Fast Control Algorithm Research Articles

Fast Beam-Pointing Control Algorithms Based on Deep Learning Models for Moving Target in MWPT

Fast Beam-Pointing Control Algorithms Based on Deep Learning Models for Moving Target in MWPT

An Adaptive Impedance Matching System With Fast Optimization Control Algorithm for Wireless Power Transfer via Magnetic Coupling Resonance

An Adaptive Impedance Matching System With Fast Optimization Control Algorithm for Wireless Power Transfer via Magnetic Coupling Resonance

Self-Adaptive PDLC Control Strategy With Smart Light Intensity Adjustment Using Photovoltaic-Thermoelectric Hybrid Energy Supply Technology

As an electronically controlled glass whose transmittance varies with voltage, polymer dispersed liquid crystal (PDLC) has the advantages of good privacy, high transmittance, fast response speed, and good heat shielding performance. It can be applied to electronically controlled building windows. However, PDLC needs to consume extra power, and electronically controlled PDLC has not yet been integrated with intelligent home environment control. In this paper, we proposed an adaptive variable voltage step-size fast control algorithm in Field Programmable Gate Array (FPGA)-based home environment control system, which is powered by photovoltaic-thermoelectric (PV-TEG) hybrid sources. The power supply voltage of the PDLC can be dynamically changed through the adaptive Pulse width modulation (PWM) duty cycle adjustment technology to realize intelligent transmittance control, thereby improving the indoor lighting environment. Experimental results show that by applying the proposed algorithm, the indoor temperature is reduced by 2°C, and the adaptive and stable regulation of the optimal indoor illumination of 500 Lux can be completed in less than 2.5 seconds. This paper provides a feasible solution for the low-energy intelligent integrated control and monitoring of the modern home environment.

Manipulator Control Based on Adaptive RBF Network Approximation

With the popularization of intelligent manufacturing, manipulator has found ever wider application in various industries. A manipulator requires a real-time and fast control algorithm in order to improve the accuracy in all kinds of precise operations. This paper proposes an algorithm based on adaptive radial basis function (RBF) for approximating the parameters of the manipulator, and the adaptive equations are designed to automatically adjust the weight of RBF. Proportional integral (PI) robust based on dynamic error tracking is used in controller to reduce the steady state errors and enhance the anti-interference performance of the system. The global asymptotic stability of the system is demonstrated by defining an integraltype Lyapunov function. Finally, MATLAB is used to simulate the angular positions tracking and angular velocities tracking of the double joints manipulator. The results show that the manipulator can track the ideal output signal quickly and accurately and has good anti-interference performance.

Adaptive Finite-Time Trajectory Tracking Control for Coaxial HAUVs Facing Uncertainties and Unknown Environmental Disturbances

In this paper, the problems of system design, dynamic modeling, and trajectory tracking control of coaxial hybrid aerial underwater vehicles (HAUVs) with time-varying model parameters and composite marine environment disturbances are investigated. It is clear that a stable transition between different media remains a challenge in the practical implementation of amphibious tasks. For HAUVs, accurate dynamic modeling to describe complex dynamic variations during vehicle takeoff from underwater to air is a huge challenge. Meanwhile, due to the rapid changes in model parameters and the external environment, vehicles are likely to fall into the sea during the cross-domain process. An integrated continuous dynamic model considering hydrodynamic changes is established by introducing a linear switching coefficient during the process of trans-medium motion. A nonsingular fast terminal sliding-mode control (NFTSMC) algorithm combined with adaptive technology is used to design the position and attitude of the controller. With no previous knowledge of external interferences and lumped uncertainties of the HAUV, the adaptive NFTSMC (ANFTSMC) algorithm achieves the control objectives; at the same time, the inherent chattering problems of sliding mode control (SMC) are weakened. The finite-time stability of the global system is proven strictly using a series of mathematical derivations based on Lyapunov theory. The effect of the controller applied is analyzed through a series of simulations with representative working conditions. The results show that the proposed ANFTSMC can realize a “seamless” air–water trans-medium process, which proves the superiority and robustness of the proposed control algorithm.

