Layered Control Architecture Research Articles

Small-Signal Stability Analysis and Lead-Lag Compensation Control for DC Networked-Microgrid Under Multiple Time Delays

Networked microgrid (NMG) system is an effective solution to enhance the power system's reliability and resilience. The distributed control is promising for NMG system due to its high reliability, scalability and low computational burden. The distributed control relies on the information exchange among distributed generators and sub-microgrids (sub-MGs), which inevitably contain heterogenous time delays that can deteriorate system stability. This paper focuses on the multiple time-delays small-signal stability analysis of a DC NMG with a distributed layered control architecture. Firstly, a generalized time-delayed DC NMG small-signal model with a typical two-layer distributed control framework is developed. Then, stability analysis is conducted considering multiple time delays from communication and measurement stages in NMG layer and MG layer. The impact of multiple time delays on NMG stability is quantified. We reveal that both the electrical coupling among MGs and different kinds of time delays in distributed control loops will impact the NMG stability. Subsequently, based on the detailed analytical results, the critical eigenvalue is identified, and a new lead-lag compensation controller is proposed to enhance the system stability by compensating for the phase lag of the critical eigenvalue. Lastly, the effectiveness of the proposed method and the accuracy of the analytical findings are verified by OPAL-RT-based real-time tests.

Complex self-driving behaviors emerging from affordance competition in layered control architectures

The deployment of autonomous driving technology is hindered by “corner cases”: unusual nuanced conditions that the self-driving software cannot understand and act fully. We argue that some corner cases originate from a “narrow AI” approach, which lacks the general knowledge that humans exploit when dealing with these cases. We propose an alternative that can be seen as a step toward features of Artificial General Intelligence.We exploit the biological principle of affordance competition in layered control architectures to create an artificial agent that realizes emergent, adaptive, and logical behaviors without programming case-specific rules or algorithms. We give six different examples of simple and complex emergent behaviors. For the case study of merge scenarios, we contrast the approach of this paper with an algorithmic solution of the literature. The ideas presented here (if not the whole agent’s sensorimotor organization) could be used to improve the robustness and flexibility of self-driving technology.

Layered feedback control overcomes performance trade-off in synthetic biomolecular networks

Layered feedback is an optimization strategy in feedback control designs widely used in engineering. Control theory suggests that layering multiple feedbacks could overcome the robustness-speed performance trade-off limit. In natural biological networks, genes are often regulated in layers to adapt to environmental perturbations. It is hypothesized layering architecture could also overcome the robustness-speed performance trade-off in genetic networks. In this work, we validate this hypothesis with a synthetic biomolecular network in living E. coli cells. We start with system dynamics analysis using models of various complexities to guide the design of a layered control architecture in living cells. Experimentally, we interrogate system dynamics under three groups of perturbations. We consistently observe that the layered control improves system performance in the robustness-speed domain. This work confirms that layered control could be adopted in synthetic biomolecular networks for performance optimization. It also provides insights into understanding genetic feedback control architectures in nature.

Distributed Double-Layer Control for Coordination of Multiplatoons Approaching Road Restriction in the Presence of IoV Communication Delays

Internet of Vehicle (IoV) has become a hot topic in the last years due to its significant benefits in transportation systems in terms of quality, costs, and traffic management capability. Indeed, accurate and real-time information acquisition and diffusion are required to reach an efficient traffic management system according to the smart city paradigm. In this context, this article provides a novel IoV-based distributed double layer control architecture to address the coordination control problem of connected autonomous vehicles (CAVs) multiple platoons negotiating the access to a road section restriction in the presence of heterogeneous communication time-varying delays. To this aim, leveraging the IoV paradigm and the equivalent virtual formation concept, we propose a novel distributed diffusive longitudinal protocol and a distributed lateral potential-function-based control strategy, where both of them are able to cope with communication delays and ensuring the multiplatoon coordination in finite time, i.e., before accessing the road section restrictions, so to avoid vehicles collisions. The finite-time stability of both the cooperative controllers is analytically proved by exploiting the Lyapunov–Krasovskii theory and the derived delay-dependent stability conditions, expressed as a set of linear matrix inequalities (LMIs), allows the proper tuning of the controllers gains. Numerical analysis confirms the theoretical derivation and discloses the effectiveness and the robustness of the proposed cooperative multiplatoon control in ensuring the safe crossing of the road section restriction in finite time.

