Stability Guarantees Research Articles

Fuel Economy Optimization for Platooning Vehicle Swarms via Distributed Economic Model Predictive Control

Cooperation among multiple connected vehicles (CVs) brings swarm intelligence to our transportation systems and helps improve their performance. This study proposes a fuel economy optimization approach for a platooning vehicle swarm via distributed economic model predictive control (DEMPC). Within the DEMPC framework, each CV shares its assumed trajectory in the predictive horizon with its neighboring CVs at each control loop. With its neighbors’ and its own assumed trajectories, each CV first solves an open-loop control optimization problem for platoon formation, and then solves an open-loop economic optimization problem for direct fuel economy improvement. In particular, the optimal cost of the former optimization problem is used in the latter one to build an upperbound constraint for stability guarantee. The asymptotic convergence of assumed terminal states is given in the analysis, and the recursive feasibility of the two optimization problems are proved with an explicit constraint on the weight matrices in the open-loop control optimization problem. Based on these analyses, the asymptotic stability of the closed-loop system is finally proved through Lyapunov analysis. Numerical simulation results validate the effectiveness of the proposed approach in terms of closed-loop stability and fuel economy improvement. Note to Practitioners—This work aims to optimize the fuel economy for a swarm of vehicles running in a platoon. Existing approaches on platoon control mainly focus on the accurate tracking of the following vehicles, but this may cause aggressive control and hence lead to poor fuel economy. Therefore, this paper proposes a distributed economic model predictive control approach to explicitly optimize the fuel consumption rate of each vehicle. The proposed method can improve fuel economy and reduce communication requirements and burdens for platooning vehicle swarms. The paper assumes no communication time delay and packet drops, so the impact of unreliable communication will be studied in the future work.

Retro-RL: Reinforcing Nominal Controller With Deep Reinforcement Learning for Tilting-Rotor Drones

Studies that broaden drone applications into complex tasks require a stable control framework. Recently, deep reinforcement learning (RL) algorithms have been exploited in many studies for robot control to accomplish complex tasks. Unfortunately, deep RL algorithms might not be suitable for being deployed directly into a real-world robot platform due to the difficulty in interpreting the learned policy and lack of stability guarantee, especially for a complex task such as a wall-climbing drone. This letter proposes a novel hybrid architecture that reinforces a nominal controller with a robust policy learned using a model-free deep RL algorithm. The proposed architecture employs an uncertainty-aware control mixer to preserve guaranteed stability of a nominal controller while using the extended robust performance of the learned policy. The policy is trained in a simulated environment with thousands of domain randomizations to achieve robust performance over diverse uncertainties. The performance of the proposed method was verified through real-world experiments and then compared with a conventional controller and the state-of-the-art learning-based controller trained with a vanilla deep RL algorithm.

Gain-Scheduled Control Design Applied to Classical dc–dc Converters in Photovoltaic Systems and Constant Power Loads

This paper investigates the problem of control design for dc–dc converters, where the solution is especially suitable to address variations in the input voltage, a frequent situation in photovoltaic systems, and the problem of constant power load, where a nonlinear load is connected to the output of the converter. The proposed approach models the converters in terms of Linear Parameter-Varying (LPV) models, which are used to compute gain-scheduled robust gains. The synthesis conditions provide stabilizing controllers with an attenuation level of disturbances in terms of the H∞ norm. Moreover, the design conditions can also overcome pole locations to comply with physical application restrictions when ensuring transient performance. The validation of the controllers is made via simulation of the classical converters (buck, boost and buck-boost), showing that the proposed method is a viable and generalized control solution that works for all three converters, with guarantees of closed-loop stability and good performance.

