High-level Control Strategy Research Articles

A novel affordable user interface for robotic surgery training: design, development and usability study.

The use of robotic systems in the surgical domain has become groundbreaking for patients and surgeons in the last decades. While the annual number of robotic surgical procedures continues to increase rapidly, it is essential to provide the surgeon with innovative training courses along with the standard specialization path. To this end, simulators play a fundamental role. Currently, the high cost of the leading VR simulators limits their accessibility to educational institutions. The challenge lies in balancing high-fidelity simulation with cost-effectiveness; however, few cost-effective options exist for robotic surgery training. This paper proposes the design, development and user-centered usability study of an affordable user interface to control a surgical robot simulator. It consists of a cart equipped with two haptic interfaces, a VR visor and two pedals. The simulations were created using Unity, which offers versatility for expanding the simulator to more complex scenes. An intuitive teleoperation control of the simulated robotic instruments is achieved through a high-level control strategy. Its affordability and resemblance to real surgeon consoles make it ideal for implementing robotic surgery training programs in medical schools, enhancing accessibility to a broader audience. This is demonstrated by the results of an usability study involving expert surgeons who use surgical robots regularly, expert surgeons without robotic surgery experience, and a control group. The results of the study, which was based on a traditional Peg-board exercise and Camera Control task, demonstrate the simulator's high usability and intuitive control across diverse user groups, including those with limited experience. This offers evidence that this affordable system is a promising solution for expanding robotic surgery training.

A feedback learning-based selection hyper-heuristic for distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time

Distributed manufacturing is increasingly common due to economic globalization. It has important practical significance to optimize global supply chains by considering cooperative scheduling of distributed production and flexible assembly. This paper studies a distributed heterogeneous hybrid blocking flow-shop scheduling problem with flexible assembly and setup time (DHHBFSP-FAST). The objective is to minimize makespan of all products. To tackle such problem, a mixed-integer linear programming model (MILP) is presented to formulate it. Then, a feedback learning-based selection hyper-heuristic (FLS-HH) is proposed, which contains high-level control strategies and low-level heuristics. For the high-level control strategies, the transition information between each pair of low-level heuristics is collected, then a feedback learning-based selection method is presented based on such transition information, which can automatically select the appropriate low-level heuristics. For the low-level heuristics, to assign jobs/products to machines, a composite dispatch rule is proposed. To generate an initial domain solution with high quality, a constructive heuristic based on double evaluation indexes is developed. The critical jobs and products are analyzed to avoid invalid searching. Based on such analysis, several low-level heuristics are presented to search the domain solution space, which combine six problem-specific perturbation heuristics and two local searches to balances exploration and exploitation. Comprehensive numerical experiments are carried out. The effectiveness of the proposed MILP and the special designs of FLS-HH are verified. To verify the effectiveness of FLS-HH, we compare it with 10 existing high-performing approaches. The comparison results show that FLS-HH significantly outperforms its competitors in solving DHHBFSP-FAST.

Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor

A high-level control strategy for a quad rotorcraft Unmanned Aircraft System to perform trajectory tracking tasks is presented, which is based on a regressor-based adaptive approach. The high-level control is designed to interact with a low-level (internal) control loop that cannot be modified to suit the needs of academic researchers. Hence, the proposed control framework computes the appropriate high-level inputs for the inner controller, enabling the trajectory tracking task. The controller includes an integral action to overcome steady-state errors that may occur due to parameter estimation errors or constant disturbances. The stability of the equilibrium point is analyzed using Lyapunov theory, which shows that the tracking errors converge to zero and the parameter estimation errors remain bounded. The proposed control framework was tested on a real-time quad rotorcraft platform, and its performance was compared with four different control strategies. The results indicate that the proposed controller exhibits high accuracy and has better performance with respect to the other controllers.

