Testing Of Controller Research Articles

Hardware-in-the-loop controller testing and visualization of an industrial gantry

In factory automation applications, the embedded control system components are often available as off-the-shelf products, while the mechanical components and environment in which they will work are still under development. Thus, the need arises to test control systems without the final assembly. This paper introduces a hardware-in-the-loop (HIL) virtual environment for control system testing, design planning, and operator training. Controller hardware and software are configured as in the actual application, whereas electro-mechanical components are virtually constructed using Unreal Engine 5 and Matlab Simulink. Motion data from the model can be used to optimize the actuator controller, make physical design assessments, and predict possible failure scenarios such as software/hardware malfunctions. The environment is used to validate an industrial gantry robot’s control systems and structural design. The testing is successful in using real-time controller inputs to generate visual display and physical assessments of the robot’s design. Similar techniques can be applied to simulate different robot configurations using Codesys and Unreal Engine 5 or similar programs.

Modified and Improved TID Controller for Automatic Voltage Regulator Systems

This paper proposes a fractional order integral-derivative plus second-order derivative with low-pass filters and a tilt controller called IλDND2N2-T to improve the control performance of an automatic voltage regulator (AVR). In this study, equilibrium optimisation (EO), multiverse optimisation (MVO), and particle swarm optimisation (PSO) algorithms are used to optimise the parameters of the proposed controller and statistical tests are performed with the data obtained from the application of these three algorithms to the AVR problem. Afterwards, the performance of the IλDND2N2-T controller is demonstrated by comparing the transient responses with the results obtained in recently published papers. In addition, extra disturbances such as frequency deviation, load variation, and short circuit faults in the generator are applied to the AVR system. The proposed controller has outperformed the compared controller against these disturbances. Finally, a robustness test is performed in terms of deterioration in the system parameters. The results show that the IλDND2N2-T controller outperforms the compared controllers under all conditions and exhibits a robust effect on the perturbed system parameters.

Dyna-PINN: Physics-informed deep dyna-q reinforcement learning for intelligent control of building heating system in low-diversity training data regimes

This paper introduces Dyna-PINN, a novel physics-informed Deep Dyna-Q (DDQ) reinforcement learning (RL) approach, designed to address the data-intensive training requirements and model-agnostic nature of the conventional model-free RL methods. The DDQ approach blends model-based and model-free elements to enhance both learning and decision-making processes. By utilizing a physics-informed neural network (PINN) based model, our method enriches the learning process with physical information, enhancing the agent's planning capabilities and leading to faster learning compared to conventional model-free RL methods like Deep Q-Network (DQN) in scenarios with low-diversity training data availability. Our results demonstrate that Dyna-PINN has 50% greater sample efficiency than DQN and outperforms rule-based control in terms of thermal discomfort. Due to physics incorporation, the Dyna-PINN implements a more logical and interpretable control policy. It shows consistently good performance compared to all control variants across low-diversity data scenarios, i.e., 6 weeks of building data, and in higher-diversity data regimes, i.e., 6 months of building energy data, demonstrating the value of physics incorporation into the RL training. Additionally, we present two other DDQ-based techniques, RC-DDQ and NN-DDQ, exploring the synergy between neural networks and physical data in intelligent control designs for building energy systems. Rigorous controller testing is performed using the Building Optimization and Testing Framework (BOPTEST), a high-fidelity simulator that closely represents a real building's operation. Through comprehensive comparisons and realistic simulations, our study underscores the effectiveness of incorporating physics-informed approaches into RL-based control strategies, paving the way for more efficient and robust building energy management systems.

Research on grid-related performance laboratory testing for variable speed pumped storage unit

Abstract Variable speed pumped storage unit is an energy storage device with excellent operation and regulation performance. For the grid-related performance laboratory testing of the AC excitation controller of variable speed pumped storage unit integrated into the power grid, this paper studied the methods of grid-related testing of the AC excitation controller based on actual pumped storage power station data, established semi-physical hardware in the loop simulation platform based on RTDS and its function boards, and proposed the key laboratory testing items for the grid-related test. The simulation testing results verified the functions of the platform.