Control method of multi-energy system based on layered control architecture

The application of hierarchical control to complicated systems can considerably simplify the complexity of system control. The multi-energy system can be divided into superior control (such as load forecasting), intermediate control (dispatch control of each subsystem), and subordinate control (equipment operation control under each subsystem), and a realisation path for the theory of overall control strategy based on hierarchical control is formulated. In this work, the combined cooling, heating, and power system(CCHP) + ground-source heat pump(GSHP) + conventional cold and heat source + storage systems are used as examples based on the TRNSYS platform. The objective is to achieve the best comprehensive evaluation index by simulating the system’s key parameters of control. The operation control strategy method applicable to the typical multi-energy system is identified. When the system load rate exceeds 5%, the CCHP is switched on as a priority. Energy storage equipment release energy when the system load rate exceeds 13% with priority release at spike and peak sections. GSHP are turned on when the system load rate exceeds 37%. The gas boiler unit is turned on when the system load rate exceeds 72%. The system water supply temperature is simultaneously optimised in stages according to the outdoor air temperature. This strategy can make the annual comprehensive energy consumption per unit area of the case system the lowest. It can achieve better energy-saving operation effect in multi-energy system.

Scaffolding layered control architectures through constraint closure: insights into brain evolution and development.

The functional organization of the mammalian brain can be considered to form a layered control architecture, but how this complex system has emerged through evolution and is constructed during development remains a puzzle. Here we consider brain organization through the framework of constraint closure, viewed as a general characteristic of living systems, that they are composed of multiple sub-systems that constrain each other at different timescales. We do so by developing a new formalism for constraint closure, inspired by a previous model showing how within-lifetime dynamics can constrain between-lifetime dynamics, and we demonstrate how this interaction can be generalized to multi-layered systems. Through this model, we consider brain organization in the context of two major examples of constraint closure—physiological regulation and visual orienting. Our analysis draws attention to the capacity of layered brain architectures to scaffold themselves across multiple timescales, including the ability of cortical processes to constrain the evolution of sub-cortical processes, and of the latter to constrain the space in which cortical systems self-organize and refine themselves.This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

A Holistic Motion Planning and Control Solution to Challenge a Professional Racecar Driver

We present a holistically designed three layer control architecture capable of outperforming a professional driver racing the same car. Our approach focuses on the co-design of the motion planning and control layers, extracting the full potential of the connected system. First, a high-level planner computes an optimal trajectory around the track, then in real-time a mid-level nonlinear model predictive controller follows this path using the high-level information as guidance. Finally a high frequency, low-level controller tracks the states predicted by the mid-level controller. Tracking the predicted behavior has two advantages: it reduces the mismatch between the model used in the upper layers and the real car, and allows for a torque vectoring command to be optimized by the higher level motion planners. The tailored design of the low-level controller proved to be crucial for bridging the gap between planning and control, unlocking unseen performance in autonomous racing. The proposed approach was verified on a full size racecar, considerably improving over the state-of-the-art results achieved on the same vehicle. Finally, we also show that the proposed co-design approach outperforms a professional racecar driver.

Path following control of tractor with an electro-hydraulic coupling steering system: Layered multi-loop robust control architecture

Tractor path following control usually suffers from the uncertainties and disturbances especially when tractors are faced with uneven soil. As the main actuator of path tracking control, a electro-hydraulic coupling system has parameter uncertainties and disturbances. Conventional methods pay little attention to the electro-hydraulic coupling system which can generate poor path tracking performance. A layered multi-loop robust control architecture (LMLRC) is proposed including a path tracking layer, an angle tracking layer, and a current tracking layer. The path tracking layer is obtained by model predictive controller (MPC) considering uncertainties and disturbances. The angle tracking layer has a sliding mode controller (SMC) which takes parameter uncertainties and disturbances of electro-hydraulic coupling system into consideration. It is designed to follow the target angle generated by path tracking layer and outputs a tracking torque. The current tracking layer is designed by Proportion-Integration-Differentiation (PID) controller to track the torque output by angle tracking layer. To verify the proposed controller, simulation test and hardware in the loop (HIL) test were implemented. The results show that when faced with uncertainties and disturbances the LMLRC is more effective at path following. Moreover, better dynamic stability is exhibited when compared with other controllers.

