Rapid Control Prototyping Research Articles

Research on online optimization scheme and deployment of PMSM control parameters based on honey badger algorithm

Permanent magnet synchronous motor (PMSM) drive systems are receiving increasing attention due to their superior control quality and efficiency. Optimizing the control parameters are key to achieve the high-performance operation of PMSMs. Although heuristic algorithms demonstrate excellent optimization outcomes in simulations, there are still challenges in deploying optimization schemes in practical drives. In this study, a real-time online deployable control parameter optimization scheme is proposed. The optimization effect is evaluated through the system step response performance, and a framework for deploying optimization algorithms within the driver is developed. A fault suppression mechanism is also designed to mitigate overshoot and vibration issues caused by suboptimal solutions. The proposed scheme is validated on a rapid prototyping control platform. Experimental results confirm that the scheme exhibits good optimization performances across various operating conditions. The honey badger algorithm employed in this paper shows faster convergence and more stable optimization effects than other optimization algorithms. The optimization effect is improved by 2.2% and its performance in terms of consistency across multiple optimization results has increased by 40%.

Comprehensive Analysis of Improved Hunter–Prey Algorithms in MPPT for Photovoltaic Systems Under Complex Localized Shading Conditions

The Hunter–Prey Optimization (HPO) algorithm represents a novel population-based optimization approach renowned for its efficacy in addressing intricate problems and optimization challenges. Photovoltaic (PV) systems, characterized by multi-peaked shading conditions, often pose a challenge to conventional maximum power point tracking (MPPT) techniques in accurately identifying the global maximum power point. In this research, an MPPT control strategy grounded in an improved Hunter–Prey Optimization (IHPO) algorithm is proposed. Eight distinct shading scenarios are meticulously crafted to assess the feasibility and effectiveness of the proposed MPPT method in capturing the maximum power point. A performance evaluation is conducted utilizing both MATLAB/simulation and an embedded system, alongside a comparative analysis with alternative power tracking methodologies, considering the diverse climatic conditions across different seasons. The simulation outcomes demonstrate the capability of the proposed control strategy in accurately tracking the global maximum power point, achieving a commendable efficiency of 100% across seven shading conditions, with a tracking response time of approximately 0.2 s. Verification results obtained from the experimental platform illustrate a tracking efficiency of 98.75% for the proposed method. Finally, the IHPO method’s output performance is evaluated on the StarSim Rapid Control Prototyping (RCP) platform, indicating a substantial enhancement in the tracking efficiency of the photovoltaic system while maintaining rapid response times.

Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.

Novel rapid control prototyping for permanent magnet synchronous motor via model-based design and STM32 chip

Novel rapid control prototyping for permanent magnet synchronous motor via model-based design and STM32 chip

Hardware Implementation of Fully Controlled Bridge Rectifier with Rapid Control Prototyping Approach

In industrial applications, rapid prototyping of digital controls is important in terms of time, cost, and getting easier design steps. Especially the complexity of different power electronic converter circuits and their necessity of providing various operating conditions make digital control inevitable. However, developing a digital control system has many unknown details. In-the-loop simulation techniques evolved to simplify this stage during the last few decades. In this paper, a fully controlled bridge rectifier is designed and implemented by using a rapid control prototyping approach; its steps are accelerated with processor-in-the-loop and hardware-in-the-loop tests. Launchpad F28379D from Texas Instruments is used as an interface between the designed rectifier hardware circuit and MATLAB/Simulink Embedded Coder platform. Additionally, a driver control board is developed to provide switching signals and analog to digital measurements. The performance of the system is experimentally tested on a 500W rectifier prototype with a closed loop PI controller for voltage regulation at different parametric variations.

Assessment of sealing systems impact on the vibration and environmental safety of rotary machines

Energy saving control algorithms of centrifugal fans/pumps are based on the use of the frequency-controlled induction motor drives and pressure or flow rate sensors, the costs of which are comparable to the cost of the fans/pumps for low-power applications. The paper develops a new and simple estimation approach of the pressure and flow rate, utilising the measured Root Mean Square (RMS) value of the stator current, estimated motor’s input active power, reference stator voltage frequency and feed-forward backpropagation artificial neural network. The error percentage for both flow rate and pressure in experimental and estimated data is within the range of ±5%, which conforms to the ISO 13348 standard. A test rig for the rapid control prototyping of the fan is designed, and necessary design and test procedures are developed. The estimation approach is verified experimentally and demonstrates better estimation accuracy compared to the existing and possible similar simple approaches. The developed algorithm can be easily embedded into the industrial variable frequency drives without any hardware changes.