Longitudinal and lateral control of vehicle platoons using Laguerre-based and robust MPC with merge and exit maneuvers

This paper addresses the longitudinal and lateral control of platooning vehicles with the objective of maintaining safe vehicular distance and accomplishing lateral maneuvers. For the longitudinal control, a two-layer controller is proposed. In the first step, feedback linearization transform the nonlinear longitudinal dynamics of vehicles to a linear one for which Laguerre-based Model predictive control (LMPC) is designed in the second layer. The objective function of LMPC includes safe vehicular distance and control effort under state and input constraints. The proposed controller using Laguerre orthonormal functions for input signal parametrization, achieves the desired platoon control and dramatically decreases computation time. For lateral control, a robust MPC is developed to track the reference path in maneuvers of merging a new vehicle to the platoon or exiting a vehicle from it. The road bump is considered a source of uncertainties and is modeled with a bounded additive disturbance. The proposed longitudinal and lateral controller is then used in a high-level control strategy to manage merge and exit maneuvers. Besides extensive simulation results to verify the efficiency of the proposed strategy, experimental evaluation using the hardware-in-the-loop (HIL) test bench based on Raspberry Pi board and actual robots validated the plan’s viability in practice.

Hierarchical Classification of Subject-Cooperative Control Strategies for Lower Limb Exoskeletons in Gait Rehabilitation: A Systematic Review

Over the last decade, lower limb exoskeletons have seen significant development, with a particular focus on improving the interaction between the subject and the exoskeleton. This has been achieved by implementing advanced control strategies that enable the safe and efficient use of the exoskeleton. In this work, the control strategies for lower limb exoskeletons are divided into upper-level control (supervisory and high-level control) and lower-level control (the servo layer). Before discussing these control strategies, a brief introduction to lower limb exoskeletons and their control schemes is provided. The control hierarchy for lower limb exoskeletons is then systematically reviewed along with an overview of the techniques used. A Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement is used to highlight the systematic process of identifying relevant articles with inclusion and exclusion criteria. The details of supervisory control, high-level control, and servo control strategies are presented by citing relevant state-of-the-art studies, particularly from the past five years. The targeted lower limb joint, training mode, and development stage for different control strategies are highlighted in a tabulated form to articulate the overall hierarchy level. Finally, the potential opportunities and limitations of subject-cooperative control are discussed. Overall, this work aims to provide an in-depth understanding of the control strategies used in lower limb exoskeletons, focusing on subject cooperation. This knowledge can be used to improve the safety and efficacy of lower limb exoskeletons, ultimately benefiting individuals with mobility impairments.

A Review on Multilevel Converters for Efficient Integration of Battery Systems in Stationary Applications

Recently, multilevel converters (MLCs) have gained significant attention for stationary applications, including static compensators, industrial drives, and utility-grid interfaces for renewable energy sources. Compared to two-level voltage-source inverters (VSI) MLCs feature high-quality AC voltage with reduced harmonic content despite the lower switching frequency of the semiconductor devices. On the DC side, MLCs can integrate multiple isolated/non-isolated battery modules instead of a single battery pack. This helps to keep the system in service in case of a malfunction of one or more battery modules, as well as active balancing among the modules, a feature not possible with two-level VSI. In general, MLCs can be classified into two types: (i) two-port MLCs, which provide a single interface to connect with the battery pack, and (ii) multiport MLCs, which provide multiple interfaces to allow connection at the module or cell level. The classical topologies of both MLC types (e.g., neutral point clamped, flying capacitor, cascaded bridge) face limitations due to the high switch count. Consequently, many hybrid and reduced-switch topologies are reported in the literature. This paper presents a critical overview of both classical and recently reported MLC topologies and offers a better insight of MLC operation for grid-connected and standalone applications. In addition, the analysis thoroughly assesses various high-level control and modulation strategies while considering active balancing among the battery modules. Other salient features such as balancing speed during offtake/grid-injection mode and fault-ride-through capability are also incorporated. In conclusion, the key findings are summarized for a better understanding of the present and future integration of battery systems in stationary applications.