Fast Fixed-Time Output Multi-Formation Tracking of Networked Autonomous Surface Vehicles: A Mathematical Induction Method

In this paper, we aim to exploit an effective way to solve the output multi-formation tracking problem of the networked autonomous surface vehicles (ASVs) in a fast fixed time manner. Specifically, addressing the output multi-formation tracking problem implies that 1) the networked ASVs are divided into multiple interconnected subnetworks with respect to multiple targets; 2) for each subnetwork, the outputs of the networked ASVs form a desired geometric formation with exchanging the local interactions. Besides, solving the fast fixed-time tracking problem in this paper implies that 1) the convergence time is independent of the initial conditions; 2) the system states are forced to reach the employed nonsingular fixed-time sliding surface in a prescribed time, which thus called fast fixed-time control. Then, based on a time-related function and a nonsingular fixed-time sliding surface, a hierarchical fast fixed-time control algorithm is proposed to solve the aforementioned problem within a fast fixed time being independent of the initial conditions. Furthermore, by employing the Lyapunov argument and mathematical induction, we present the sufficient conditions for fast fixed-time convergence of the tracking errors with respect to multiple targets. Finally, numerous simulation examples are presented to demonstrate the effectiveness of the proposed control algorithm.

A Surgical Robotic Framework for Safe and Autonomous Data-Driven Learning and Manipulation of an Unknown Deformable Tissue with an Integrated Critical Space

Aside from reliable robotic hardware and sensing technologies, to successfully transition from teleoperation to an autonomous and safe minimally invasive robotic surgery on unknown Deformable Tissues (U-DTs), various challenges need to be simultaneously considered and tackled to ensure safety and accuracy of the procedure. These challenges mainly include but are not limited to online modeling and reliable tracking of a U-DT with integrated critical tissues as well as development of reliable and fast control algorithms to enable safe, accurate, and autonomous surgical procedures. To collectively and simultaneously address these challenges and toward performing an autonomous and safe minimally invasive robotic surgery in a confined environment, in this paper, we present a surgical robotic framework with (i) real-time vision-based detection algorithm based on a Convolutional Neural Network (CNN) architecture that enables tracking the time-varying deformation of a critical tissue located within a U-DT and (ii) a complementary data-driven adaptive constrained optimization approach that learns deformation behavior of a U-DT while autonomously manipulating it within a time-varying constrained environment defined based on the output of the CNN detection algorithm. To thoroughly evaluate the performance of the proposed framework, we used the da Vinci Research Kit (dVRK) and performed various experiments on a custom-designed U-DT phantom with an arbitrary deformable vessel embedded within the phantom’s body (serving as the U-DT’s integrated critical space). Various experiments were conducted and analyzed to demonstrate the performance of the proposed framework and ensure robustness and safety while performing an autonomous surgical procedure.

Underwater Pollution Tracking Based on Software-Defined Multi-Tier Edge Computing in 6G-Based Underwater Wireless Networks

The forthcoming 6G networks are expected to provide a vision of overlapping aerial-ground-underwater wireless networks. Meanwhile, the rapid development of the Internet of Underwater Things (IoUTs) brings forth many categories of Autonomous Underwater Vehicle (AUV)-assisted Underwater Wireless Networks (UWNs). In this paper, we argue that the AUV-assisted UWNs can be intelligently utilized to track underwater pollution. To perform smart underwater pollution tracking, we propose the paradigm of AUV flock-based networking system and Software-Defined Networking (SDN)-enabled AUV flock Networking System (SDN-AUVNS). We introduce the concept of Mobile Edge Computing (MEC) into the control of SDN-AUVNS and propose the upgrade of the control plane of the SDN-AUVNS to with the multi-tier edge computing ability. By the proposed system architecture, we adopt the artificial potential field theory to construct the network controlling model. And we present the underwater tracking model for SDN-AUVNS, especially for the underwater pollution equipotential line of a particular concentration. Furthermore, to provide accurate path planning for the equipotential line tracking, we utilize the linearizability mechanism to optimize and revise the control input for the SDN-AUVNS. Lastly, we give a fast united control algorithm that can intelligently schedule the SDN-AUVNS to track underwater pollution equipotential lines. In particular, we propose a smart approach with the name of ’Inverse Distance Weighting’ to optimize the detection sample of the SDN-AUVNS. Evaluation results indicate that our proposal is able to track/survey the equipotential lines within a satisfactory error.

Adaptive optimal control of entangled qubits.