A predictive fault tolerant control method for qLPV systems subject to input faults and constraints

In this paper, we investigate the use of Model Predictive Control (MPC) applications for quasi-Linear Parameter Varying (qLPV) systems subject to faults along the input channels. We propose a Fault Tolerant Control (FTC) mechanism based on a robust state-feedback MPC synthesis, considering polytopic inclusions. In order to alleviate the numerical burden of the robust min-max procedure, we use small prediction horizons, in such a way that the solution becomes viable for real-time systems. The FTC system is able to tolerate time-varying saturation of the actuator, which may happen due to malfunctions. Recursive feasibility and poly-quadratic stability guarantees are ensured through the synthesis of adequate terminal ingredients. Accordingly, we present a catalogue of three different LMI remedies, considering: (a) parameter-independent ingredients, (b) a parameter-dependent terms and (c) a parameter-dependent maps that take into account bounded rates of parameter variation. An autonomous driving car example is used to illustrate the performances of the proposed technique, which is compared to other MPCs from the literature. The proposed FTC method is able to ensure good performances, obtained with reduced computational demand.

EMPC with adaptive APF of obstacle avoidance and trajectory tracking for autonomous electric vehicles

In this paper, event-triggered model predictive control (EMPC) with adaptive artificial potential field (APF) is designed to realize obstacle avoidance and trajectory tracking for autonomous electric vehicles. An adaptive APF cost function is added to achieve obstacle avoidance and guarantee stability. The optimization problem for MPC is feasible by considering a special obstacle avoidance constraint. An event-triggered mechanism is proposed to reduce computational burden and ensure effectiveness of obstacle avoidance. Input and state constraints of autonomous electric vehicles are considered in both feasibility and stability by a robust terminal set. Effectiveness of both obstacle avoidance and trajectory tracking is shown by experimental results on autonomous electric vehicles.

Government's impression management strategies, trust in government and social cohesion: An evidence from Songjiang University Town, China.

Trust in government and social cohesion are crucial guarantees for long-term social stability. With the development of the Internet, cross-border flows of information have become increasingly easier, enabling more factors to influence people’s political perceptions and loyalty. This study explores the mechanism of governments’ impression management behaviors on trust in government and social cohesion using the questionnaire survey with college students in Shanghai as the research subjects. Impression management strategies are classified into promotive ones and protective ones herein based on the social psychology theory. The results indicate that both promotive and protective impression management strategies take by governments positively affect citizens’ loyalty, and loyalty positively affects citizens’ social cohesion and the level of trust in government; moreover, the level of trust in government positively affects social cohesion. These findings provide implications for boosting the sound development of China’s political society during the transition period.

Linear Tracking MPC for Nonlinear Systems—Part II: The Data-Driven Case

In this article, we present a novel data-driven model predictive control (MPC) approach to control unknown nonlinear systems using only measured input–output data with closed-loop stability guarantees. Our scheme relies on the data-driven system parameterization provided by the fundamental lemma of Willems <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> We use new input–output measurements online to update the data, exploiting local linear approximations of the underlying system. We prove that our MPC scheme, which only requires solving strictly convex quadratic programs online, ensures that the closed loop (practically) converges to the (unknown) optimal reachable equilibrium that tracks a desired output reference while satisfying polytopic input constraints. As intermediate results of independent interest, we extend the fundamental lemma to affine systems and we derive novel robustness bounds w.r.t. noisy data for the open-loop optimal control problem, which are directly transferable to other data-driven MPC schemes in the literature. The applicability of our approach is illustrated with a numerical application to a continuous stirred tank reactor.