Improved Flow Rate and Pressure ANN Estimators for a Centrifugal Fan with Induction Motor Drive

Energy saving control algorithms of centrifugal fans/pumps are based on the use of the frequency-controlled induction motor drives and pressure or flow rate sensors, the costs of which are comparable to the cost of the fans/pumps for low-power applications. The paper develops a new and simple estimation approach of the pressure and flow rate, utilising the measured Root Mean Square (RMS) value of the stator current, estimated motor’s input active power, reference stator voltage frequency and feed-forward backpropagation artificial neural network. The error percentage for both flow rate and pressure in experimental and estimated data is within the range of ±5%, which conforms to the ISO 13348 standard. A test rig for the rapid control prototyping of the fan is designed, and necessary design and test procedures are developed. The estimation approach is verified experimentally and demonstrates better estimation accuracy compared to the existing and possible similar simple approaches. The developed algorithm can be easily embedded into the industrial variable frequency drives without any hardware changes.

Robust controller design and experimental validation based on bounded uncertainty for collaborative industrial robots.

In this paper, derived from the proportional-derivative control and robust control, a novel practical robust control method based on a dynamic feedforward model is established by taking six-axis motion cooperative industrial robots as the research object. The nonlinear friction, parameter uncertainty, and external disturbance are taken into consideration while establishing the dynamic model of the cooperative industrial robot. The method includes a proportional-derivative control and a robust control. Lyapunov theory is used to analyze the proposed controller, and it is shown that this method can guarantee uniformly bounded and uniformly final bounded systems. Simulation and experiment results show that the proposed controller is better than proportional-integral-derivative control and mode-based proportional-derivative control in stability tracking performance and robustness. In addition, the CSPACE platform for rapid controller prototyping may reduce the arduous programming effort and offer a lot of ease for the trials.

Design and Experiment of Ordinary Tea Profiling Harvesting Device Based on Light Detection and Ranging Perception

Due to the complex shape of the tea tree canopy and the large undulation of a tea garden terrain, the quality of fresh tea leaves harvested by existing tea harvesting machines is poor. This study proposed a tea canopy surface profiling method based on 2D LiDAR perception and investigated the extraction and fitting methods of canopy point clouds. Meanwhile, a tea profiling harvester prototype was developed and field tests were conducted. The tea profiling harvesting device adopted a scheme of sectional arrangement of multiple groups of profiling tea harvesting units, and each unit sensed the height information of its own bottom canopy area through 2D LiDAR. A cross-platform communication network was established, enabling point cloud fitting of tea plant surfaces and accurate estimation of cutter profiling height through the RANSAC algorithm. Additionally, a sensing control system with multiple execution units was developed using rapid control prototype technology. The results of field tests showed that the bud leaf integrity rate was 84.64%, the impurity rate was 5.94%, the missing collection rate was 0.30%, and the missing harvesting rate was 0.68%. Furthermore, 89.57% of the harvested tea could be processed into commercial tea, with 88.34% consisting of young tea shoots with one bud and three leaves or fewer. All of these results demonstrated that the proposed device effectively meets the technical standards for machine-harvested tea and the requirements of standard tea processing techniques. Moreover, compared to other commercial tea harvesters, the proposed tea profiling harvesting device demonstrated improved performance in harvesting fresh tea leaves.

LExCI: A framework for reinforcement learning with embedded systems

Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system.

Development of the two-stage SCR control strategy to satisfy ultra-low NOX emission regulation for heavy-duty diesel engine