Next Level of Human-Robot Collaboration by Utilizing AI Pose Estimation and Model Predictive Motion Planning Technologies

Over recent years, there has been a significant focus on Human Robot Interaction (HRI). The aim of this interaction is to create a mutually beneficial partnership between humans and robots, with robots contributing precision, speed, and force, while humans provide experience, intuition, and high-level management and control strategy understanding. One of the most crucial factors in achieving a successful HRI is the guidance of these robots. This paper proposes the use of reliable Hand Detection based on Long Short-Term Memory (LSTM) and intelligent robot motion planning based on Model Predictive Control Methods (MPC) to detect hand signals and communicate with the robot. By utilizing vision systems, a micro controller-based Edge AI approach, and a wired connection, the reaction speed of the robot can be optimized to maintain a safe separation distance between the human operator and the robot, thus enabling a collaborative and safe environment. The effectiveness and capabilities of this automation framework are validated through a case study.

Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

Leveraging on-site renewable sources like solar and wind provides ample opportunities on developing environmental friendly and energy-efficient data centers. Due to intermittent nature of renewable energy and fluctuating grid energy price, advanced data centers often deploy distributed UPS systems with high efficiency, scalability, and reliability. In this study, we investigate the problem of energy management for data centers with renewable resources and energy storages. We propose an interface-backbone layer framework designed unified energy efficiency management system called REDUX, which properly allocate fluctuating renewable energy, UPS battery energy storage or grid power with dynamic price aiming to minimize the long-term electricity bill for the data center. With prediction of renewable energy supply, categorization of grid power price level and energy storage in the UPS devices, REDUX orchestrates workload distribution with heuristic algorithms which act as renewable energy smoothing, UPS device control, and high level control strategies, and make back-fills or defer decisions for the non-urgent jobs. Compared with the existing strategies, REDUX provides a comprehensive view for the energy efficiency data center and demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.

Current developments of robotic hip exoskeleton toward sensing, decision, and actuation: A review.

The aging population is now a global challenge, and impaired walking ability is a common feature in the elderly. In addition, some occupations such as military and relief workers require extra physical help to perform tasks efficiently. Robotic hip exoskeletons can support ambulatory functions in the elderly and augment human performance in healthy people during normal walking and loaded walking by providing assistive torque. In this review, the current development of robotic hip exoskeletons is presented. In addition, the framework of actuation joints and the high-level control strategy (including the sensors and data collection, the way to recognize gait phase, the algorithms to generate the assist torque) are described. The exoskeleton prototypes proposed by researchers in recent years are organized to benefit the related fields realizing the limitations of the available robotic hip exoskeletons, therefore, this work tends to be an influential factor with a better understanding of the development and state-of-the-art technology.

Nonlinear Model Predictive Control for Optimal Motion Planning in Autonomous Race Cars

Motorsport has historically driven automobile innovation by challenging the world's best car manufacturers to design, and develop vehicles that push limits of contemporary technology, and compete at physical vehicle limits. Autonomous driving is a rapidly evolving field that garners interest in industry, government, and research due to substantial improvements in road safety, and traffic flow. Autonomous racing is a byproduct of advancements in autonomous driving, and guarantees innovation in the field through the design of state-of-the-art perception, motion planning, and control algorithms developed to perform in fast-paced, multi-object environments at high speeds, operating at a vehicle's acceleration, and tire limits. We propose a high-level Nonlinear Model Predictive Control (NMPC) strategy incorporating a Pacejka tire model, and nonlinear vehicle dynamics in the global coordinate system with constraints based on track boundaries, and vehicle input limits for optimal motion planning to minimize lap time. The NMPC motion planner is evaluated in three real-world racetracks, Circuit of the Americas, Circuit de Spa-Francorchamps, and Autodromo Nazionale Monza for three race car classes, Formula 1 (F1), Le Mans Prototype (LMP1), and Grand Touring Endurance (GTE). The proposed NMPC strategy is shown to generate time-optimal trajectories for each vehicle class in the evaluated tracks, conforming to optimal racing lines demonstrated by professional racing drivers.