Developing fast, robust, and accurate methods for optimal control of quantum systems comprising interacting particles is one of the most active areas of current science. Although a valuable repository of algorithms is available for numerical applications in quantum control, the high computational cost is somewhat overlooked. Here, we present a fast and accurate optimal control algorithm for systems of interacting qubits, QOALA (quantum optimal control by adaptive low-cost algorithm), which is predicted to offer [Formula: see text](M2) speedup for an M-qubit system, compared to the state-of-the-art exact methods, without compromising overall accuracy of the optimal solution. The method is general and compatible with diverse Hamiltonian structures. The proposed approach uses inexpensive low-accuracy approximations of propagators far from the optimum, adaptively switching to higher accuracy, higher-cost propagators when approaching the optimum. In addition, the utilization of analytical Lie algebraic derivatives that do not require computationally expensive matrix exponential brings even better performance.

An adaptive backstepping nonsingular fast terminal sliding-mode control for the electromechanical brake system with backlash nonlinearity compensation

With the operation of low-floor trams, unavoidable nonlinear factors such as backlash and parameter perturbation are present in electromechanical brake (EMB) systems. This results in poor clamping force tracking performance and challenges the operational safety of trams. Thus, an adaptive nonsingular fast terminal sliding-mode control algorithm is proposed to address this thorny problem. Firstly, a differentiable function approximates the backlash nonlinearity. Then, a dynamic mathematical model for an EMB system with backlash nonlinearity and parameter perturbation is established. Based on the backstepping control principle, this paper divides the system into three subsystems, and the nonsingular fast terminal sliding-mode control (NFTSMC) algorithm is adopted to design the controller of each subsystem. Notably, the NFTSMC algorithm can solve the parameters perturbation problem by the large switching gains, which may cause the chattering phenomenon. This paper utilizes the adaptive law to establish an adaptive NFTSMC algorithm to improve this phenomenon. Furthermore, the global asymptotic stability of the system is analyzed using Lyapunov theory. Finally, experimental results clearly validate the effectiveness and superiorities of the proposed control algorithm.

Improved MPPT System Based on FTSMC for Thermoelectric Generator Array Under Dynamic Temperature and Impedance

The thermoelectric generator (TEG) is typically used as a clean power supply to harvest waste heat energy in applications involving a large thermal gradient, such as industrial heat removal and power electronic equipment systems. However, it is often difficult to achieve the optimal output power in the loop of the array system of the TEG owing to different output loads. This article proposes an improved fast terminal sliding-mode variable-structure control algorithm (FTSMC) to maximize power point tracking. The variable-structure sliding-mode control function used in the nonlinear sliding-mode surface of the algorithm allows us to obtain the characteristics of global stability that can enable it to converge to the sliding-mode surface at any position to reduce chatter. Digital modeling and simulation as well as experimental developmental field programmable gate array platforms were built to verify the effectiveness of the proposed FTSMC. It can attain the nonlinear sliding mode more quickly than the traditional sliding-mode algorithm. The results of experiments show that it can reach a tracking response speed of 0.08 s and a maximum conversion efficiency of 99.91%. The work here provides a new way for the efficient use of the TEG array for waste heat recovery.

Fast Model Predictive Control of PEM Fuel Cell System Using the L1 Norm

This work describes the development of a fast Model Predictive Control (MPC) algorithm for a Proton Exchange Membrane (PEM) fuel cell. The MPC cost-function used considers the sum of absolute values of predicted control errors (the L1 norm). Unlike previous approaches to nonlinear MPC-L1, in which quite complicated neural approximators have been used, two analytical approximators of the absolute value function are utilised. An advanced trajectory linearisation is performed on-line. As a result, an easy-to-solve quadratic optimisation task is derived. All implementation details of the discussed algorithm are detailed for two considered approximators. Furthermore, the algorithm is thoroughly compared with the classical MPC-L2 method in which the sum of squared predicted control errors is minimised. A multi-criteria control quality assessment is performed as the MPC-L1 and MPC-L2 algorithms are compared using four control quality indicators. It is shown that the presented MPC-L1 scheme gives better results for the PEM.

