Implementation Of Control Algorithm Research Articles

GPU implementation of a fast active noise control algorithm with multiple reference signals

Traditional active noise control (ANC) systems are mostly implemented on digital signal processor (DSP). Due to the inadequate computational power of the DSP, especially in applications with multiple reference signals, the DSP is unable to update the optimal control filter for different types of noise. Hence, Graphics Processing Unit (GPU) has been incorporated to the ANC system to enhance its computational power. In this work, a DSP-CPU-GPU computing architecture is adopted to improve the computational performance of the ANC system and ensure the real-time signal processing. In addition, a fast ANC algorithm on the GPU is employed to update the control filters to handle different reference signals. Finally, the effectiveness of the method is verified by experiments in the condition of multiple reference signals.

Research on the Implementation of Financial Risk Control Models and Algorithms Based on Machine Learning

Nowadays, machine learning has been rapidly applied to the financial services industry. With the applications of machine learning, it will bring challenges to the stable development of the financial industry. This paper studies the user behavior analysis algorithm based on operator big data, and provides financial risk control mechanism for financial enterprises. Taking finance as the data collection object, in the user behavior prediction algorithm, the experiment uses the spectral clustering algorithm to cluster the non-convex data samples, and converts the samples to the Laplacian feature space. It not only reduces the dimension, but also clusters the data, and obtains the global optimal solution. The proposed algorithm is analyzed experimentally, and the tool prototype is realized. The developed gadgets have also been applied in enterprises. The research provides a method for the construction of a risk control model in the financial industry, thereby helping financial enterprises to better control the risk of lending. This paper also provides reference for the performance improvement of the model.

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Design and Implementation of Extremum-Seeking Control Based on MPPT for Dual-Axis Solar Tracker

The increase in the production efficiency of photovoltaic technology depends on its alignment in relation to the solar position. Solar tracking systems perform the tracking action by implementing control algorithms that help the reduction of tracking errors. However, conventional algorithms can reduce the life of actuators and mechanisms due to control action, significantly reducing operation times and profitability. In this article, an unconventional control scheme is developed to address the mentioned challenges, presenting the design and implementation of an extremum-seeking control to perform maximum power point tracking for a two-axis solar tracker instrumented with a solar module. The proposed controller is governed by the dynamics of a classic proportional-integral scheme and assisted by sensorless feedback. Also, it has an anti-wind-up-type configuration for the integral component and counts with a variable amplitude for the dither signal. The proposal is validated experimentally by comparison between a fixed system and a two-axis system in azimuth-elevation configuration. In addition, two performance indices are defined and analyzed, system energy production and tracking error. The results show that the proposal allows producing up to 27.75% more than a fixed system, considering the tracker energy consumption due to the tracking action and a pointing accuracy with ±1.8° deviation. Finally, an analysis and discussion are provided based on the results, concluding that the proposed algorithm is a viable alternative to increase the performance of tracked photovoltaic systems.

Green energy solution for grid integrated solar PV system for water storage towers

In India, water storage towers are present almost everywhere, especially across small towns to rural areas. The storage tanks either utilize diesel powered or utility grid fed induction motor (IM) water pumps. The large spaces available with water storage towers can be utilized to plant solar PV panel for large storage towers water pumping operation, hence, making a switch from diesel or grid run storage towers to solar operated storage towers. This paper presents a robust control of grid integrated solar PV-based water pump system with efficient and smooth transition between standalone and grid interfaced modes. A smart DC link voltage control is presented for distributed generation system (DGS), which provides full control on DC link voltage for both modes of operation at all dynamic load conditions. An enhanced phase-locked loop (EPLL) control is utilized for grid source converter (GSC) in grid control operation, making it flexible to different grid scenarios and power quality. It works for harmonics suppression, load balancing and grid side converter control. An enhanced phase-locked loop (EPLL) is implemented for synchronization control algorithm, which provides an accurate estimation of voltage angle even during frequency ramps and exhibiting superior performance particularly in terms of robustness against DC offsets. The primary requirement for smooth operation of grid-tied solar PV system is effective power management between different sources. The issues of power management and power quality, are discussed in this work, and solutions are presented. The implemented control algorithms are tested successfully for dynamic solar insolation, varying load conditions, unbalance in load, sudden grid outage condition. The system appropriateness is validated by experimental results.

