General Control Framework Research Articles

Task Space Compliant Control and Six-Dimensional Force Regulation Toward Automated Robotic Ultrasound Imaging

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> We propose a general control framework for task space compliant motion and six-dimensional (6-D) force regulation towards automated robotic ultrasound (US) imaging. The framework endows a position-controlled robotic manipulator with the capability of accurate compliant motion in free space and accurate force control in motion-constrained environment. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> An intuitive six degree-of-freedom (6-DoF) admittance control model expressed in an arbitrary Cartesian body frame is mathematically derived with closed-form task space error mapping. Its practical implementation on widely-used collaborative manipulators is proposed to achieve full task space compliant behaviors and accurate 6-D force control. A hybrid control law is presented to achieve good motion accuracy in free space and improved coupled stability in motion-constrained environment. The coupled model of physical human-robot interaction is established and the reason for the improved coupled stability is analyzed through simulation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Evaluation experiments on the proposed control framework were performed to show the effectiveness. The mean error of compliant trajectory following was less than 0.30 mm in free space. The mean relative force and moment control accuracy in three orthogonal directions was better than 0.5% and 0.8%, respectively. The improved coupled stability under the same model parameters was also confirmed by human-robot interaction experiments. Finally, an automated robotic US imaging experiment on a human volunteer in a real clinical scenario was carried out to show the potential application of our proposed framework. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Experimental results have shown the advantages of the control framework, including satisfied force control accuracy, high accuracy of compliant motion, improved coupled stability, and system effectiveness on a human volunteer. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper was motivated by the increasing needs of automated ultrasound (US) scanning for both diagnostic and interventional purpose. Clinical sonographers suffer from repeated workload when performing diagnostic US imaging, which could benefit from automated robotic scanning. Robotic US imaging involves physical interaction between the robot end-effector (i.e., US transducer) and the human body. The dynamics of the interaction is regulated by the control law to guarantee the contact of the US transducer and the safety of the procedure. Most existing works have focused on regulating in-plane contact force in terms of the position without considering the compliance in other dimensions. However, it is not a trivial work to extend the positional compliance to six degree-of-freedom (6-DoF) compliance. As the prevalence of low-cost collaborative robotic arms in medical scenarios, how to perform 6-DoF compliant trajectory following and accurate six-dimensional (6-D) force control on these robotic arms becomes increasingly important. This paper gives a complete general solution to achieve 6-DoF compliant control and 6-D force regulation with accurate kinematics on a position-controlled robotic arm. A hybrid control law is proposed to switch the government of “instantaneous model” and “theoretical model” to achieve compliant motion accuracy in free space and improved coupled stability in motion-constrained environment. No expensive torque sensors and torque control interface are required. And no prior geometric knowledge about the scanning object is needed. We have demonstrated the application for robotic US imaging in a real clinical scenario.

An Integrated Scheme for Forecasting and Controlling Ramps in Offshore Wind Farms Considering Wind Power Uncertainties during Extreme Storms

Global warming-induced extreme tropical storms disrupt the operation of offshore wind farms, causing wind power ramp events and threatening the safety of the interconnected onshore grid. In order to attenuate the impact of these ramps, this paper proposes an integrated strategy for forecasting and controlling ramps in offshore wind farms. First, the characteristics of wind power ramps during tropical storms are studied, and a general ramp control framework is established. Second, a wind power ramp prediction scheme is designed based on a minimal gated memory network (MGMN). Third, by taking into account the wind power ramp prediction results and wind power uncertainties, a chance-constraint programming-based optimal ramp control scheme is developed to simultaneously maximize wind power absorption and minimize ramp control costs. Finally, we use real-world offshore wind farm data to validate the effectiveness of the proposed strategy.

A multi-scale control framework for urban traffic control with connected and automated vehicles

Urban traffic control is multi-scale in both temporal and spatial domains, which is of great importance to improve the quality and efficiency of our transportation system. In this paper, we propose a general multi-scale modeling and control framework for urban traffic control with connected and automated vehicles (CAVs). We apply the proposed multi-scale framework to explicitly model and solve the two-scale signal-vehicle coupled control (SVCC) problem under the environment of full penetration of CAVs. To solve the SVCC problem, we propose a model predictive control (MPC) scheme and develop a stability analysis method based on the concept of consistency of key states between the two scales. We show numerical results of the SVCC model and comparisons with some benchmark methods. We also discuss how to extend the SVCC model to the mixed traffic flow and tri-scale/multi-scale urban traffic control problems.