Diversity-enabled sweet spots in layered architectures and speed–accuracy trade-offs in sensorimotor control

Nervous systems sense, communicate, compute, and actuate movement using distributed components with severe trade-offs in speed, accuracy, sparsity, noise, and saturation. Nevertheless, brains achieve remarkably fast, accurate, and robust control performance due to a highly effective layered control architecture. Here, we introduce a driving task to study how a mountain biker mitigates the immediate disturbance of trail bumps and responds to changes in trail direction. We manipulated the time delays and accuracy of the control input from the wheel as a surrogate for manipulating the characteristics of neurons in the control loop. The observed speed-accuracy trade-offs motivated a theoretical framework consisting of two layers of control loops-a fast, but inaccurate, reflexive layer that corrects for bumps and a slow, but accurate, planning layer that computes the trajectory to follow-each with components having diverse speeds and accuracies within each physical level, such as nerve bundles containing axons with a wide range of sizes. Our model explains why the errors from two control loops are additive and shows how the errors in each control loop can be decomposed into the errors caused by the limited speeds and accuracies of the components. These results demonstrate that an appropriate diversity in the properties of neurons across layers helps to create "diversity-enabled sweet spots," so that both fast and accurate control is achieved using slow or inaccurate components.

Artificial intelligence for operation and control: The case of microgrids

Research on artificial intelligence (AI) has advanced significantly in recent years. A variety of AI algorithms have shown great promise in a large number of applications for power system operation and control. This article examines the potential of applying AI in microgrids (MGs). Specifically, as MGs commonly employ onsite generation including an increasing penetration of non-dispatchable distributed energy resources (DERs) and require seamless transition between operation modes (e.g., grid-connected and island) for different operation scenarios, the energy management within an MG is particularly complicated. Many factors including lack of inertia needed for system stability, generation uncertainty from DERs, and complex MG network topology composition (e.g., AC, DC, and hybrid AC/DC MGs) contribute to the difficulty of microgrid energy management. AI techniques such as deep learning (DL) and deep reinforcement learning (DRL) have recently demonstrated their excellence in tackling problems pertinent to decision making, providing a possible solution to overcome the above-mentioned challenges. This article discusses the applications of AI to MG operation and control, with an emphasis on DL and DRL. We survey the available DL and DRL technologies and their applications to power grids. We also investigate the unique issues associated with MGs including their layered control architecture, single vs. networked structure, and topology optimization. Perspectives on the ongoing challenges and viable AI solutions to MG operation and control are presented.

IP Traffic Modelling in Scalable Drone Network for Carrying Scalable Logistic Operations

This study presents a self modelling method for control system of multi-coupled non-linear scalable drones based logistic services with the help of second-order dynamics of communicating membrane system by realizing consensus. This usually requires a layered multiple network control architecture designed mainly for coordinating access of various unmanned aerial vehicles to control airspace, package delivery, rescue, traffic surveillance and more. Here, the communicating membrane system is modelled based on the theory of fractional differential equations. At every interval of the sampling time, all the subsystems are optimally synchronized in P population system by multiset-rewriting rules including the effect of symport/antiport systems. Model of the minimal symport or antiport is also derived to ensure the universality for P systems with multiset-rewriting rules. Further research in this domain will enable the development of evolving architecture of a non-linear cyber physical system. The close loop stability and the recursive feasibility of the evolved architecture are also studied. Comparison are also drawn predictive control to prove the effectiveness of the proposed system.