PRODUCT QUALITY MANAGEMENT AT THE ENTERPRISE IN THE CONDITIONS OF UKRAINE’S ENTRY INTO THE EU

The peculiarities of the concept of Total Quality Management (hereinafter – TQM) in the activities of domestic enterprises in the conditions of the implementation of the European development vector of Ukraine were studied. It is indicated that the guarantee of the enterprise's activity on the basis of sustainability is its competitiveness. It is emphasized that ensuring the proper quality of the performed works and services is one of the important tasks that are solved at the enterprise within the framework of management activities. Attention is focused on the fact that in modern conditions of economic development, quality is a general set of characteristics of products (work, services), which are part of (are a component of) marketing, development, production and maintenance, with the help of which products (work, service) must meet needs at a price the consumer can afford. Quality management is defined as a continuous process of influencing production through the consistent performance of logically interrelated functions to ensure product quality. It has been investigated that effective management of the quality of goods, works and services can be ensured by creating a quality management system at the enterprise. It is noted that today in quality management at the enterprise, a certified quality management system is important, which is a guarantee of high stability of quality characteristics of products, goods and services. The quality system certificate allows the enterprise to maintain a competitive advantage in the market. It was emphasized that a significant impetus for the creation of international standards of the ISO 9000 series can be considered a significant increase in consumer demands for product quality.These requirements are regulated by relevant regulatory documentation (standards, technical conditions and regulations). It is noted that the quality management process should cover all the stages of production. It is indicated that, regardless of the direction of the activity of the business entity, the quality policy declared by the top management of the enterprise must ensure such a level of quality that guarantees the optimal profit of the enterprise. Ways to achieve this goal can be different and are determined by the general market strategy of the enterprise. It was concluded that modern quality management at the enterprise, regardless of the forms of ownership and scale of production, should optimally combine actions, methods and means that ensure, on the one hand, the production of products, goods and the provision of services that meet the current requirements and needs of the market, and on the other hand, the development of new products capable of meeting future market demands and needs.

Stable deep Koopman model predictive control for solar parabolic-trough collector field

Concentrated Solar Power plants (CSP) have the energy storage capability to generate electricity when sunlight is scarce. However, due to the highly non-linear dynamics of these systems, a simple linear controller will not be able to overcome the variable dynamics and multiple disturbance sources affecting it. In this paper, a deep Model Predictive Control (MPC) based on the Koopman operator is proposed and applied to control the Heat Transfer Fluid (HTF) temperature of a distributed-parameter model of the ACUREX solar collector field located at Almería, Spain. The Koopman operator is an infinite-dimensional linear operator that fully captures a system's non-linear dynamics through the linear evolution of functions of the state-space. However, one of the major problems is identifying a Koopman linear model for a non-linear system. Koopman eigenfunctions are involved in converting a non-linear model to a Koopman-based linear model. In this paper, a deep Long Short-Term Memory (LSTM) autoencoder is designed to calculate Koopman eigenfunctions of the solar collector field. The Koopman linear model is then used to design a linear MPC with terminal components to ensure closed-loop stability guarantees. Simulation results are utilized to show the satisfactory tracking performance of the proposed approach.

Deep learning fuzzy immersion and invariance control for type-I diabetes

In this study, a novel approach is proposed for glucose regulation in type-I diabetes patients. Unlike most studies, the glucose–insulin metabolism is considered to be uncertain. A new approach on the basis of the Immersion and Invariance (I&I) theorem is presented to derive the adaptation rules for the unknown parameters. Also, a new deep learned type-II fuzzy logic system (T2FLS) is proposed to compensate the estimation errors and guarantee stability. The suggested T2FLS is tuned by the singular value decomposition (SVD) method and adaptive tuning rules that are extracted from stability investigation. To evaluate the performance, the modified Bergman model (BM) is applied. Besides the dynamic uncertainties, the meal effect on glucose level is also considered. The meal effect is defined as the effect of edibles. Similar to the patient activities, the edibles can also have a major impact on the glucose level. Furthermore, to assess the effect of patient informal activities and the effect of other illnesses, a high random perturbation is applied to glucose–insulin dynamics. The effectiveness of the suggested approach is demonstrated by comparing the simulation results with some other methods. Simulations show that the glucose level is well regulated by the suggested method after a short time. By examination on some patients with various diabetic condition, it is seen that the suggested approach is well effective, and the glucose level of patients lies in the desired range in more than 99% h.