SSMSPC: self-supervised multivariate statistical in-process control in discrete manufacturing processes

Self-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning effective representations by solving a supervised pretext task with pseudo-labels generated from unlabeled data provides a promising concept for industrial downstream tasks such as process monitoring. In this paper, we present SSMSPC a novel approach for multivariate statistical in-process control (MSPC) based on self-supervised learning. Our motivation for SSMSPC is to leverage the potential of unsupervised representation learning by incorporating self-supervised learning into the general statistical process control (SPC) framework to develop a holistic approach for the detection and localization of anomalous process behavior in discrete manufacturing processes. We propose a pretext task called Location + Transformation prediction, where the objective is to classify both, the type and the location of a randomly applied augmentation on a given time series input. In the downstream task, we follow the one-class classification setting and apply the Hotelling’s T2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T^2$$\\end{document} statistic on the learned representations. We further propose an extension to the control chart view that combines metadata with the learned representations to visualize the anomalous time steps in the process data which supports a machine operator in the root cause analysis. We evaluate the effectiveness of SSMSPC with two real-world CNC-milling datasets and show that it outperforms state-of-the-art anomaly detection approaches, achieving 100%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$100\\%$$\\end{document} and 99.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$99.6\\%$$\\end{document} AUROC, respectively. Lastly, we deploy SSMSPC at a CNC-milling machine to demonstrate its practical applicability when used as a process monitoring tool in a running process.

A General Framework for Hierarchical Redundancy Resolution Under Arbitrary Constraints

The increasing interest in autonomous robots with a high number of degrees of freedom for industrial applications and service robotics demands control algorithms to handle multiple tasks as well as hard constraints efficiently. This article presents a general framework in which both kinematic (velocity- or acceleration-based) and dynamic (torque-based) control of redundant robots are handled in a unified fashion. The framework allows for the specification of redundancy resolution problems featuring a hierarchy of arbitrary (equality and inequality) constraints, arbitrary weighting of the control effort in the cost function and an additional input used to optimize possibly remaining redundancy. To solve such problems, a generalization of the saturation in the null space algorithm is introduced, which extends the original method according to the features required by our general control framework. Variants of the developed algorithm are presented, which ensure both efficient computation and optimality of the solution. Experiments on a KUKA LBRiiwa robotic arm, as well as simulations with a highly redundant mobile manipulator are reported.

An adaptive frequency regulation strategy with high renewable energy participating level for isolated microgrid

To improve the renewable energy sources (RES) utilization of isolated microgrids (IMG) in frequency regulation process, an adaptive frequency regulation strategy with high renewable energy participating level is proposed in this paper: 1) an adaptive droop control method is conceived for primary frequency regulation, in which the designed droop coefficients are adjustable with the real-time wind velocity and state of charge (SoC) of energy storage units (ESUs); as the most important contribution, 2) a general flexible model predictive control (FMPC) framework with adaptive cost function and constraints is developed for secondary frequency regulation. For 2), i) the weight coefficients of the cost function can be adaptively adjusted according to frequency deviation through a pre-designed intelligent function, in which the specification of determining the key parameters is involved; ii) the designed constraints' boundaries can dynamically shift with wind velocity and ESU's SoC; iii) the negative impact of model's uncertainties can also be mitigated by implementing the proposed FMPC. Finally, the effectiveness of the proposal is verified by the comprehensive simulation and experimental study, and the results show that the proposal can significantly improve RES's utilization level while guaranteeing frequency regulation performance and operation safety in IMG.

A general motion control framework for an autonomous underwater vehicle through deep reinforcement learning and disturbance observers

This paper investigates the application of deep reinforcement learning (RL) in the motion control for an autonomous underwater vehicle (AUV), and proposes a novel general motion control framework which separates training and deployment. Firstly, the state space, action space, and reward function are customized under the condition of ensuring generality for various motion control tasks. Next, in order to efficiently learn the optimal motion control policy in the case that the AUV model is imprecise and there are unknown external disturbances, a virtual AUV model composed of the known and determined items of an actual AUV is put forward and a simulation training method is developed on this basis. Then, in the given deployment method, three independent extended state observers (ESOs) are designed to deal with the unknown items in different directions, and the final controller is obtained by compensating the estimated value of ESOs into the output of the optimal motion control policy obtained through simulation training. Finally, soft actor-critic is chosen as deep RL algorithm of the framework, and the generality and effectiveness of the proposed method are verified in four different AUV motion control tasks.