METHOD OF PROTOTYPING CONTROL UNITS BASED ON THE OPENPLC APPLICATION

The problem of designing control devices based on programmable logic controllers (PLCs) is their high cost and, as a rule, unavailability at the initial design stage. The purpose of the research is to reduce the time and cost of designing the system by creating prototypes of control units with the software implementation of control algorithms in the languages of the MEK 61131-3 standard and the execution of programs in the Arduino board. The research method consists in the decomposition of project models of operating and control automata of the control device and their implementation in the software environment of the OpenPLC application in the form of program organization components (POU) in Ladder Diagram, Function Block Diagram, Sequential Function Chart and Structured Text languages. The result of the study is a method of creating typical POU operating and control automata of the control system, which are executed in the Arduino board. An example of the application of the proposed methodology for the design of a prototype of the facility's temperature control system is given. The developed prototype was tested using a logical PLC and a physical prototype, which confirmed their functional compliance with the original and a reduction in the cost of the equipment by at least an order of magnitude.

On multi-use applications unlocking behind-the-meter flexibility for grid-serving services

This paper introduces multi-use applications to unlock behind-the-meter flexibility by integrating grid-serving capabilities with user-oriented control strategies. We deploy a hierarchical flexibility coordination process in a co-simulation research environment and propose a control strategy prioritizing self-consumption optimization with a secondary focus on grid-serving applications. This approach enhances the viability of behind-the-meter technologies, particularly benefiting battery energy storage systems with high investment costs. It also facilitates active grid operation amid the growing presence of distributed energy resources. The research’s key contribution lies in designing an information architecture to efficiently utilize behind-the-meter flexibility for grid-serving processes. The paper demonstrates the feasibility of a multi-use operation, combining user-oriented optimization and grid-serving flexibility provision for local grid congestion management. The implemented control algorithm in a home energy management system achieves grid-serving objectives while maintaining behind-the-meter asset performance. Co-simulation results show that in less than 2.5% of simulated time steps, transformer utilization exceeds operational limits in both scenarios, indicating the viability of multi-use operation for leveraging new value streams and managing grid congestion simultaneously.

End-to-end deep reinforcement learning for control of an autonomous underwater robot with an undulating propulsor

This paper focuses on the development and implementation of control algorithms for positioning an Autonomous Underwater Vehicle (AUV) with an undulating propulsor, using reinforcement learning methods. It provides an analysis and overview of works incorporating reinforcement learning methods such as Actor-only, Critic-only, and Actor-Critic. The paper primarily focuses on the Deep Deterministic Policy Gradient method and its implementation using deep neural networks to train the Actor-Critic agent. In the agent's architecture, a replay buffer and target neural networks were utilized to address the data correlation issue that induces training instability. An adaptive ar-chitecture was proposed for training the agent to force the robot to move from the initial point to any target point. Additionally, a random target point generator was incorporated at the training stage so as not to retrain the agent when the target points change. The training objective is to optimize the actor's policy by optimizing the critic and maximizing the reward function. Reward function is determined as the distance from the robot's center of mass to the target points. Consequently, the reward received by the agent increases when the robot gets closer to the target point and becomes maximal when the target point is reached with an acceptable error.

Examination of a Magnetorheological Damper Control System with Vibration Energy Harvesting

Abstract The study deals with the experimental examination of a magnetorheological (MR) damper control system with vibration energy harvesting using a specially engineered electronic unit (EU). Unlike a typical MR damper control system, which requires an external energy source, the developed system is powered exclusively by energy extracted from a vibrating structure (mechanical system with one-degree-of freedom) and processed through the EU. The work describes the structure and functions of the EU, presents the test rig and the control algorithm implementation, and discusses the test results of the control system under harmonic kinematic excitations of low frequency range.