Vision-Guided Autonomous Robotic Electrical Bio-Impedance Scanning System for Abnormal Tissue Detection

In cancer surgery, the correct localization of cancerous tissue is critical for the success of the surgery. Specifically, the surgical goal is to remove the malignant tissue completely while preserving the maximum amount of healthy tissue. Unfortunately, limited technologies are available for intra-operative assessment of tumor margin in Robotic Minimally Invasive Surgery (RMIS). This work presents a vision-based autonomous robotic scanning system for abnormal tissue detection. A stereo vision system, including dense 3D surface reconstruction and tissue tracking, is used to control the scanning motion to compensate for tissue motion and deformation. A high level control strategy allows to control a robot arm to scan autonomously the tissue by means of a monopolar forceps measuring the Electric Bio-Impedance (EBI) of the tissue. The acquired information is used for discriminating cancerous from healthy tissue in real-time, and this information is presented as an impedance map overlaid on the endoscopic video using Augmented Reality (AR). The proposed system performance is evaluated on ex-vivo animal tissues, through experiments simulating the identification and localization of hepatic cancer for resection surgery. Quantitative results show that the system is able to autonomously scan the area of interest and provide a tissue characterization with an accuracy over 82%.

AN INTELLIGENT ENERGY MANAGEMENT SYSTEM FOR SHIP HYBRID POWER SYSTEM BASED ON RENEWABLE ENERGY RESOURCES

A hybrid ship power system with fuel cell and storage system batteries/supercapacitors can be developed by adding renewable energy sources. Adding PV to the hybrid system enhances the system's reliability and dependability. However, a high-level control strategy is needed to manage the generated power between the fuel cell and the photovoltaic array and determine the suitable time to charge or discharge the stored energy according to the load demand. The perfect solution is using an intelligent neural network technique to control the ship's hybrid power system because of the system's nonlinearity and the existence of pulsing and high-density load demand. This paper introduces an intelligent artificial neural network (ANN) technique that depends on previous experience. ANN is flexible and easy to modify, adding/removing power system components, and be scaled to any ship power system rating. Simulation results using MATLAB software prove that the robust, intelligent power management system can control and identify which energy source will be exploited according to the daylight. Moreover, calculate the amount of generating power depending on the shipload demand. In addition to that, it ensures the system dependability considering the other source as standby while the storage system is the power source in the transient period in case of switching between the two systems and maintaining the storage system in the high state of charge possible. Furthermore, this will reduce fuel consumption during the ship's cursing mission. يعتبر نظام القدره الکهربيه المکون من خلايا هيدروجينيه ونظام تخزين للطاقه مناکثر الانظمه استخداما فى جميع المجالات. يهدفهذا البحث الى تحسين أداء هذا النظام عن طريقه إضافه مجموعه خلايا شمسيه لزيادة اعتماديه النظام وتوفير الوقودالمستخدم وايضا الى تقليل التلوث البيئى . وجود اکثر من مصدر للطاقه الغير متجانس يتطلب وجود نظام اداره قوى لتحديد المصدر المناسب لتغذيه الاحمال وافضل الاوقات لشحن او تفريغ البطاريات. يقدم البحث نظام اداره الطاقه الذکى باستخدام تقنيه الشبکات العصبونيه والذى يتميز بالمرونه والقدره على التکيف اعتمادا على الخبرات السابقه للمستخدم وامکانيه اضافه او حذف اى مصدر او تعديل المدخلات . تم استخدام المخاکاة باستخدام الماتلاب لضمان واختبار النظام تبعا للخبرات السابقه.

Model-Based Control and External Load Estimation of an Extensible Soft Robotic Arm.