The emission regulations for heavy-duty diesel engines regarding nitrogen oxide (NOX) are becoming increasingly stringent, particularly in relation to cold start cycles. While the two-stage selective catalytic reduction (SCR) has the potential to achieve ultra-low NOX emissions, several challenges remain, including the accurate prediction of ammonia (NH3) storage mass and the co-control of the two-stage SCR. The first step in this study involved the establishment of a rapid control prototype platform to facilitate the development and validation of a two-stage SCR control strategy. Secondly, an initial method for predicting the NH3 storage based on the mass conservation law was proposed, which was subsequently improved by filling and emptying experiments. The third step involved the development of a two-stage SCR co-control strategy, including obtaining the steady-state NH3 storage target value, dynamic correction for NH3 storage target value, regulation of NH3 storage, and control of the close-coupled SCR urea injector state. Finally, the two-stage SCR urea injection control strategy was certified under the world harmonized transient cycle (WHTC). The results demonstrate that the composite value of engine outlet NOX emissions under cold and hot start WHTC cycles is 13 g/(kW·h). Meanwhile, the composite value of tailpipe NOX emissions under cold and hot start WHTC cycles is 0.065 g/(kW·h), representing only 14% of the EU VI limit value of 0.46 g/(kW·h). Thus, the findings demonstrate that integrating an accurate NH3 storage prediction method with the two-stage SCR co-control function is crucial for heavy-duty diesel engines to achieve ultra-low NOX emissions.

The electro-hydraulic mix-drive cantilever arm platform (EMCAP) for general technology research in remote handling for Tokamak

The EMCAP (electro-hydraulic mix-drive cantilever arm platform) is built to study general functions required by the maintenance of tokamak and cultivate remote handling engineers. This paper aims to give an overview of this robotic platform for general technology research. The platform consists of radiation tolerant materials and joints, a long cantilever arm with redundant degrees of freedom, a rapid control prototype, a vacuum vessel model, a bolts driving tool, cutting tools, and a virtual reality software. The design of this platform is partly presented: 10 degrees of freedom are demonstrated; the length of articulated arm reaches 3.15 m; the load capacity of terminal is 10 kg; the radiation tolerance of a test arm with electro-hydraulic joints reaches 4MGy in gamma ray. The motion planning is tested in a vacuum vessel model. The primary process is also studied by the end effectors for functional researches. Screwing and unscrewing bolts are successfully realized without connecting to cantilever arm, but pipe cutting from outside space still faces some force issues, and the laser cutting tool for inner pipe working space brings about waste contamination problems, which requires improvement. This remote handling platform is the first visible success for SWIP in developing unmanned maintenance for the fusion devices.

Fractional-Order Least-Mean-Square-Based Active Control for an Electro–Hydraulic Composite Engine Mounts

For the vibration of automobile powertrain, this paper designs electro–hydraulic composite engine mounts. Subsequently, the dynamic characteristics of the hydraulic mount and the electromagnetic actuator were analyzed and experimentally studied separately. Due to the strong nonlinearity of the hybrid electromechanical engine mount, a Fractional-Order Least-Mean-Square (FGO-LMS) algorithm was proposed to model its secondary path identification. To validate the vibration reduction effect, a rapid control prototype test platform was established, and vibration active control experiments were conducted based on the Multiple–Input Multiple–Output Filter-x Least-Mean-Square (MIMO-FxLMS) algorithm. The results indicate that, under various operating conditions, the vibration transmitted to the chassis from the powertrain was significantly suppressed.

Supporting Efficient, Standards-compliant Development of Embedded Software with Rapid Control Prototyping

Supporting Efficient, Standards-compliant Development of Embedded Software with Rapid Control Prototyping

Experimental study on the double-skyhook controls of semi-active suspension with variable inertance and damping

As a conventional control approach, the skyhook damping control algorithm can respond to changes in road circumstances but not to those in load circumstances. The skyhook inertance control algorithm, a newly developed control approach, may adapt to changes in load circumstances rather than in road circumstances. The recent introduction of double-skyhook control algorithms has allowed vehicle suspension systems to accomplish both load and road adaptation. This paper presents an experimental investigation contrasting the performance of the double-skyhook configuration to that of the traditional skyhook damper configuration and the skyhook inerter configuration. By integrating the double-skyhook control strategy with semi-active devices with variable inertance and damping, the paper proposes two strategies for coordinating skyhook inertance and skyhook damping control. A controller using the double-skyhook control strategy is designed to employ the Development to Production rapid control prototyping platform. The controller mode is adjusted using MotoTune software to implement control methods. Three tests were conducted under various road conditions, load conditions, and impact conditions for semi-active suspension. Test results indicate that the double-skyhook configuration considerably enhances driving comfort compared to the single-skyhook configuration. Moreover, the greater the deterioration of road conditions and the decrease in load, the more pronounced the enhancement.