-Synthesis-Based Generalized Robust Framework for Grid-Following and Grid-Forming Inverters

Grid-following and grid-forming inverters are integral components of microgrids and for integration of renewable energy sources with the grid. For grid following (GFL) inverters, which need to emulate controllable current sources, a significant challenge is to address the large uncertainty of the grid impedance. For grid forming (GFM) inverters, which need to emulate a controllable voltage source, large uncertainty due to varying loads has to be addressed. This article presents a generalized control framework by leveraging the voltage-current duality in the plant dynamic model of GFL and GFM inverters. The modeling of uncertainties is also generalized under the control framework by quantifying the uncertainties in grid impedance parameters and the uncertainties in equivalent loading parameters for GFL and GFM inverters, respectively. Based on the generalized control framework, a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\mu}$</tex-math></inline-formula> -synthesis-based robust control design methodology is proposed for both GFL and GFM inverters. The control objectives, while designing the proposed optimal controllers, are reference tracking, disturbance rejection, and harmonic compensation capability with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${i})$</tex-math></inline-formula> sufficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${LCL}$</tex-math></inline-formula> resonance damping under large variations of grid impedance uncertainty for GFL inverters and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${ii})$</tex-math></inline-formula> with enhanced dynamic response under large variations of equivalent loading uncertainty for GFM inverters. A combined system-in-the-loop, controller hardware-in-the-loop, and power hardware-in-the-loop based experimental validation on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {10}$</tex-math></inline-formula> -kVA microgrid system with two physical inverter systems is conducted in order to evaluate the efficacy and viability of the proposed controllers.

Generalized Design, Modeling and Control Methodology for a Snake-like Aerial Robot.

Snake-like robots have been developing in recent decades, and various bio-inspired ideas are deployed in both the mechanical and locomotion aspects. In recent years, several studies have proposed state-of-the-art snake-like aerial robots, which are beyond bio-inspiration. The achievement of snake-like aerial robots benefits both aerial maneuvering and manipulation, thereby having importance in various fields, such as industry surveillance and disaster rescue. In this work, we introduce our development of the modular aerial robot which can be considered a snake-like robot with high maneuverability in flight. To achieve such flight, we first proposed a unique thrust vectoring apparatus equipped with dual rotors to enable three-dimensional thrust force. Then, a generalized modeling method based on dynamics approximation is proposed to allocate the wrench in the center-of-gravity (CoG) frame to thrust forces and vectoring angles. We further developed a generalized control framework that can handle both under-actuated and fully actuated models. Finally, we show the experimental results with two different platforms to evaluate the flight stability of the proposed snake-like aerial robot. We believe that the proposed generalized methods can provide a solid foundation for the snake-like aerial robot and its applications regarding maneuvering and manipulation in midair.

Multi-UAV flocking control with individual properties inspired by bird behavior

This paper focuses on the cooperative control problem for multiple unmanned aerial vehicles (UAVs). Unlike the existing models emphasizing the behavioral dimension, a general control framework inspired by bird behavior is developed by decoupling the problem into top decision-making and low-level management. Based on the individual properties, the models of communication modes, individual contribution, social relationship, and movement characteristics are proposed, where the individual contribution and social relationship can reflect the results of high-level decisions. The desired commands of the low-level realization are generated after the pre-linearized process and decoupled operator. Meanwhile, to guarantee the safety of each UAV, the collision avoidance mechanism is also applied. The convergence and stability of the proposed algorithm are also discussed. Experimental results demonstrate the effectiveness of the control framework in solving the UAV flocking control problem.

General parameter control framework for evolutionary computation

This study proposes a general multiple parameter control framework by leveraging the ability of a reinforcement learning system to learn empirical knowledge for evolutionary computation. We design a feedback evaluation mechanism to define the rewards offered to agents, using which they can learn to choose appropriate parameters in formulated action sets. Moreover, a learning strategy is proposed to utilize the parameter selection-related knowledge that is gained during training episodes. Three famous evolutionary computation (EC) methods (i.e., particle swarm optimization, artificial bee colony, and differential evolution) are selected as the baseline algorithms and applied to the proposed framework. The aforementioned redesigned algorithms are tested on 15 common benchmark functions, as well as the CEC2017 benchmarks. In addition, the robustness of the algorithms is demonstrated through parameter sensitivity analysis. The results of the comparative analysis reveal that the three improved algorithms exhibit a faster overall convergence and higher accuracy than their state-of-the-art variants. It is also confirmed that our proposed framework has the capability to improve the performance of EC approach.