A Dynamic Virtual Inertia Controller Testing to Improve Load Frequency Efficacy of a Power System Penetrated with Renewable Energy Sources

A Dynamic Virtual Inertia Controller Testing to Improve Load Frequency Efficacy of a Power System Penetrated with Renewable Energy Sources

Analisis Pengaruh Temperatur Radiasi Matahari Terhadap Daya Luaran Solarcell 150 Wp Pematangsiantar

By conducting an analysis of the influence of solar radiation temperature on the output power of a 150 Wp Pematang Siantar solar cell whose energy source comes from renewable energy, which is the sun, it is hoped to help the community's maximum needs by obtaining it directly from solar energy, in addition to being cheap, the use of solar energy has never exhausted, the maintenance cost of Solarcell is also very reasonable and not least it does not produce pollution that can damage the environment. This research aims to find out the performance of a 150 Wp solar panel in producing electricity in the form of current, voltage and power to charge a battery with a capacity of 12 V 7 Ah. Research data collection was carried out at 08.00 WIB - 17.00 WIB. The test to determine the current, voltage and power in this study uses several components, namely a 150 Wp solar panel, solar charger controller, battery, voltmeter and ammeter Testing of a 150 Wp solar panel as an alternative energy source and data taken every hour for 9 hours. The magnitude of the current and voltage from the solar panel can be read directly. Each solar panel has a different level of efficiency. In general, solar panels on private residential solar systems have a standard temperature of 25°C (77°F), with a general temperature range between 15°C and 35°C. It is in this temperature range that solar panels perform best. The limit point for solar panel efficiency is 65°C (149°F). Above this temperature, the performance efficiency of the solar system can be disrupted due to the drop in voltage produced.

Fault Condition Testing of Photovoltaic Inverter Controller Based on Active Support Technology using Hardware-In-the-Loop in RTDS

Abstract Photovoltaic power generation is an important component of sustainable power generation and has been widely utilized. Grid connected inverters are key equipment in photovoltaic integrated systems, which directly affect the stability of the system. In this paper, the transient model of photovoltaic inverter controller based on active support technology is derived. The transient mathematical model and corresponding self-closing loop control strategy are analyzed. Based on the RTDS, a hardware controller semi-physical simulation platform is established to simulate and test photovoltaic inverters under different fault and control methods. Finally, the simulation data obtained from the self-closing loop control method and the hardware control method are compared to verify the stability and reliability of the photovoltaic inverter hardware controller.

A multifaceted suite of metrics for comparative myoelectric prosthesis controller research.

Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.

Experimental platform for studying energy regeneration in electric vehicle powertrains

Investigation into the energy consumption in electric vehicles (EVs) plays a pivotal role in determining their autonomy and assessing the electric system performance across diverse operational scenarios. This study focuses on the concept of energy regeneration, encompassing the recovery and storage of kinetic mechanical energy during braking or descent in EVs. Employing control systems in power electronics becomes necessary to establish a seamless workflow across operational quadrants to ensure efficient energy regeneration in an electric machine functioning as both a motor and a generator. To seamlessly integrate new technologies into practical applications, it is essential to conduct thorough evaluations in laboratories prior to deployment. This paper introduces an experimental platform specifically designed to analyze energy consumption and storage in EVs by emulating their powertrains in a controlled laboratory environment. The platform comprises key components for emulating the powertrain of a single-motor electric vehicle with single-axle traction, including a power converter configured in two quadrants, an energy storage system, a primary rotating electric machine, and a mechanically coupled point load torque (another motor). This paper provides a detailed guide on implementing such a laboratory and for facilitating the testing of diverse motor technologies and controllers under varied operational conditions. This comprehensive approach allows for the assessment of electromechanical system efficiency, focusing on both energy recovery and comprehensive control of electric power converters. Validation tests conducted under urban conditions and on steep terrains demonstrate the effectiveness of the platform in analyzing the energy efficiency of both the induction machine and the power controller.