A Rapid Control Prototyping and Hardware-in-the Loop Approach for Upper Limb Robotic Exoskeletons Control

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

Distributed Dynamic Surface Control for a Class of Quadrotor UAVs with Input Saturation and External Disturbance

An adaptive dynamic surface trajectory tracking control method based on the Nussbaum function is proposed for a class of quadrotor UAVs encountering unknown external disturbances and unidentified nonlinearities. By transforming controller expressions into numerical solutions, the challenge of overly complex controller design expressions is addressed, simplifying the overall controller design process and enhancing the efficiency of simulation programs. Additionally, an adaptive controller based on Nussbaum gain is introduced to effectively resolve actuator saturation issues. This approach mitigates complexities associated with traditional control design and ensures smooth operation of the quadrotor UAVs. The proposed methodology offers promising prospects for enhancing the robustness and performance of quadrotor UAVs under uncertain operating conditions. Finally, to validate the effectiveness of the proposed control scheme, a hardware-in-the-loop experimental setup is constructed. The dynamic model of the quadrotor UAVs and the proposed controller scheme are implemented on the Rapid Control Prototype (RCP) and Real-Time Simulator (RTS), respectively. This facilitates a semi-physical simulation experiment, providing a basis for the subsequent application of the control scheme to actual aerial vehicles. The concluding experimental results affirm the effectiveness of the proposed control scheme and highlight its potential for practical applications.

An Integrated Control Framework for Torque Vectoring and Active Suspension System

Four-wheel independently driven electric vehicles (FWID-EV) endow a flexible and scalable control framework to improve vehicle performance. This paper integrates the torque vectoring and active suspension system (ASS) to enhance the vehicle’s longitudinal and vertical motion control performance. While the nonlinear characteristic of the tire model leads to a relatively heavier computational burden. To facilitate the controller design and ease the load, a half-vehicle dynamics system is built and simplified to the linear-time-varying (LTV) model. Then a model predictive controller is developed by formulating the objective function by comprehensively considering the safety, energy-saving and comfort requirements. The in-wheel motor efficiency and the power loss of tire slip are treated as optimization indices in this work to reduce energy consumption. Finally, the effectiveness of the proposed controller is verified through the rapid-control-prototype (RCP) test. The results demonstrate the enhancement of the energy-saving as well as comfort on the basis of vehicle stability.

Real-Time Implementation for the Robust Controller of the Switched Boost Inverter within an Autonomous Modified Nanogrid

SBI stands for switched-boost inverter and is a single-stage power converter used to connect low-power sources, such as solar photovoltaic (PV) arrays within a modified nanogrid, to loads. Despite its many benefits, such as the fact that it does not require expensive sophisticated dead-time circuitry, its duty ratio is limited to between 0 and 0.5. As a result, this publication is a contribution towards improving the SBI's performance for use in a modified nanogrid. Furthermore, it also presents a revolutionary strategy for a simple closed-loop control technique that overcomes the majority of inverter shortcomings and system nonlinearity. The suggested robust controller uses a metaheuristic optimization technique, called gorilla troops optimization (GTO), to optimize the performance of this controller. The robustness of the suggested controller is tested against the sudden change of the nanogrid input voltage (i.e., PV-voltage), modulation index of the inverter, and the step-change in DC-link output voltage of the inverter. The test results obtained from Matlab/Simulink software are compared with particle swarm optimization (PSO) – as another optimization algorithm. Furthermore, the proposed system is experimentally implemented with OPAL RT-4510v real-time hardware in the loop (HIL), rapid control prototyping, OP-8660 HIL controller and data acquisition platform.

A 2-DOF drag-free control ground simulation system based on Stewart platform

Space-based gravitational wave detection missions use multiple satellites to form a very large scale Michelson laser interferometer in space. This requires extremely high precision displacement measurements at the picometer level between test masses even millions of kilometers apart. Drag-free control is a key technology to ensure the ultra-static and ultra-stable space experiment platform for space-based gravitational wave detection. This paper proposes an innovative ground simulation scheme for drag-free control principle based on the Stewart platform. The kinematics and dynamics modeling of the Stewart platform used in the experiment is presented. A drag-free ground simulation experimental equipment is designed and built. A two-degree-of-freedom (2-DOF) drag-free controller is designed based on the H∞ loop shaping algorithm which outperforms a PID controller in Simulink simulation. A semi-physical simulation experiment is conducted to verify the controller designed using rapid control prototyping technology. The experimental results show that the control performance reaches the limit accuracy of the hardware device, thus verifying the effectiveness of the drag-free control algorithm.