A Layered Control Architecture of Sleep and Arousal

Sleep and wakefulness are promoted not by a single neural pathway but via wake or sleep-promoting nodes distributed across layers of the brain. We equate each layer with a brain region in proposing a layered subsumption model for arousal based on a computational architecture. Beyond the brainstem the layers include the diencephalon (hypothalamus, thalamus), basal ganglia, and cortex. In light of existing empirical evidence, we propose that each layer have sleep and wake computations driven by similar high-level architecture and processing units. Specifically, an interconnected wake-promoting system is suggested as driving arousal in each brain layer with the processing converging to produce the features of wakefulness. In contrast, sleep-promoting GABAergic neurons largely project to and inhibit wake-promoting neurons. We propose a general pattern of caudal wake-promoting and sleep-promoting neurons having a strong effect on overall behavior. However, while rostral brain layers have less influence on sleep and wake, through descending projections, they can subsume the activity of caudal brain layers to promote arousal. The two models presented in this work will suggest computations for the layering and hierarchy. Through dynamic system theory several hypotheses are introduced for the interaction of controllers and systems that correspond to the different populations of neurons at each layer. The models will be drawn-upon to discuss future experiments to elucidate the structure of the hierarchy that exists among the sleep-arousal architecture.

A Comparison of Six Legged ODE (Open Dynamics Engine) Based Gait control Algorithm and Standard Walking Gaits

In legged robots, there are changes in the sequence of legs and the foothold positions depending on the speed. The walking sequence, known as gait, is important for the robot's ability to move in a stable and less energy-consuming way. In this study, the previously developed ODE-based gait control algorithms for a six-legged mobile robot and the standard walking gaits were compared in terms of stability and repeatability. In the ODE-based gait control algorithm, dynamic effects are not neglected, as in standard gait patterns. Owing to this, the ODE-based gait controller has the ability to constantly balance itself against external disturbances. In order to test this feature of the control algorithm, the same comparison operations were repeated for a fixed motion scenario on a rotatable inclined floor. In this study firstly layered control architecture has explained which developed for ODE-based controllers. Thereafter, servo motor controllers, ODE based stability and ODE based gait controllers used in robot are introduced respectively. After in order to make the performance of these controllers comparable to the methods in the literature, standard gait patterns have been introduced. Following this section, the performance criteria to be used in the comparison procedures and the reasons for their selection are given. Later on, the rotatable inclined floor, which was builded in order to demonstrate the performance of the robot and controller used in the test system better, has explained. Afterwards, an experimental scenario in which the slopes are changed periodically over time was determined, with the aim of using this scenario in comparison experiments in which performed on the rotatable inclined floor. In the comparison results section, the experimental performance results shown by ODE-based gait controller and standard walking arrangements are given in detail through this test system and comparison criteria. As a result of the study, a 65 percent improvement was observed in stability of robot, both flat and inclined floor according to standard walking gaits.

Human-robot coexistence and interaction in open industrial cells

Abstract Recent research results on human–robot interaction and collaborative robotics are leaving behind the traditional paradigm of robots living in a separated space inside safety cages, allowing humans and robot to work together for completing an increasing number of complex industrial tasks. In this context, safety of the human operator is a main concern. In this paper, we present a framework for ensuring human safety in a robotic cell that allows human–robot coexistence and dependable interaction. The framework is based on a layered control architecture that exploits an effective algorithm for online monitoring of relative human–robot distance using depth sensors. This method allows to modify in real time the robot behavior depending on the user position, without limiting the operative robot workspace in a too conservative way. In order to guarantee redundancy and diversity at the safety level, additional certified laser scanners monitor human–robot proximity in the cell and safe communication protocols and logical units are used for the smooth integration with an industrial software for safe low-level robot control. The implemented concept includes a smart human-machine interface to support in-process collaborative activities and for a contactless interaction with gesture recognition of operator commands. Coexistence and interaction are illustrated and tested in an industrial cell, in which a robot moves a tool that measures the quality of a polished metallic part while the operator performs a close evaluation of the same workpiece.

On Proactive, Transparent, and Verifiable Ethical Reasoning for Robots

Previous work on ethical machine reasoning has largely been theoretical, and where such systems have been implemented, it has, in general, been only initial proofs of principle. Here, we address the question of desirable attributes for such systems to improve their real world utility, and how controllers with these attributes might be implemented. We propose that ethically critical machine reasoning should be proactive, transparent, and verifiable. We describe an architecture where the ethical reasoning is handled by a separate layer, augmenting a typical layered control architecture, ethically moderating the robot actions. It makes use of a simulation-based internal model and supports proactive, transparent, and verifiable ethical reasoning. To do so, the reasoning component of the ethical layer uses our Python-based belief–desire–intention (BDI) implementation. The declarative logic structure of BDI facilitates both transparency, through logging of the reasoning cycle, and formal verification methods. To prove the principles of our approach, we use a case study implementation to experimentally demonstrate its operation. Importantly, it is the first such robot controller where the ethical machine reasoning has been formally verified.