Soft robotics has widely been known for its compliant characteristics when dealing with contraction or manipulation. These soft behavior patterns provide safe and adaptive interactions, greatly relieving the complexity of active control policies. However, another promising aspect of soft robotics, which is to achieve useful information from compliant behavior, is not widely studied. This characteristic could help to reduce the dependence of sensors, gain a better knowledge of the environment, and enrich high-level control strategies. In this paper, we have developed a state-change model of a soft robotic arm, and we demonstrate how compliant behavior could be used to estimate external load based on this model. Moreover, we propose an improved version of the estimation procedure, further reducing the estimation error by compensating the influcence of pressure deadzone. Experiments of both methods are compared, displaying the potential effectiveness of applying these methods.

PuRSUE -from specification of robotic environments to synthesis of controllers

Developing robotic applications is a complex task, which requires skills that are usually only possessed by highly-qualified robotic developers. While formal methods that help developers in the creation and design of robotic applications exist, they must be explicitly customized to be impactful in the robotics domain and to support effectively the growth of the robotic market. Specifically, the robotic market is asking for techniques that: (i) enable a systematic and rigorous design of robotic applications though high-level languages; and (ii) enable the automatic synthesis of low-level controllers, which allow robots to achieve their missions. To address these problems we present the PuRSUE (Planner for RobotS in Uncontrollable Environments) approach, which aims to support developers in the rigorous and systematic design of high-level run-time control strategies for robotic applications. The approach includes PuRSUE-ML a high-level language that allows for modeling the environment, the agents deployed therein, and their missions. PuRSUE is able to check automatically whether a controller that allows robots to achieve their missions might exist and, then, it synthesizes a controller. We evaluated how PuRSUE helps designers in modeling robotic applications, the effectiveness of its automatic computation of controllers, and how the approach supports the deployment of controllers on actual robots. The evaluation is based on 13 scenarios derived from 3 different robotic applications presented in the literature. The results show that: (i) PuRSUE-ML is effective in supporting designers in the formal modeling of robotic applications compared to a direct encoding of robotic applications in low-level modeling formalisms; (ii) PuRSUE enables the automatic generation of controllers that are difficult to create manually; and (iii) the plans generated with PuRSUE are indeed effective when deployed on actual robots.

A Stabilization Method Based on an Adaptive Feedforward Controller for the Underactuated Bipedal Walking with Variable Step-Length on Compliant Discontinuous Ground

Both compliance and discontinuity are the common characteristics of the real ground surface. This paper proposes a stabilization method for the underactuated bipedal locomotion on the discontinuous compliant ground. Unlike a totally new control method, the method is actually a high-level control strategy developed based on an existing low-level controller meant for the continuous compliant ground. As a result, although the ground environment is more complex, the calculation cost for the robot walking control system is not increased. With the high-level control strategy, the robot is able to adjust its step-length and velocity simultaneously to stride over the discontinuous areas on the compliant ground surface. The effectiveness of the developed method is validated with a numerical simulation and a physical experiment.

Cascaded Pumping Cycle Control for Rigid Wing Airborne Wind Energy Systems

Airborne wind energy is an emerging technology that uses tethered unmanned aerial vehicles for harvesting wind energy at altitudes higher than conventional towered wind turbines. To make the technology competitive to other renewable energy technologies a reliable control system is required that allows autonomously operating the system throughout all phases of flight. In the present work a cascaded nonlinear control scheme for reliable pumping cycle control of a rigid wing airborne wind energy system is proposed. The high-level control strategy in the form of a state machine as well as the flight controller consisting of path-following guidance and control, attitude, and rate loop is presented along with a winch controller for tether force tracking. A mathematical model for an existing prototype will be derived, and results from a simulation study will be used to demonstrate the robustness of the proposed concept in the presence of turbulence and wind gusts.