A versatilely high fidelity electric machines emulator for rapid testing of motor controller.

Electric machines emulators (EMEs) based on hardware-in-the-loop (HIL), which effectively act as emulators to mimic the actual motor behavior of Interior Permanent Magnet (IPM) machines. EME is frequently used to evaluate motor controller and motor control methodologies prior to development. The inverse magnetization motor model, which is used as the basis for real-time simulation in this paper's proposal for an electric machine emulator system based on HIL, uses FEA to create the motor model data. The nonlinear features of the motor may be successfully replicated with this motor model, and the accuracy of the electric machine emulator can be enhanced by using a straightforward and trustworthy motor controller. The real-time simulation tool typhoon HIL is used in the study to develop a hardware-in-the-loop simulation platform for an IPM electric machines emulator.

High Specific-Impulse Electrospray Explorer Trajectory Guidance and Control Air Bearing Demonstration

As small satellite-mission concepts that require travel farther from their traditional domain of low-Earth orbit are being considered, there is a growing need to develop small-scale propulsion systems that can provide the necessary net impulse to enable deep-space operation. The High Specific-Impulse Electrospray Explorer for Deep-Space (HiSPEED) project is an example concept considered for this paper, as it would be a small satellite that uses electrospray thrusters to navigate along a low-thrust spiral trajectory from Earth to a near-Earth asteroid. The NASA Jet Propulsion Laboratory Small Satellite Dynamics Testbed operates simulation and air- bearing testing environments. This paper focuses on its planar air-bearing platform used to create a nearly frictionless environment in three degrees of freedom: two translational and one rotational. The platform maneuvers using eight compressed air thrusters controlled by an onboard single-board computer using position feedback from a motion-capture system. This paper discusses the design, development, and testing of a closed-loop controller for this platform that enables trajectory following with position error less than 4 cm. The paper presents data from tests representative of HiSPEED spiral escape trajectories and operations in proximity to an asteroid, along with implications for HiSPEED and similar concepts.

Hardware-in-the-loop dynamic load emulation of robotic systems actuated by fluidic artificial muscles

Hardware-in-the-loop (HIL) testing is a popular control system testing method because it bridges the gap between modeling/simulation and experiments. Instead of designing a full hardware-based experiment to validate the results of a simulation, the plant hardware can be replaced with an emulator device that responds to exogenous inputs and effectively emulates the dynamic behavior of a system. This approach can be more cost-effective and modular, since the emulated plant system can be modeled in a simulation environment, implemented on a simplified piece of hardware and changed quickly without having to fabricate new parts. This paper develops the hardware and control scheme for a certain type of HIL device called a dynamic load emulator that consists of a 1-DOF linear hydraulic dynamometer equipped with in-line sensing to measure both its own position and the force exerted on it by a device-under-test. This measured force is passed to a real-time model of the emulated dynamic system. The model outputs the emulated system position, and a closed-loop controller is used to emulate this position. The emulator controller incorporates both model-based feedforward and standard feedback PI control. This paper characterizes the dynamometer-based dynamic load emulator and its controller, determining its hardware limitations and validating its capabilities when experiencing a force input from a linear spring with known parameters. Additionally, this paper demonstrates the ability of the emulator to represent the dynamics of a 1-DOF robotic joint when actuated by a pair of fluidic artificial muscles (FAMs). The primary contribution of this work is to allow for more comprehensive testing of FAM configurations, topologies, and controllers for a wide range of parameters, because the same hardware can be used to emulate multiple systems. As a result, this work will lead to more cost-effective, time-efficient, and energy-efficient designs of robotic systems and the FAMs used to actuate them.