Fast finite-time tracking control for a 3-DOF cable-driven parallel robot by adding a power integrator✰

Cable-driven parallel robots (CDPRs) usually suffer from kinematic and dynamic uncertainties, which makes it difficult for traditional trajectory tracking control algorithms to achieve high precision, fast response time, and robustness. In this study, we present a novel fast finite-time tracking control (FFTTC) algorithm which solves this problem to a large extent. Specifically, we firstly used a function of exponential errors with fractional power combined with API technique, to deal with the key difficulty of the convergence rate degradation which exists in traditional finite-time tracking control (TFTTC) when system states are far from the equilibrium point. Simultaneously, the API technique was used to avoid the problem of the explosion of complexity. To facilitate algorithm evaluation, the finite-time stability of the close system consisting of the proposed FFTTC algorithm and the CDPRs was proved mathematically and the settling time was estimated correspondingly. The trajectory tracking experiments were performed on a 3-DOF CDPR driven by 4 cables. Simulation and experimental results show that the proposed FFTTC algorithm can cope with external disturbances, variable load, and inaccurate model parameters. The comparison experiment indicates that the proposed FFTTC algorithm is superior to the model predictive control and TFTTC algorithms in precision, response speed and robustness.

An adaptive multiagent control system for autonomous economic operation and resilience assurance in a hybrid-energy islanded microgrid

Multi-agent control is widely recognized as an effective management system for modern microgrids. This paper proposes a hierarchical distributed multi-agent control system for an islanded microgrid with hybrid-energy resources and a battery-storage bank. The dispatchable dispersed generators are to be online-controlled to stabilize the islanded microgrid and minimize operation cost. A non-dispatchable agent is assigned to each bus with a non-dispatchable generator or a load. A dispatchable agent is assigned to each bus with a dispatchable generator. The control scheme is organized into three layers. The primary control layer comprises only the agent which runs the very fast local voltage/frequency droop-control of the battery-storage bank. It achieves a quasi-instantaneous power balance under any disturbance to maintain the frequency and voltage stability of the microgrid. The secondary control layer is formed by fast control algorithms on the dispatchable agents working at 2 ms time step. It is responsible for regulating the islanded microgrid frequency and voltage at minimum participation time of the battery-storage bank. The tertiary control layer is made by relatively slow control algorithms on the dispatchable agents working at a time step of 200 ms. It copes with the economic operation of the microgrid. Each agent behaves autonomously and shares data with others via communication links. The architecture and control algorithms of each layer are presented. Besides, the coordination protocol between different layers is described. The scheme is implemented by a parallel Matlab-Jade computing setup. Results of a case study islanded microgrid are analysed and compared to literature.

Dual-mode fast DMC algorithm for the control of ORC based waste heat recovery system

Organic Rankine Cycle (ORC) system stands out in low-grade waste heat recovery for its outstanding performance. Considering the fluctuating nature of the heat source, model predictive control (MPC) is used to control the ORC systems and has achieved good performance. However, with the increase of the number of variables and the expansion of the control/prediction horizon, MPC will take longer calculation time. This may bring about the incapacity to obtain optimization problem solutions in one sampling instant and finally, lead to the failure of the setpoint tracking. To address this problem, in this article, a dual-mode fast dynamic matrix control (FDMC) algorithm grounded on singular value decomposition (SVD) and dynamic matrix control (DMC) is proposed. The presented algorithm features in its calculation speed improvement on the premise of ensuring tracking performance. Two simulations on disturbance rejection and setpoint tracking are performed to verify the effectiveness and rapidity of the proposed algorithm in comparison with the traditional DMC approach.

PeacoQC: Peak-based selection of high quality cytometry data.

In cytometry analysis, a large number of markers is measured for thousands or millions of cells, resulting in high‐dimensional datasets. During the measurement of these samples, erroneous events can occur such as clogs, speed changes, slow uptake of the sample etc., which can influence the downstream analysis and can even lead to false discoveries. As these issues can be difficult to detect manually, an automated approach is recommended. In order to filter these erroneous events out, we created a novel quality control algorithm, Peak Extraction And Cleaning Oriented Quality Control (PeacoQC), that allows for automated cleaning of cytometry data. The algorithm will determine density peaks per channel on which it will remove low quality events based on their position in the isolation tree and on their mean absolute deviation distance to these density peaks. To evaluate PeacoQC's cleaning capability, it was compared to three other existing quality control algorithms (flowAI, flowClean and flowCut) on a wide variety of datasets. In comparison to the other algorithms, PeacoQC was able to filter out all different types of anomalies in flow, mass and spectral cytometry data, while the other methods struggled with at least one type. In the quantitative comparison, PeacoQC obtained the highest median balanced accuracy and a similar running time compared to the other algorithms while having a better scalability for large files. To ensure that the parameters chosen in the PeacoQC algorithm are robust, the cleaning tool was run on 16 public datasets. After inspection, only one sample was found where the parameters should be further optimized. The other 15 datasets were analyzed correctly indicating a robust parameter choice. Overall, we present a fast and accurate quality control algorithm that outperforms existing tools and ensures high‐quality data that can be used for further downstream analysis. An R implementation is available.