Modeling and Control Simulation of Power Converters in Automotive Applications

This research introduces a model-based approach for the analysis and control of an onboard charger (OBC) system for contemporary electrified vehicles. The primary objective is to integrate the modeling of SiC/GaN MOSFETs electrothermal behaviors into a unified simulation framework. The motivation behind this project stems from the fact that existing literature often relies on finite element method (FEM) software to examine thermal dynamics, necessitating the development of complex models through partial derivative equations. Such intricate models are computationally demanding, making it difficult to integrate them with circuit equations in the same virtual environment. As a result, lengthy wait periods and a lack of communication between the electrothermal models limit the thorough study that can be conducted during the design stage. The selected case study for examination is a modular 1ϕ (single phase) onboard computer (OBC). This system comprises a dual active bridge (DAB) type DC/DC converter, which is positioned after a totem pole power factor correction (PFC) AC/DC converter. Specifically, the focus is directed toward a 7 kW onboard computer (OBC) utilizing high-voltage SiC/GaN MOSFETs to ensure optimal efficiency and performance. A systematic approach is presented for the assessment and selection of electronic components, employing circuit models for the totem pole power factor correction (PFC) and dual active bridge (DAB) converter. These models are employed in simulations closely mimicking real-world scenarios. Furthermore, rigorous testing of the generated models is conducted across a spectrum of real-world operating conditions to validate the stability of the implemented control algorithms. The validation process is bolstered by a comprehensive exploration of parametric variations relative to the nominal case. Notably, each simulation adheres to the recommended operational limits of the selected components and devices. Detailed data sheets encompassing electrothermal properties are provided for contextual reference.

Automated PLC Code Generation for the Implementation of Mode-Based Control Algorithms in Buildings

Faulty programming of control functions in Building Automation and Control Systems (BACS) might result in inefficient building operations. To reduce programming errors, an automated implementation process of control functions might be a promising solution. Recently, Building Information Modeling (BIM) contributes to digitizing building construction projects but is rarely used in the planning and implementation of control functions in BACS. The control description in BIM also remains unclear. Regarding these problems, a control documentation method for BIM and an automated control implementation approach can simplify control implementation in BACS and hence improve the building operation. In the previous work, we developed the MODI method for a structured planning process of mode-based control algorithms for building energy systems. This method showed the potential to support digitized control planning and implementation in BACS. Based on this, in this paper, we introduce a documentation method to report mode-based control algorithms in the industrial foundation class (IFC), enabling data sharing among BIM, and a software-assisted approach to automatically generate PLC codes for implementing these algorithms. The case study demonstrates the documentation of a desired mode-based control strategy for an energy supply network in IFC and the implementation of this strategy in a PLC program. In the simulation phase, we test the implemented control strategy to verify the functionalities of the PLC program. The results prove that mode-based control strategies can be fully automatically implemented in a PLC program based on IFC data.

Study of an improved single neuron PID control algorithm in the Tokamak plasma density control system

Tokamak is an important device for controlled nuclear fusion research. The plasma electronic density control system (PEDCS) is an important system for controlling the Tokamak discharge process, which should be of high stability, rapidity, and accuracy. Gas seeding systems are widely used in many Tokamak devices to achieve plasma electronic density control. According to the mechanism model analysis for the plasma electronic density object, an adapted single neuron proportion integration differentiation (PID) control algorithm with the radial basis function (RBF) neural network tuning is studied. The principle and the implementation of the intelligent control algorithm are described in detail in this paper. The intelligent controller enables the system to optimize the PID parameters online according to the density state in the discharge process. The experimental results show that the adapted algorithm achieves a good control effect and also improves the control performance. The proposed method provides a useful reference for Tokamak devices and other similar control systems.

Exploring Micro Aerial Vehicle Mechanism and Controller Design Using Webots Simulation

Quadrotor Micro Aerial Vehicle (MAV) is one of the Unmanned Aerial Vehicle (UAV) which very versatile with their capabilities for specific purposes. Understanding and controlling MAV is important for various applications, from surveillance and delivery services to disaster response and entertainment. In engineering education, providing a hardware platform for understanding MAV mechanism and the controller design is very challenging in terms of cost and safety issue. This paper presents a study on drone mechanisms and controller design using Webots, a versatile robot simulation environment. The primary objectives of this research were to simulate and analyze the behavior of drones in a line following task, employing PID controller as the control strategies, and to evaluate the suitability of Webots as a simulation tool for drone-related teaching and learning as well as research. In the methodology section, the setup of the Webots-based drone simulation, including the selection of a suitable drone model, sensor configurations, and the implementation of control algorithms will be explained. Line following task will be focused to analyze the drone mechanism and the controller design. Data on the drone performance will be collected through the experiment and will be analyzed rigorously. The findings reveal the effectiveness of Webots as a platform for simulating drone behavior. The advantages of using simulation tools like Webots, including cost-effectiveness, safety, and the ability to test and refine control algorithms before deployment on physical drones was proved in this study. It is shown that Webots can be leveraged to teach and train individuals in drone technology without the need for physical hardware.

