Low-level Control Research Articles

The intensity of oxidative stress and systemic inflammation in patients with a combination of bronchial asthma and chronic obstructive pulmonary disease

Background. Chronic obstructive pulmonary disease and bronchial asthma, when combined in one patient, are characterized by a low level of control. Excess weight aggravates the course of obstructive diseases. The study of the features of this syntropy will improve the effectiveness of therapeutic measures.
 Aim. The study of the level of cytokines and carbonylated proteins in patients with a combination of bronchial asthma and chronic obstructive pulmonary disease with overweight and normal weight during an exacerbation.
 Material and methods. The study included 136 people: the first group a combination of bronchial asthma and chronic obstructive pulmonary disease (n=30), the second bronchial asthma (n=36), the third chronic obstructive pulmonary disease (n=29), the fourth volunteers without respiratory diseases (n=41). Each group was divided into two subgroups depending on the body mass index (less than 25 kg/m2 or 25 kg/m2 and more). The concentrations of interleukins-6 and -8, tumor necrosis factor in blood plasma were determined by enzyme immunoassay. The level of carbonylated plasma proteins was assessed spectrophotometrically. Statistical processing was performed in the Statistica 10.0 program using nonparametric criteria. The correlation of the studied parameters was assessed using the Spearman coefficient.
 Results. In patients with a combination of bronchial asthma and chronic obstructive pulmonary disease, statistically significant positive correlations between the levels of interleukins-6 and -8, tumor necrosis factor and carbonylated proteins were found 0.51, 0.59 and 0.55, respectively (p 0.05). Patients of the first group with overweight differed by 37.5% in higher levels of interleukin-6 compared with patients with body mass index 25 kg/m2.
 Conclusion. The intensity of systemic inflammation in patients with a combination of bronchial asthma and chronic obstructive pulmonary disease during exacerbation correlates with the intensity of oxidative damage.

Energy Management System (EMS) Based on Model Predictive Control (MPC) for an Isolated DC Microgrid

Microgrids have become an alternative for integrating distributed generation to supply energy to isolated communities, so their control and optimal management are important. This research designs and simulates the three levels of control of a DC microgrid operating in isolated mode and proposes an Energy Management System (EMS) based on Model Predictive Control (MPC), with real-time measurement feedback for optimal energy dispatch, which ensures power flow distribution and operation at minimum cost while extending the lifespan of the BESS. The EMS can react to disturbances produced in the lower control levels. The microgrid’s performance is analyzed and compared in two scenarios without EMS, and with EMS against changes in irradiation and changes in electricity demand. The fulfillment of the power balance is evaluated by analyzing the power delivered by each generation unit, the operating cost, and the state of charge of the battery (SOC).

µRT: A lightweight real-time middleware with integrated validation of timing constraints.

Middlewares are standard tools for modern software development in many areas, especially in robotics. Although such have become common for high-level applications, there is little support for real-time systems and low-level control. Therefore, µRT provides a lightweight solution for resource-constrained embedded systems, such as microcontrollers. It features publish-subscribe communication and remote procedure calls (RPCs) and can validate timing constraints at runtime. In contrast to other middlewares, µRT does not rely on specific transports for communication but can be used with any technology. Empirical results prove the small memory footprint, consistent temporal behavior, and predominantly linear scaling. The usability of µRT was found to be competitive with state-of-the-art solutions by means of a study.

Improving Mobile Robot Maneuver Performance Using Fractional-Order Controller.

In this paper, the low-level velocity controller of an autonomous vehicle is studied. The performance of the traditional controller used in this kind of system, a PID, is analyzed. This kind of controller cannot follow ramp references without error, so when the reference implies a change in the speed, the vehicle cannot follow the proposed reference, and there is a significant difference between the actual and desired vehicle behaviors. A fractional controller is proposed which changes the ordinary dynamics allowing faster responses for small times, at the cost of slower responses for large times. The idea is to take advantage of this fact to follow fast setpoint changes with a smaller error than that obtained with a classic non-fractional PI controller. Using this controller, the vehicle can follow variable speed references with zero stationary error, significantly reducing the difference between reference and actual vehicle behavior. The paper presents the fractional controller, studies its stability in function of the fractional parameters, designs the controller, and tests its stability. The designed controller is tested on a real prototype, and its behavior is compared to a standard PID controller. The designed fractional PID controller overcomes the results of the standard PID controller.