Available-Maneuvering-Margins-Based Ship Collision Alert System

The timing of a ship taking evasive maneuvers is crucial for the success of collision avoidance, which is affected by the perceived risk by the navigator. Therefore, we propose a collision alert system (CAS) based on the perceived risk by the navigator to trigger a ship’s evasive maneuvers in a timely manner to avoid close-quarters situations. The available maneuvering margins (AMM) with ship stability guarantees are selected as a proxy to reflect the perceived risk of a navigator; hence, the proposed CAS is referred to as an AMM-based CAS. Considering the dynamic nature of ship operations, the non-linear velocity obstacle method is utilized to identify the presence of collision risk to further activate this AMM-based CAS. The AMM of a ship are measured based on ship maneuverability and stability models, and the degree to which they violate the risk-perception-based ship domain determines the level of collision alert. Several typical encounter scenarios are selected from AIS data to demonstrate the feasibility of this AMM-based CAS. The promising results suggest that this proposed AMM-based CAS is applicable in both ship pair encounter and multi-vessel encounter scenarios. Collision risk can be accurately detected, and then a collision alert consistent with the risk severity is issued. This proposed AMM-based CAS has the potential to assist autonomous ships in understanding the risk level of the encounter situation and determining the timing for evasive maneuvers. The advantages and limitation of this proposed method are discussed.

Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

Physical human-robot collaboration is increasingly required in many contexts (such as industrial and rehabilitation applications). The robot needs to interact with the human to perform the target task while relieving the user from the workload. To do that, the robot should be able to recognize the human's intentions and guarantee safe and adaptive behavior along the intended motion directions. The robot-control strategies with such attributes are particularly demanded in the industrial field, where the operator guides the robot manually to manipulate heavy parts (e.g., while teaching a specific task). With this aim, this work proposes a Q-Learning-based Model Predictive Variable Impedance Control (Q-LMPVIC) to assist the operators in a physical human-robot collaboration (pHRC) tasks. A Cartesian impedance control loop is designed to implement a decoupled compliant robot dynamics. The impedance control parameters (i.e., setpoint and damping parameters) are then optimized online in order to maximize the performance of the pHRC. For this purpose, an ensemble of neural networks is designed to learn the modeling of the human-robot interaction dynamics while capturing the associated uncertainties. The derived modeling is then exploited by the model predictive controller (MPC), enhanced with the stability guarantees by means of Lyapunov constraints. The MPC is solved by making use of a Q-Learning method that, in its online implementation, uses an actor-critic algorithm to approximate the exact solution. Indeed, the Q-learning method provides an accurate and highly efficient solution (in terms of computational time and resources). The proposed approach has been validated through experimental tests, in which a Franka EMIKA panda robot has been used as a test platform. Each user was asked to interact with the robot along the controlled vertical z Cartesian direction. The proposed controller has been compared with a model-based reinforcement learning variable impedance controller (MBRLC) previously developed by some of the authors in order to evaluate the performance. As highlighted in the achieved results, the proposed controller is able to improve the pHRC performance. Additionally, two industrial tasks (a collaborative assembly and a collaborative deposition task) have been demonstrated to prove the applicability of the proposed solution in real industrial scenarios.

What factors affect the structural resilience of urban networks during COVID-19 epidemic? A comparative analysis in China

ABSTRACT Under the COVID-19 epidemic, the structural resilience of urban networks has become an important guarantee for social and economic stability and recovery. In this study, population mobility data and freight demand information are selected to construct urban networks. The complex network is used to describe the development level from five aspects of diversity, equilibrium, reciprocity, assortativity and transmission of network structure. And the entropy weight TOPSIS method is used to calculate the structural resilience of urban network based on the description of development level. We find that as the epidemic increases, the structure of population mobility network and freight demand network shows an evolutionary trend from ‘single-center’ to ‘dual-center’, and then to ‘polycentric’. Meanwhile, a flat network structure is conducive to higher structural resilience. Because the impact of diversity on the structural resilience of population mobility network and freight demand network is respectively 0.541 and 0.142, while the impact of equilibrium is respectively 0.440 and 0.769. The network with high diversity and equilibrium tends to form flat structure and show stronger resilience. Besides, the ‘long-range link’ in the population mobility network and the phenomenon of ‘imbalance transfer’ in the freight demand network can also help to improve the structural resilience of the network. Therefore, cities can establish a flat network through polycentric construction and cross-regional cooperation, while improving the heterogeneity of the urban network, they should also promote all-round and multi-faceted development of the network.