High-speed polarization tracking using thin film lithium niobate integrated dynamic polarization controller.

Dynamic polarization controllers (DPCs) are essential devices in various optical applications. We develop a thin film lithium niobate (TFLN) integrated DPC driven by the real-time implemented Jacobian control algorithm for fast polarization tracking. Experimental results demonstrate a high polarization tracking speed of 100 krad/s when targeting a specific linear state of polarization, with a low control loop delay of 420 ns, half-wave control voltages of 2.75 V, and a fast polarization restoring time of 1.6 us. Compared to previously reported integrated DPCs, the TFLN-based DPC achieves significantly higher tracking speed and lower loop delay. The results highlight the effectiveness of the Jacobian method and the outstanding performance of TFLN-based DPCs. The study opens up possibilities for further advancements in DPC solutions using TFLN technology.

Non-linear Model-based Predictive Control for Trajectory Tracking and Control Effort Minimization in a Smartphone-based Quadrotor

In this paper, the design and implementation of a non-linear model-based predictive controller (NMPC) for predefined trajectory tracking and to minimize the control effort of a smartphone-based quadrotor is carried out. The optimal control actions are calculated in each iteration by means of an optimal control algorithm based on the non-linear model of the quadrotor, considering some aerodynamic effects. Control algorithm implementation and simulation tests are executed on a smartphone using the CasADi framework. In addition, a technique for estimating the energy consumed based on control signals is presented. NMPC controller performance is compared to an H-infinity controller and an LQI controller, using three predefined trajectories, where the NMPC average tracking error was around 50\% lower, and average estimated power and energy consumption slightly higher, with respect to the H-infinity and LQI controllers.

Energy and Cost Analysis of an Integrated Photovoltaic and Heat Pump Domestic System Considering Heating and Cooling Demands

The integration of photovoltaic panels and heat pumps in domestic environments is a topic that has been studied extensively. Due to their electrical nature and the presence of elements that add thermal inertia to the system (water tanks and the building itself), the functioning of compression heat pumps can be manipulated to try to fulfill a certain objective. In this paper, following a rule-based control concept that has been identified in commercial solutions and whose objective is to improve the self-consumption of the system by actively modulating the heat pump compressor, a parametric analysis is presented. By making use of a lab-tested model, the performance of the implemented control algorithm is analyzed. The main objective of this analysis is to identify and quantify the effects of the main parameters in the performance of the system, namely the climate (conditioning both heating and cooling demands), the photovoltaic installation size, the thermal insulation of the building and the control activation criteria. A total of 168 yearly simulations have been carried out. The results show that the average improvement in self-consumption is around 13%, while the cost is reduced by 2.5%. On the other hand, the heat from the heat pump and the power consumed increase by 3.7% and 5.2%, respectively. Finally, a linear equation to estimate the performance of the controller is proposed.

REALISATION OF MPC ALGORITHM FOR QUANSER QUBE-SERVO

This paper offers an in-depth look into the design and implementation of a Model Predictive Control (MPC) algorithm for the QUANSER QUBE-SERVO system. The QUBE-SERVO is a sophisticated laboratory experimental setup consisting of a servo motor, an encoder, and a rotary module. This combination provides a robust platform for investigating and testing various control strategies. In particular, the central focus of this study is the usage of the MPC algorithm for controlling the position of the QUBE-SERVO’s rotary disc load module. The MPC algorithm plays a pivotal role in this application by predicting the future behaviors of the system, and controlling the system by minimizing an objective function over a defined finite horizon. This makes it a versatile and effective tool for controlling complex systems. One of the key challenges in practical control applications is maintaining system stability in the presence of disturbances and uncertainties. To this end, we propose a MPC algorithm designed specifically to stabilize the QUBE-SERVO under such conditions. The functionality of this algorithm is not limited to the QUBE-SERVO system alone, and can be extended to other control systems exhibiting similar characteristics. The effectiveness of the proposed MPC algorithm is rigorously tested through simulation studies. These studies involve subjecting the QUBE-SERVO to various reference signals and disturbances. The results of the simulations provide strong evidence of the algorithm’s capability to effectively track reference signals, while also rejecting disturbances and uncertainties, thereby corroborating its efficacy for the QUBE-SERVO application. Moreover, the original MPC algorithm was enhanced to improve its performance for trajectory tracking tasks. We also discuss the integration of the MPC algorithm within the MatLAB and LabVIEW programming environments, which served as the base platforms for designing and running the simulations in this project. This paper, therefore, presents a comprehensive and practical approach for the successful implementation of the MPC algorithm in the QUANSER QUBE-SERVO system, and demonstrates its potential for wider application in similar control systems.