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots

The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel learning-based controller that intuitively uses onset motion to predict the desired end-point position for an assistive robot. A multi-modal sensing system comprising inertial measurement units (IMUs), electromyographic (EMG) sensors, and mechanomyography (MMG) sensors was implemented. This system was used to acquire kinematic and physiological signals during reaching and placing tasks performed by five healthy subjects. The onset motion data of each motion trial were extracted to input into traditional regression models and deep learning models for training and testing. The models can predict the position of the hand in planar space, which is the reference position for low-level position controllers. The results show that using IMU sensor with the proposed prediction model is sufficient for motion intention detection, which can provide almost the same prediction performance compared with adding EMG or MMG. Additionally, recurrent neural network (RNN)-based models can predict target positions over a short onset time window for reaching motions and are suitable for predicting targets over a longer horizon for placing tasks. This study's detailed analysis can improve the usability of the assistive/rehabilitation robots.

Encouraging Trust in Demand-Side Management via Interaction Design: An Automation Level Based Trust Framework

The energy transition requires increased end-user flexibility in consumption as a response to the more volatile production patterns of renewable resources. Automated demand-side management solutions can provide support in achieving this but struggle with trust and acceptance issues from end-users. This paper contributes insights into how communicating trustworthiness can be achieved through careful and context-aware interaction design in such systems. Core interface features such as feedback, automation transparency, control options, benefit information, and actionable information were identified and fifteen case studies from six countries were analysed with regard to provided interaction features, automation level, and end-user experiences. The results provide insights into the form and frequency of these features used at different automation levels and their varying role in trust and acceptance building. Based on our results, we recommend particular attention to providing actionable information and actively reaching out to users at a low automation level, to provision and communication of control at a medium automation level, and to providing transparency at a high automation level in order to promote trust successfully, while benefit information is central for a “social license to automate” independently of the automation level.

IoT-Enabled Motion Control: Architectural Design Challenges and Solutions

Ever-increasing demands for highly-efficient customized manufacturing are driving the development of Industry 4.0. Reconfigurable manufacturing systems based on modular, convertible, and interoperable equipment present a key enabler of the fourth industrial revolution. Besides suitable mechanical design, control of these smart manufacturing resources should facilitate Internet of Things (IoT) integration and reconfigurability. Existing numerical control kernels (NCK)—the major control component for motion control—hinder rapid reconfiguration due to the complexity of their monolithic centralized structure. On the other hand, reconfigurability is naturally promoted by the distributed control paradigm, as proposed by the industrial IoT (IIoT) concept; hence, in this article, we investigate design challenges in distributing the conventional centralized NCK designs used for control of computerized numerical control systems. We introduce an architecture where each axis module is augmented with a networked, IIoT-enabled low-level controller (LLC) that performs local control and exposes a network interface for communication with other LLCs toward executing the desired process. These smart manufacturing resources communicate with an edge-based high-level controller (HLC) that provides the trajectory specification over the network and schedules manufacturing tasks. We investigate real-time and network bandwidth requirements of different mappings of the NCK layers to the LLCs and the HLC, providing design-time tradeoffs for implementing IoT-ready, distributed motion control. We demonstrate feasibility of our approach using industry-grade single-axis robots and low-cost IoT microcontrollers, and show that minimal accuracy impairment is introduced compared to the centralized setup based on ISO 230 and ISO 10791-7 standards.

Safety-Critical and Flexible Cooperative On-Ramp Merging Control of Connected and Automated Vehicles in Mixed Traffic

Cooperative on-ramp merging control for connected and automated vehicles (CAVs) can effectively improve traffic throughput and vehicle fuel efficiency at highway on-ramp merging bottlenecks. However, in the mixed traffic scenario where CAVs and human-driven vehicles (HDVs) coexist, the uncertain maneuvers of human drivers pose a major challenge to merging control in terms of safety and flexibility. To this end, this paper proposes a hierarchical cooperative on-ramp merging control strategy for CAVs to optimize flexible trajectories with safety guarantees in mixed traffic. First, the on-ramp merging control problem for CAVs is considered in the case of a three-vehicle coordination, resulting in an optimal control problem (OCP) coordinating on-ramp and main-lane CAVs for efficient operation while satisfying multiple safety-critical constraints. Second, a two-level hierarchical control architecture is developed to solve the OCP with mixed state-control constraints. The upper-level planner solves an unconstrained OCP with Pontryagin’s Minimum Principle to calculate an expected merging position, which is embedded in the variable time headway of safe merging constraints in the lower-level controller. Then, the controller converts the nonlinear OCP with safety-critical constraints to a quadratic programing (QP) problem by exploiting Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). By solving the QP efficiently, the time and energy efficient trajectory for each CAV is obtained. In addition, a receding horizon control framework is employed, which enables CAVs to determine flexible merging opportunity and tackle the disturbances caused by HDVs. Finally, comprehensive simulation results show that the proposed cooperative on-ramp merging strategy has potential in enabling merging flexibility, improving traffic efficiency and energy economy in real time.