Development of a neural network model predictive controller for the fluidized bed biomass gasification process

In this study, an advanced controller for the fluidized bed biomass gasification (FBG) process is proposed. The controller is based on the model predictive control method, utilizing a long short-term memory neural network to accurately predict the controlled variable in the FBG process. Then, employing a gradient-based optimization algorithm, it optimizes the inputs to achieve the desired control objective. The controller specifically aims for temperature regulation in three spatial regions (fluidized bed, freeboard and outlet), using primary air, secondary air, and biomass flow rates as input variables. Open-loop simulations are used to fine-tune the controller's parameters (prediction and control horizon). For unbiased controller testing, a computational fluid dynamics (CFD) model of the FBG process is also developed, which is integrated with the controller in a closed-loop system, simulating real-process feedback for performance evaluation. The proposed controller effectively maintains temperatures (steady state error < 1.5 %) at desired set points (800–900 °C) with a short prediction horizon of 24 time steps, reducing response time to under 5 s, making it suitable for real-time FBG process control.

Development of PLC-Based Hardware Test-Bench Prototype for Solar-Wind-Battery-Based Microgrid System’s Control Algorithm Validation

Programmable Logic Controllers (PLCs) are gaining traction in microgrid operation due to their real-time deployment, reliable and robust operation, and fast response, even in harsh environments. Recognizing the critical need for robust and reconfigurable microgrid control and operation, this research proposes a hardware test-bench prototype using the Reconfigurable Allen-Bradley—Micro820/2080-LC20-20QBB PLC to demonstrate control algorithms aiming for efficient control, energy management, and resilient microgrid automation. The design and construction of a prototype controller test bench, along with the PLC configuration, wiring diagrams, and control logic, constitute the significant contributions of this research. The proposed controller test bench’s performance is evaluated through similar condition simulations in the presence of disturbances induced by the reduced contributions of renewable energy sources, such as solar and wind. The results illustrate the effectiveness of the developed control algorithm implemented in the proposed controller, ensuring the stable operation of the microgrid. Consequently, this work advances the simulation-based validation of microgrid control algorithms and provides insights into the practical application of a PLC-based hardware test bench under various environmental conditions.

Testing a Soil Moisture Sensor Based Irrigation Controller in both Laboratory and Field Conditions for Fargo Silty Clay Soils

Highlights A soil matric potential sensor-based irrigation system was tested in lab and field experiments. Soil matric potential and soil moisture levels for each threshold were determined for Fargo silty clay soil through lab experiment. Thresholds 5, 6, 7, and 8 at -35, -45, -55, and -70 kPa were tested through the field experiment for watermelon production. Abstract. Irrigation scheduling plays a pivotal role in water management. Conventional irrigation methods often struggle to maintain consistent moisture across fields due to uniform water application irrespective of field conditions and soil moisture levels. Automatic soil moisture sensors offer a solution by tailoring irrigation based on field conditions. These systems trigger irrigation when soil is too dry or skip it when moisture levels are sufficient. Hence, setting the right threshold value is key for sensor-based irrigation. This study involved laboratory testing of an irrigation controller, and determining soil matric potential and volumetric water content at various thresholds (1 to 9) for Fargo Silty Clay soil. Subsequently, a field experiment assessed its performance for a drip irrigation system at different thresholds (5, 6, 7, and 8) for watermelon cultivation, both with and without clear plastic mulch, in a randomized layout. Thresholds 5, 6, 7, and 8 (-35, -45, -55, and -70 kPa), along with no irrigation, were considered with one irrigation event daily. Results from laboratory and field tests underscore the controller’s efficacy in managing drip irrigation timing and duration. Notably, threshold 7 (-55 kPa) combined with clear plastic mulch emerged as the most optimal treatment, yielding the highest watermelon production of 129.6 tons/ha. Moreover, this approach delivered premium produce quality, but utilizing only 142.2 mm of irrigation water. This study emphasizes the role of precise irrigation scheduling. By integrating smart technology and thoughtful threshold setting, agricultural practices can be elevated, yielding abundant crops while conserving water and promoting sustainability. Keywords: Controller, Drip irrigation, Mulch, Sensor, Watermelon.