Reviewing high-level control techniques on robot-assisted upper-limb rehabilitation

ABSTRACTThis paper presents a comprehensive review of high-level control techniques for upper-limb robotic training. It aims to compare and discuss the potentials of these different control algorithms, and specify future research direction. Included studies mainly come from selected papers in four review articles. To make selected studies complete and comprehensive, especially some recently-developed upper-limb robotic devices, a search was further conducted in IEEE Xplore, Google Scholar, Scopus and Web of Science using keywords (‘upper limb*’ or ‘upper body*’) and (‘rehabilitation*’ or ‘treatment*’) and (‘robot*’ or ‘device*’ or ‘exoskeleton*’). The search is limited to English-language articles published between January 2013 and December 2017. Valuable references in related publications were also screened. Comparative analysis shows that high-level interaction control strategies can be implemented in a range of methods, mainly including impedance/admittance based strategies, adaptive control techniques, and physiological signal control. Even though the potentials of existing interactive control strategies have been demonstrated, it is hard to identify the one leading to maximum encouragement from human users. However, it is reasonable to suggest that future studies should combine different control strategies to be application specific, and deliver appropriate robotic assistance based on physical disability levels of human users.

Advances in Powered Ankle-Foot Prostheses.

We present a review of recent developments in powered ankle-foot prostheses (PAFPs), with emphasis on actuation, high- and low-level control strategies, and pneumatic, hydraulic, and electromechanical actuators. A high-level control strategy based on finite-state machines, combined with low-level control that drives the ankle torque, is the most common control strategy. On the other hand, brushless direct-current motors along with an energy storage and release mechanism are commonly used to reduce the overall size of the actuators and increase PAFP autonomy. Most designs have been evaluated experimentally, showing acceptable results in walking velocity and gait symmetry. Future research must focus on reducing weight, increasing energy efficiency, improving gait phase classification and/or intent of motion-prediction algorithms, updating low-level control of torque and position, and developing the ability of the patient to walk on sloped surfaces and negotiate stairs.

Positioning control of an underwater robot with tilting thrusters via decomposition of thrust vector

Positioning control of an underwater robot is a challenging problem due to the high disturbances of ocean flow. To overcome the high disturbance, a new underwater robot with tilting thrusters was proposed previously, which can compensate for disturbance by focusing the thrusting force in the direction of the disturbance. However, the tilting motion of the thrusters makes the system nonlinear, and the limited tilting speed sometimes makes the robot unstable. Therefore, an optimized controller is necessary. A new positioning controller is proposed for this robot using a vector decomposition method. Based on the dynamic model, the nonlinear force input term of the tilting thrusters is decomposed in the horizontal and vertical directions. Based on the decomposition, the solution is determined by a pseudo-inverse and null-space solution. Using the characteristics of the decomposed input matrix, the final solution can be found by solving a simple second-order algebraic equation to overcome the limitations of the tilting speed. The positioning was simulated to validate the proposed controller by comparing the results with a switching-based controller. Tracking results are also presented. In future work, a high-level control strategy will be developed to take advantage of the tilting thrusters by focusing the forcing direction toward the disturbance with a limited stability margin.

Real-Time Power-Hardware-in-the-Loop Implementation of Variable-Speed Wind Turbines

This paper presents the real-time (RT) power-hardware-in-the-loop (PHIL) emulation of the two most popular variable-speed wind turbines (WT): the doubly-fed induction generator WT with partial-scale power converter and the permanent-magnet synchronous machine WT with full-scale power converter. RT-PHIL emulation offers a realistic, controlled, and easily reconfigurable test environment for analyzing grid integration problems, in which classic tools frequently show a lack of capacity. The proposed discrete-time models and implementation methodology result in creation of platform-independent models suitable for application on any RT platform. A voltage-source converter is used as PHIL power interface between the RT simulation and a microgrid testbed. This test environment is of interest for verification of high-level control strategies for wind farms and for evaluation of the response of hardware systems, such as active filters or static synchronous compensators (STATCOMs) connected to a grid (or microgrid) in presence of wind power generation. The RT discrete models of both WTs are demonstrated in a grid-connection experiment.