Model-based and machine learning-based high-level controller for autonomous vehicle navigation: lane centering and obstacles avoidance

<p>Researchers have been attempting to make the car drive autonomously. The environment perception together with safe guidance and control is an important task and are one of the big challenges when developing this kind of system. Geometrical or physical based models, machine learning based models and those based on a mixture of both models, are the three types of navigation methods used to resolve this problem. The last method takes advantage of the learning capability of machine learning models and uses the safeness of geometric models in order to better perform the navigation task. This paper presents a hybrid autonomous navigation methodology, which takes advantage of the learning capability of machine learning and uses the safeness of the dynamic window approach geometric method. Using a single camera and a 2D lidar sensor, this method actuates as a high-level controller, where optimal vehicle velocities are found, then applied by a low-level controller. The final algorithm is validated on CARLA Simulator environment, where the system proved to be capable to guide the vehicle in order to achieve the following tasks: lane keeping and obstacle avoidance.</p>

Characteristics of Multi-Stage Auxiliary Equipment Linkage in Pumped Storage Power Station Under Double Carbon Target

The auxiliary control linkage system of the pumped storage power station has become a necessary technical means for the safe operation and production of the station. However, due to the scattered linkage control function and low control level of each subsystem of the pumped storage power station, the security system cannot play a full role, affecting the safety portion and other effects. This paper studies a multistage auxiliary equipment linkage method based on a linkage body for the pumped storage power station. In the framework of the system dividing safety intervals, five linkage bodies are constructed for multi-layer hierarchical linkage, including real-time monitoring, fire fighting, five-prevention locking, online monitoring, and video monitoring. Completing the development of the linkage method of auxiliary equipment comprehensive control based on a multi-level linkage strategy and implementing multi-stage auxiliary equipment linkage in the station can reduce the loss of auxiliary equipment in pumped storage stations and improve the carbon neutrality and carbon compliance benefit of the pumped storage stations.

A Hybrid Systems-Based Hierarchical Control Architecture for Heterogeneous Field Robot Teams.

Field robot systems have recently been applied in a wide range of research fields. Further automation, development, and activation of such systems require cooperation among heterogeneous robots. Classical control theory is inefficient in managing large-scale complex dynamic systems. Therefore, a discrete-event system based on the supervisory control theory must be introduced to overcome this limitation. In this article, we propose a hybrid system-based hierarchical control architecture using a supervisory control-based high-level controller and a traditional control-based low-level controller. The hybrid system and its dynamics are modeled through a formal method, called hybrid automata, and the behavior specifications are designed to express the control objectives for cooperation. In addition, modular supervisors that are more scalable and maintainable than a centralized supervisory controller were synthesized. The proposed hybrid system and hierarchical control architecture were implemented, validated, and evaluated for performance through a physics-based simulator and field tests. The experimental results confirmed that the robot team satisfied the given specifications and presented systematic results, validating the efficiency of the proposed control architecture.

Induced Pre-Saturation Tower: A Technological Innovation for Oily Water Treatment in Semi-Industrial Scale

In this work, an induced pre-saturation tower (IPST) for oil–water separation was built on a semi-industrial scale, based on experimental results obtained on a laboratory scale prototype. The main strategy for generating these criteria was to increase the efficiency of the bench scale prototype, which is limited by conditions of low levels of automation and control, with the use of a biosurfactant as an auxiliary collector. The validation of the developed criteria allowed the construction of an IPST with three stages, all fed with previously saturated effluents. The IPST was built in stainless steel, with multistage centrifugal pumps and adapted to generate microbubbles without the use of saturation tanks or compressors. The most relevant operational parameters were selected using a fractional factorial design, while a central composite rotatable design (CCRD) followed by the application of the desirability function allowed to optimize the conditions for partial and global variables, the latter with desirability of 95%. A nominal flow rate of approximately 1000 L·h−1, a recycle flow rate of 450 L·h−1, a scraper rotation speed of 80 rpm, an average pressure of the microbubble pumps of 11 bar, and an effluent temperature from IPST of about 38 °C ensured optimized operation for the proposed technological development.