Attitude maneuvers avoiding forbidden directions

Many space missions require the execution of large-angle attitude slews during which stringent pointing constraints must be satisfied. For example, the pointing direction of a space telescope must be kept away from directions to bright objects, maintaining a prescribed safety margin. In this paper we propose an open-loop attitude control algorithm which determines a rest-to-rest maneuver between prescribed attitudes while ensuring that any of an arbitrary number of body-fixed directions of light-sensitive instruments stays clear of any of an arbitrary number of space-fixed directions. The approach is based on an application of a version of Pontryagin’s Maximum Principle tailor-made for optimal control problems on Lie groups, and the pointing constraints are ensured by a judicious choice of the cost functional. The existence of up to three first integrals of the resulting system equations is established, depending on the number of light-sensitive and forbidden directions. These first integrals can be exploited in the numerical implementation of the attitude control algorithm, as is shown in the case of one light-sensitive and several forbidden directions. The results of the test cases presented confirm the applicability of the proposed algorithm.

Design and Implementation of an Energy-Efficient Vehicle Platoon Control Algorithm Using Prescribed Performance and Extremum Seeking Control

Platooning has emerged as a promising approach to enhancing the fuel efficiency of vehicles, but determining the inter-vehicular distance that achieves the minimum consumption remains a challenge. In this article, an algorithm is proposed that employs extremum seeking control integrated with the prescribed performance control technique to find the optimal inter-vehicular distance. The algorithm utilizes the predecessor-following architecture to track the desired distance while minimizing the estimated aerodynamic drag coefficient to seek the optimal value. To estimate the coefficient, an observer is designed. Simulation results are presented to demonstrate the effectiveness of the approach. The proposed algorithm exhibits a significant improvement over existing methods that do not incorporate prescribed performance. Consequently, our scheme provides a valuable contribution to the field of platooning and paves the way for future research directions.

The Design and Processor-In-The-Loop Implementation of a Super-Twisting Control Algorithm Based on a Luenberger Observer for a Seamless Transition between Grid-Connected and Stand-Alone Modes in Microgrids

The abrupt transfer from grid-connected (GC) to stand-alone (SA) operation modes is one of the major issues that may threaten the stability of a distributed generation (DG) system. Furthermore, if the islanding mode happens, it is vital to take into consideration the load voltages or load current waveforms as soon as feasible. This paper develops an advanced control technique based on a super-twisting sliding mode controller (ST-SMC) for a three-phase inverter operating in both the GC and SA modes. This control scheme is proposed to ensure a smooth transition from the GC to SA mode and enhance the load voltage waveforms under the islanding mode. In addition, to minimize the operational costs of the system and the complexity of the studied model, a digital Luenberger observer (DLO) with a proper design is adopted for estimating the inverter-side current. The control scheme of the whole system switches between a current control mode during the GC mode and a voltage control mode during the SA mode. The super-twisting control algorithm is applied to the outer voltage control loop involved in the cascaded voltage/current control scheme in the SA mode. Simulation tests of a three-phase inverter are performed for the purpose of assessing the suggested control performance by using the PowerSim (PSIM) software and comparing it with a classical PI controller. Furthermore, a processor-in-the-loop (PIL) implementation in a DSP board TMS32F28335 while debugging is conducted using code composer studio 6.2.0. The obtained results show efficient control properties, such as a smooth transition among the microgrid (MG) operating modes, as well as effectiveness and robustness during both the GC and SA operation modes.