Biologically Guided Driver Modeling: the Stop Behavior of Human Car Drivers

This paper presents a principled approach to the modeling of human drivers—applied to stop behavior—by uniting recent ideas in cognitive science and optimal control. With respect to the former, we invoke the affordance competition hypothesis, according to which human behavior is produced by resolving the competition between action affordances that are simultaneously instantiated in response to the environment. From the theory of optimal control, we deploy motor primitives based on minimum jerk as the potential suite of actions. Furthermore, we invoke a layered control architecture, which carries out action priming and action selection sequentially, to model the biological affordance competition process. Motor output may be directed to distinct motor channels, which may be partially inhibited, e.g., to model gas pedal release saturation. Within this architecture, two types of motor units—“deceleration” acting on a gas pedal channel and “brake” acting on a brake pedal channel—are sufficient to model, with remarkable accuracy, the various phases that can be observed in human maneuvers in stopping a car, namely: gas release, gas chocked, brake, and final brake release at stop. The model is validated using experimental data collected in 16 different stop locations, from roundabouts to traffic lights. We also carry out a comparison with the well-known Intelligent Driver Model, discuss the scaling of this framework to more general driving scenarios and finally give an example application where the driver model is used, within a mirroring process, to infer the human driver intentions.

Hierarchical economic MPC for systems with storage states

Economic model predictive control (EMPC) differs from conventional tracking model predictive control by explicitly incorporating the plant economic cost into the stage cost. One particular class of systems of interest in the deployment of economic MPC are those containing storage devices such as microgrids and hybrid electric vehicles. Such systems may benefit from a two layer control architecture due to the wide range of time-scales that can be exhibited, with the first and second layers comprising a scheduling controller and the EMPC controller respectively. This, in turn, requires an alternative control system formulation since its structure differs from standard economic MPC.This paper proposes an EMPC control algorithm that is suitable for this particular two-layer problem. The proposed control algorithm ensures that feasibility is always maintained, even in the presence of a changing cost function. Existing EMPC theory is extended in order to prove stability of a set of economically optimal steady states, in a finite time setting. The proposed controller is then used in a simulation of a network connected, hybrid solar photovoltaic (PV) / battery system, and demonstrated to provide superior performance to standard EMPC.

Cooperative Intersection Support System Based on Mirroring Mechanisms Enacted by Bio-Inspired Layered Control Architecture

This paper presents a cooperative intersection support system implemented with an artificial cognitive system enacted by an agent that replicates human driver longitudinal sensorimotor control in the application domain. The engineering of the agent exploits recent ideas from Cognitive Science, among which the notion of mirroring (the agent discovering driver intentions by simulating possible human actions). The paper introduces the design principles for the agent and the following implementation. The system is evaluated with experimental data and compared to state of the art implementations that use different approaches.

Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge

Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension toward maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lane-reduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams’ individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving.

Structural Analysis and Five-Layered Control Frame Work for Underground Pipe Inspection Robot

Design of a robot generally involves various types of analyses that invariably include structural, numerical, kinematic, dynamic and experimental analysis. A robot designed as a solution for social problems would have to undergo all these stages before it is incorporated in-situ. The authors in continuation to their earlier work [1] are presenting the results of structural analysis in this paper. Initially a detailed description of the anatomy of the proposed robot is provided for clarity along with the identified design parameters. The primary design parameter ‘load to power ratio’ establishes certain geometric and structural constrains on the design of links comprising the robot. This relationship is studied in order to improve to the load to power ratio of the robot. A significant reduction in weight is achieved by working on these variables which leads to the improvement in the functional aspects too, viz., reduced torque requirement from the motor. Upon finalizing on the choice of motors and sensors the 5 layered control architecture based on statespace representation is also presented. Fuzzy logic based control algorithm is proposed to unite the layered approach depending on the task allocated to the robot.