Coordinated multi-robot trajectory tracking control over sampled communication

In this paper, we propose an inverse-kinematics controller for a class of multi-robot systems in the scenario of sampled communication. The goal is to make a group of robots perform trajectory tracking in a coordinated way when the sampling time of communications is much larger than the sampling time of low-level controllers, disrupting theoretical convergence guarantees of standard control design in continuous time. Given a desired trajectory in configuration space which is pre-computed offline, the proposed controller receives configuration measurements, possibly via wireless, to re-compute velocity references for the robots, which are tracked by a low-level controller. We propose joint design of a sampled proportional feedback plus a novel continuous-time feedforward that linearizes the dynamics around the reference trajectory: this method is amenable to distributed communication implementation where only one broadcast transmission is needed per sample. Also, we provide closed-form expressions for instability and stability regions and convergence rate in terms of proportional gain k and sampling period T. We test the proposed control strategy via numerical simulations in the scenario of cooperative aerial manipulation of a cable-suspended load using a realistic simulator (Fly-Crane). Finally, we compare our proposed controller with centralized approaches that adapt the feedback gain online through smart heuristics, and show that it achieves comparable performance.

Disturbance observer-based adaptive neural guidance and control of an aircraft using composite learning

An adaptive neural guidance and control system is proposed in this paper for a generic fixed-wing aerial robot. Unlike most of the existing low-level control systems, which utilize a non-adaptive guidance loop, in this work both the guidance and control loops are trained using an efficient adaptive neural algorithm. A feedforward neural network is employed in each loop to identify uncertain dynamics, while an adaptive disturbance observer allows to compensate for both the external disturbances and estimation error of the neural network. This would lead to a resilient flight control system, and thus, the asymptotic stability of both the guidance and control loops can be theoretically ensured for a generic aerial robot subject to different types of nonparametric internal and external disturbances. Besides, to enhance the learning efficiency, a composite learning method is adopted in which the neural network and the disturbance observer are trained using a composite error function consisting of the tracking error and the estimation error of an introduced adaptive state observer. To the best of the authors’ knowledge, this is the first completely adaptive integrated guidance and control system with guaranteed stability under parametric and nonparametric internal and external disturbances. The introduced control system is then applied to a simulation model of an electric aircraft that has been validated on the basis of real data and flight experiments. The obtained results indicate that the proposed approach could be considered a reliable guidance and control system for a generic fixed-wing aerial vehicle in the presence of actuator faults, unmodeled dynamics, external disturbances, and measurement noises.

A 3-DoF robotic platform for the rehabilitation and assessment of reaction time and balance skills of MS patients.

The central nervous system (CNS) exploits anticipatory (APAs) and compensatory (CPAs) postural adjustments to maintain the balance. The postural adjustments comprising stability of the center of mass (CoM) and the pressure distribution of the body influence each other if there is a lack of performance in either of them. Any predictable or sudden perturbation may pave the way for the divergence of CoM from equilibrium and inhomogeneous pressure distribution of the body. Such a situation is often observed in the daily lives of Multiple Sclerosis (MS) patients due to their poor APAs and CPAs and induces their falls. The way of minimizing the risk of falls in neurological patients is by utilizing perturbation-based rehabilitation, as it is efficient in the recovery of the balance disorder. In light of the findings, we present the design, implementation, and experimental evaluation of a novel 3 DoF parallel manipulator to treat the balance disorder of MS. The robotic platform allows angular motion of the ankle based on its anthropomorphic freedom. Moreover, the end-effector endowed with upper and lower platforms is designed to evaluate both the pressure distribution of each foot and the CoM of the body, respectively. Data gathered from the platforms are utilized to both evaluate the performance of the patients and used in high-level control of the robotic platform to regulate the difficulty level of tasks. In this study, kinematic and dynamic analyses of the robot are derived and validated in the simulation environment. Low-level control of the first prototype is also successfully implemented through the PID controller. The capacity of each platform is evaluated with a set of experiments considering the assessment of pressure distribution and CoM of the foot-like objects on the end-effector. The experimental results indicate that such a system well-address the need for balance skill training and assessment through the APAs and CPAs.