Fast control parameterization optimal control with improved Polak–Ribière–Polyak conjugate gradient implementation for industrial dynamic processes

This paper proposes a fast control parameterization optimal control algorithm for industrial dynamic process with constraints. Derived from the frame of control variable parameterization (CVP) technique, the proposed method combines an efficient gradient computation strategy with an improved nonlinear optimization computation approach to overcome the challenge of computation efficiency caused by gradients and bounds in optimal control problems. Firstly, a fast gradient computation method based on the costate system of Hamiltonian function is developed to decrease the computational expense of gradients by employing approximate treatments and numerical integration strategy. Then, a trigonometric function transformation scheme is presented to tackle the boundary constraints so that the original optimal control problem is further converted into an unconstrained one. On this basis, an improved restricted Polak–Ribière–Polyak (PRP) conjugate gradient approach is introduced to solve the nonlinear optimization problem by using conjugate gradient iterations and strong Wolfe line search. Meanwhile, to enhance the convergence, a restricting condition is imposed in strong Wolfe line search to create iteration step-length. Finally, the proposed algorithm is implemented on three dynamic processes. The detailed comparison among the classical CVP method, literature results and the proposed method are carried out. Simulation studies show that the proposed fast approach averagely saves more than 90% computation time in contrast to the classical CVP method, demonstrating the effectiveness of the proposed fast optimal control approach.

Automatic Balancing Control of Air-bearing Simulator Based on Firefly Algorithm Improved Neural Network

In order to accurately reproduce the multi-satellite networking technology and simulate the multi-mode high-resolution imaging process on the ground, the three-axis air-bearing test bed was the key device of simulation. In this paper, a fast automatic balance adjustment control algorithm was proposed based on firefly algorithm improved BP (Back Propagation) neural network PID (Proportion Integration Differentiation) control. Aiming at the problem of long adjustment time and easy to obtain a non-optimal solution, the firefly algorithm was introduced to optimize the initial weight and threshold value of the BP neural network, and improve the algorithm performance in convergence rate and stability. Based on the kinematics and dynamics model of the three-axis air-bearing simulator platform, the simulation results show that the optimized algorithm reduces the <italic>x</italic>-axis centroid offset to 2.3×10^-7 m in 3.1 s. The algorithm has faster and higher stability on air-bearing simulator automatic balancing control, and satisfies the demand for multi-satellite imaging process simulation.

Improved fast three vector predictive current control of permanent magnet synchronous motor

In order to improve the steady-state performance of the traditional model predictive control and reduce the amount of calculation of the control algorithm, this paper proposes a new fast three-vector predictive current control algorithm, which uses the principle of deadbeat control of the dq-axis current to calculate the reference voltage vector, and obtains the reference voltage vector position through sector judgment, combined with the three-vector model predictive control algorithm, the optimal voltage vector is directly applied to the motor, without the need to predict the behavior of the system under the voltage vector, which greatly reduces the amount of calculation. At the same time, due to the introduction of three vectors, three functions are applied in one cycle. The voltage vector greatly improves the steady-state characteristics of the system. The simulation results show that the proposed new three-vector predictive current control algorithm can reduce the amount of calculation and improve the steady-state characteristics of the system.

Fast charging converter and control algorithm for solar PV battery and electrical grid integrated electric vehicle charging station

Electric Vehicles (EV) offer eco-friendly transportation, but the growth of the electric vehicle market year over year is very minimal due to insufficient EV charging stations, slow charging time and grid instability during peak hours. This paper proposes a high gain, fast charging DC–DC converter and a control algorithm for grid integrated Solar PV based Electric Vehicle Charging Station (SPV-EVCS) with battery backup. The proposed converter and its control algorithm’s performance are investigated in three different modes using MATLAB/Simulink tool and the simulated results are validated with Real-Time Digital Simulation (RTDS) in OPAL-RT. The observed results meet the Power Quality limits of an IEC61000-3-2 standard and met the level-4 dc charging standard IEC61851.