Resource-Efficient Platooning Control of Connected Automated Vehicles Over VANETs

This paper is concerned with distributed cooperative longitudinal platooning control of connected automated vehicles over resource-constrained vehicular ad-hoc networks. First, a general platoon-based control framework is constructed, which simultaneously accounts for unknown leader control input, external disturbances, intermittent data samplings and transmissions, various spacing policies, and different vehicle-to-vehicle communication topologies. To alleviate excessive occupancy of the limited communication resources, an adaptive Zeno-free event-triggered communication mechanism is developed to determine when the sampling and transmission actions of vehicular data should be performed among interacting vehicles. Then, a scalable co-design approach for the desired adaptive platoon controller as well as event triggering condition is developed. One promising feature of the proposed platooning control strategy is that it is independent on the platoon scale or any global knowledge of the associated communication topology, and thus implementable on each vehicle in a scalable manner. Finally, several numerical case studies are conducted to verify the efficacy of the derived approach.

Reactive Task Adaptation of a Dynamic System With External Disturbances Based on Invariance Control and Movement Primitives

This article proposes a general control framework for robot physical contact tasks. To satisfy the tradeoff between safety and performance in the interaction between robots and the environment, multipriority control is regarded as a preemptive strategy for constraint management when unknown disturbances exist. The equality and inequality constraints related to robot safety are enforced by invariance control, while reference profile tracking is accommodated by dynamic movement primitives without violating the higher priority constraints. With this framework, we complete the unification of force control and motion control in robot task execution. The robot thus acquires the capability for strong disturbance rejection and transferrable intelligence between similar tasks. At the same time, a variant linear–quadratic regulator (LQR) is integrated into the framework, which enables the robot to achieve exponential convergence of the tracking error. The proposed approach is tested and evaluated with two types of physical contact tasks, showing a superior control effect and faster convergence than the existing methods.

Fractal impedance for passive controllers: a framework for interaction robotics

There is increasing interest in control frameworks capable of moving robots from industrial cages to unstructured environments and coexisting with humans. Despite significant improvement in some specific applications (e.g., medical robotics), there is still the need for a general control framework that improves interaction robustness and motion dynamics. Passive controllers show promising results in this direction; however, they often rely on virtual energy tanks that can guarantee passivity as long as they do not run out of energy. In this paper, a Fractal Attractor is proposed to implement a variable impedance controller that can retain passivity without relying on energy tanks. The controller generates a Fractal Attractor around the desired state using an asymptotic stable potential field, making the controller robust to discretization and numerical integration errors. The results prove that it can accurately track both trajectories and end-effector forces during interaction. Therefore, these properties make the controller ideal for applications requiring robust dynamic interaction at the end-effector.

Model predictive control of portable electronic devices under skin temperature constraints

Thermal management is becoming a major challenge for electronics, and a better temperature control algorithm that could maximize the system performance will play a greater role in fully utilizing the existing cooling capacity. Unfortunately, the simplest look-up table method is still widely used as the temperature control algorithm in current portable electronic devices, especially laptops, resulting in a significant performance loss of devices. In this paper, a general temperature control framework for a commercial laptop that considers the skin temperature constraints is proposed based on the model predictive control algorithm. In specific, a high-accuracy compact thermal model is first generated through the model order reduction method and validated by abundant experimental data. Then the proposed MPC is numerically evaluated in three test scenarios, covering different workloads and performance indexes. The results show that the proposed MPC outperforms the baseline look-up table method by achieving about 10–20% higher performance index in different test scenarios. The open-loop optimal control method is also considered to estimate the optimality of the proposed MPC. Moreover, a parametric study is conducted to analyze the influence of different control parameters, indicating broad prospects for the future application of the proposed MPC algorithm.

Secure and collision-free multi-platoon control of automated vehicles under data falsification attacks

This paper addresses the secure and safe distributed cooperative control problem of multiple platoons of automated vehicles under unknown data falsification attacks on driving commands. First, a general multi-platoon control framework is developed, which accommodates longitudinal and lateral vehicle dynamics, inter- and intra-platoon information exchanges, falsified driving commands, and unknown external disturbances. To deal with the unknown falsified driving commands on the platoon performance, a neural-network-based adaptive control strategy is developed to compensate their adverse effects. In order to avert both longitudinal and lateral collisions under various maneuvering scenarios, a built-in avoidance mechanism is then designed for each platoon vehicle. Furthermore, a secure and anti-collision multi-platoon control design approach is proposed to ensure the desired inter- and intra-platoon tracking performance with a collision-free guarantee. It is formally proved that the inter- and intra-platoon tracking errors converge to small neighborhood around zero. Finally, several comparative simulation cases are presented to verify the effectiveness and merits of the proposed multi-platoon control approach.