Adaptive distributed observer-based predictive formation tracking control for autonomous underwater vehicles

Numerous existing autonomous underwater vehicles (AUVs) formation control works have focused on the observation of external time-varying disturbances in trajectory tracking control. However, external time-varying disturbance outliers cannot be ignored due to the complex marine environment. It is rarely considered in previous research. In this paper, a double-layer distributed formation prediction control scheme is designed, including an upper-level adaptive distributed observer and a lower-level predictive controller. Firstly, the velocity is observed and regarded as an unknown variable by the expectation maximization algorithm. The gain in the observer will be optimized by adaptive cubature Kalman to improve the anti-disturbance performance of the adaptive distributed observer. Then, the constraints of internal safety distance and input saturation are fully considered. The distributed cooperative cost function is constructed based on the virtual trajectory, which is further introduced into local rolling domain optimization to realize formation predictive control. The boundedness of innovation and Lyapunov stability are utilized to prove the reliability of the control system. Some comparative simulation experiments are designed to demonstrate the effectiveness and robustness of the proposed method.

Sensor Fusion using Model Predictive Control for Differential Dual Wheeled Robot

Every mobile robot mission starts with the robot being moved to the task site. From there, the robot executes its tasks. A control system is required to move the mobile robot's actuator (which may be in the shape of wheels or legs) and comprehend the environment around the robot to perform these movements (perception). This research aims to develop a technique to control a robot’s movement while detecting obstacles and distances toward an object. The robot is equipped with LIDAR and a camera to perform these tasks. The control is divided into two major parts, low-level and high-level controller. As part of a low-level controller robot, the Model Predictive Control (MPC) method is proposed to help with the control of the wheel while the Artificial Neural Network (ANN) approach to use in this study to identify obstacles and the Convolutional Neural Network (CNN) method for detecting objects, both ANN and CNN as a control for high-level part of the robot. The results of this study can prove that CNN can help detect existing objects with a value of 45% for detecting some objects. The obtained result from the MPC method, which has been combined with an ANN as an obstacle detector, is that the smaller the horizon value, the shorter the time needed to reach the desired coordinates with the result being 45 seconds.

Trajectory Tracking Model Predictive Controller Design for Autonomous Vehicles with Updating Constrains of Tire Characteristics

In this paper, we address the problem of trajectory tracking control of autonomous vehicles by considering the nonlinear characteristics of tires. By considering the influence of the tires’ dynamics on steering stability, the proposed predictive controller can track the desired trajectory and desired velocity in the presence of road curvature while minimizing the lateral tracking deviation. First of all, a hierarchical control structure is adopted, in which the upper-level controller is used to calculate the desired acceleration and the desired front-wheel angle to maintain the control target, and the lower-level controller realized the command through the corresponding component devices. Moreover, a force estimator is designed based on the radial basis function (RBF) neural network to estimate the lateral force of the tires, which is incorporated into the boundary conditions of the vehicle envelope constraint to improve the adaptability of the controller to the vehicle performance. Finally, the proposed controller is tested by co-simulation of CarSim (a simulation software specifically for vehicle dynamics)/Simulink (a modular diagram environment for multidomain simulation as well as model-based design) and hardware-in-loop simulation system. The co-simulation and experimental results demonstrate the controller safely driving at the vehicle’s handling limits and effectively reduce the slip phenomenon of the vehicle.

Novel Multibus Multivoltage Concept for DC-Microgrids in Buildings: Modeling, Design and Local Control

In this paper, a novel microgrid (MG) concept suitable for direct current (DC) multibus architectures is depicted. Multibus feature is improved in order to distribute power in DC using a number of buses at different voltage level. A teachers offices building that houses several kinds of loads, including a charging station for electric vehicles (EV), is considered to validate the strategy. Several topologies of power electronics converters (PECs) are included in the system to perform specific tasks and providing isolation between bus and final loads. In order to develop the PECs, first, a switching function is used to obtain average model of each converter. Then, converters design is done by using well known methods that allow to obtain parameter values of all the devices in every version of each kind of converter. A hierarchical control is selected to govern the direct current microgrid (DCMG). At a lower control level, local control stage is implemented and tuned using models and designs obtained, with linear controllers in some PECs and classic strategies in others. In higher control level, there is a supervisory strategy that prioritizes the use of generated power to supply the building’s loads. This energy management system (EMS) is based in Petri net theory; it consists of a start-up test, then source condition synchronous algorithm and load condition synchronous algorithm operate the DCMG according to the mentioned priority. Finally, PECs are tested on standalone, performing in closed loop, facing load changes to verify the adequate operation. Some trajectories of a simplified version of the CDMG are tested with local control in order to validate the multibus multivoltage concept. In order to verify coordinated control, some events managed by EMS are presented.

Guest editorial: Application of cloud energy storage systems in power systems

Guest editorial: Application of cloud energy storage systems in power systems