump speed, respectively). In addition, since hemodynamic parameters in the cardiovascular system change over time due to short-term autonomic nerve regulation, we further considered baroreflex regulation to improve control performance; baroreflex regulation is one of the key factors of human's hemostatic mechanisms. The performance of the physiological controller is investigated and validated with computer simulations, which shows that the LVAD can respond to constant and time-varying blood demand.

Sim2real for Autonomous Vehicle Control using Executable Digital Twin

In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.