Design, realization and testing of a synthetic inertia controller for wind turbine power generators

The progressive inclusion of Renewable Energy Sources (RES) in power grids is causing a reduction of the overall system inertia, leading to a reduction of the system resiliency. A possible solution is equipping RES with synthetic inertia controllers so that additional power can be provided in case of severe frequency transients. This work reports the results of a research activity that aimed at improving the maturity of synthetic inertia controllers for wind turbine generators through the design and implementation of a controller prototype based on an advanced version of a synthetic inertia controller. Hardware-in-the-loop simulations are performed to validate the correct behavior of the prototype.

Nonlinear Controller for an Inverted Pendulum Using the Trigonometric Function

In this paper, a nonlinear controller (TR) for an inverted pendulum using the trigonometric function is presented. The TR controller is a new proposal, which is represented by a simple mathematical formula. TR operation does not require complex calculations, so it can be applied even to the simplest microcontrollers. Tuning the TR controller is very simple, and the range of stable operation is very wide. Simulation tests of the TR controller showed that the controller is effective even for deviations exceeding 50∘. The TR controller tests were compared to the results of a PID controller. The TR controller is designed to stabilise an inverted pendulum in the equilibrium point, a state in which the pendulum is in a upright position. Stabilisation for other deflection-angle set points was not taken into account. During the research, steps were taken to simulate phenomena characteristic of real solutions. An inertial block and a disturbance were introduced into the test system. Despite the introduced difficulties, the TR controller effectively stabilised the pendulum without the need to retune the controller settings. Consequently, the TR controller is an attractive alternative to previously applied solutions for the stabilisation of an inverted pendulum.

Augmented wheelchair control for collision avoidance

An electric wheelchair gives people with disabilities the ability to move independently and participate in social activities. However, the control of wheelchairs requires concentration, foresight, and spatial orientation, which often deteriorate with the progression of a disease and with age. The loss of these efficiency can be significantly compensated by the safety system. The research presented on augmentation in manual control of the wheelchair is carried out. Although most of the existing propositions focus on stopping before an obstacle, bypassing it, or automatic navigation to the indicated area, they do not resolve safe motion in tight spaces and areas with many obstacles. This article presents a new approach to collision controller for manual control. The proposed solution takes into account the situation in which the driver of the electric wheelchair uses it as a tool to move moveable objects in its surroundings. The developed and tested system provides safe and controlled contact with the obstacle. As a result, the intent to move resulting from joystick deflection causes a controlled low-speed motion of the object. This article contains descriptions of the control method and the research stand with the wheelchair, simulation results and experimental studies. The simulation model was used to analyse dynamics of the controlled object, select collision controller parameters and test controller operation. The results of wheelchair tests and simulations are similar and consistent with the assumed mode of operation, confirming correctness of the proposed control method.

SIL Setup for Motor Controller Testing in Virtual Vehicle Environments

This research paper explores the implementation and benefits of Software-in-the-Loop (SIL) setups for testing motor controllers within virtual vehicle environments. SIL testing offers significant advantages in terms of cost, efficiency, and safety compared to traditional hardware-based testing methods. This paper discusses the key components of SIL setups, their implementation strategies, and case studies demonstrating their effectiveness in real-world applications. Additionally, challenges and future research directions in SIL testing for motor controllers are also addressed.

Optimal rule based fuzzy-PI controller for core power control of nuclear reactor

In this paper, an optimal rule based fuzzy proportional integral controller is proposed to control the core power of the molten salt breeder reactor (MSBR) model. Firstly, a nonlinear MSBR model is considered. It is then linearized using the perturbation linearizing approach, and the transfer function of the linearized MSBR system is established. Secondly, an optimal Fuzzy-PI control structure is formulated where a PI controller is set as the principal controller for the MSBR model and a fuzzy control system is used to adjust the PI controller parameters during any disturbance in the MSBR. The rule base system of the fuzzy controller is optimized with a reshaped class topper optimization algorithm. Finally, the optimized Fuzzy-PI controller tests under a variety of conditions. Furthermore, the Nyquist stability approach is used to analyze the stability of the closed loop MSBR model.