PARTI-A Haptic Virtual Reality Control Station for Model-Mediated Robotic Applications

In this paper, we introduce a tele-robotic station called “PARTI” that leverages state-of-the-art robotics and virtual reality technology to enable immersive haptic interaction with simulated and remote environments through robotic avatars. Our hardware-in-the-loop framework integrates accurate multibody system dynamics and frictional contacts with digital twins of our robots in a virtual environment with real-time computational capabilities. This model mediated hardware-in-the-loop approach to robotic control allows a teleoperator to use the PARTI system to teach, evaluate, and control various robotic applications. In the current contribution, we focus on the general system description, integrated simulation and control framework, and a series of experiments highlighting the advantages of our approach.

A general locomotion control framework for multi-legged locomotors

Serially connected robots are promising candidates for performing tasks in confined spaces such as search and rescue in large-scale disasters. Such robots are typically limbless, and we hypothesize that the addition of limbs could improve mobility. However, a challenge in designing and controlling such devices lies in the coordination of high-dimensional redundant modules in a way that improves mobility. Here we develop a general framework to discover templates to control serially connected multi-legged robots. Specifically, we combine two approaches to build a general shape control scheme which can provide baseline patterns of self-deformation (‘gaits’) for effective locomotion in diverse robot morphologies. First, we take inspiration from a dimensionality reduction and a biological gait classification scheme to generate cyclic patterns of body deformation and foot lifting/lowering, which facilitate the generation of arbitrary substrate contact patterns. Second, we extend geometric mechanics, which was originally introduced to study swimming at low Reynolds numbers, to frictional environments, allowing the identification of optimal body–leg coordination in this common terradynamic regime. Our scheme allows the development of effective gaits on flat terrain with diverse numbers of limbs (4, 6, 16, and even 0 limbs) and backbone actuation. By properly coordinating body undulation and leg placement, our framework combines the advantages of both limbless robots (modularity and narrow profile) and legged robots (mobility). Our framework can provide general control schemes for rapid deployment of general multi-legged robots, paving the way toward machines that can traverse complex environments. In addition, we show that our framework can also offer insights into body–leg coordination in living systems, such as salamanders and centipedes, from a biomechanical perspective.

Path tracking and stability integrated control of intelligent vehicles under steering collision avoidance

In order to improve the path tracking performance and stability of intelligent vehicles during steering and collision avoidance working condition, an integrated control method combining four-wheel steering, active suspension and direct yaw-moment is proposed. Firstly, a unified chassis dynamics model with 18 degrees-of-freedom (DOF) is established, which includes steering, braking and suspension subsystems. Phase plane analysis results for both traditional 2-DOF bicycle model and the 18-DOF model are compared. The results show that although the uneven road has a certain influence on the phase portraits of sideslip angle and sideslip angular velocity of the 18-DOF model, the overall trend is consistent with those of the 2-DOF model. Secondly, a general control framework for steering and collision avoidance of autonomous vehicle is put forward, consisting of the steering control for path tracking, and active suspension and direct yaw-moment control for stability. The collision avoidance reference path is planned by the fifth-degree polynomial, and three types of steering control methods including optimal control front wheel steering, optimal control four-wheel steering and front wheel steering combined with feedforward and feedback control, are adopted for comparison. The main contribution of this paper is that the role of active suspension is considered in the path tracking and stability integrated control of intelligent vehicles under steering collision avoidance. The active suspension subsystem uses optimal control method by two different kinds of weight coefficients, which can be switched between “comfort mode” and “sport mode” based on the vehicle lateral acceleration. According to the phase portraits of vehicle center of gravity sideslip angle and sideslip angular velocity, the direct-yaw moment control system adopts proportional differential control method based on the distance from the state point to the stability boundary. Finally, simulations and comparative analysis are compared among four different control systems on high and low adhesion roads. The research results indicate that the integrated control system can improve the vehicle path tracking performance and stability significantly.

Autonomous Monitoring and Inspection Operations with UUVs in Fish Farms

In this study, a general control framework for autonomous operations in highly complex and dynamically changing environments such as fish farms is proposed and experimentally validated. Since fish farms feature an environment that includes fish, deformable flexible structures and highly variable environmental disturbances, the framework is designed to interact with these. The proposed control approach integrates estimates of the cage structure dynamics and fish behavior, adaptive path planning and path following control concepts in one unified and compact framework that could be used to implement and demonstrate different concept studies in dynamically changing environments. The performance of the control framework is investigated though field trials using a remotely operated vehicle (ROV) in a commercial fish farm. Experimental results show that the proposed framework can be applied to challenging operations in fish farms.