Adaptive finite-time impedance backstepping control for uncertain robotic systems interacting with unknown environments

This paper aims to address robot/environment interacting problems by designing an adaptive finite-time impedance controller. To deal with crucial issues regarding these sorts of problems, namely interacting with unknown environments and the system's uncertain nonlinearity, a backstepping control structure accompanied by an adaptive Taylor series system is developed. By designing a sliding surface term, two new variables are defined to form the backstepping controller to guarantee that the sliding surface converges to zero in a finite time. Also, impedance parameters are tuned adaptively using the gradient-descent algorithm inspired by a well-designed control literature. The designed controller has several useful features, such as being capable of implementing on n-DOF (degree-of-freedom) industrial manipulators, a guaranteed finite-time stability analysis, a regressor-free design, and dealing with uncertainties. Finally, a numerical simulation applied on a six-DOF manipulator and also a comparison with an impedance control are provided to show the efficiency of the designed controller.

Stochastic predictor-based leader-following control with input and communication delays

ABSTRACT We consider the leader-following control problem on connected directed graphs for stochastic linear agents in the presence of communications and actuator delays. We propose to use a distributed protocol for detecting the distance of agents from the leader and we show that by suitably using this information it is possible to solve efficiently the leader-following control problem by means of predictors, thus recovering results for the single-agent case. The proposed predictor and controller are easy to design and the delay bound that guarantees stability can be computed from closed-form expressions without resorting to LMIs.

М.М. Сперанский: особенности российских реформ

The purpose of this article is to analyze the significance and political role of M.M. Speransky as the first theorist and methodologist of Russian reforms based on a rational, scientific approach, with a focus on identifying the civilizational, mental and political specifics of Russia. The article used such methods as historical, comparative and hermeneutic. Speransky was the founder of a special conservative-liberal trend in Russia, according to which, when carrying out any reforms, it is necessary to take into account the stable monarchical, autocratic traditions of public consciousness. He considered optimal the option of "reforms from above" controlled by the supreme power and the state apparatus, with the preservation of guarantees of stability and legality of socio-political processes. Historically, the institutions of the autocratic system were considered by Speransky not as "political rudiments", but as effective "levers" and "mechanisms" for carrying out the necessary transformations. According to Speransky, the reform strategy should include an in-depth analysis of well-thought-out preparatory measures and rational consistency of all elements of the reform program. Social transformations should be cautious, gradual, step-by-step, excluding any drastic, radical steps and measures. At the same time, the most important factors were both the enlightened nature of the state power itself, and the purposeful processes of public education, political education initiated by the authorities, and the formation of a mature legal consciousness in society in the direction of the development of civil society. The conservative-liberal path of reforms under the leadership of the supreme power was opposed by M.M. Speranian revolutionary and radical-liberal methods of socio-political transformations, which have destructive, destructive consequences for the country.

An efficient application of particle swarm optimization in model predictive control of constrained two-tank system

&lt;span&gt;Despite all the model predictive control (MPC) based solution advantages such as a guarantee of stability, the main disadvantage such as an exponential growth of the number of the polyhedral region by increasing the prediction horizon exists. This causes the increment in computation complexity of control law. In this paper, we present the efficiency of particle swarm optimization (PSO) in optimal control of a two-tank system modeled as piecewise affine. The solution of the constrained final time-optimal control problem (CFTOC) is derived, and then the PSO algorithm is used to reduce the computational complexity of the control law and set the physical parameters of the system to improve performance simultaneously. On the other hand, a new combined algorithm based on PSO is going to be used to reduce the complexity of explicit MPC-based solution CFTOC of the two-tank system; consequently, that the number of polyhedral is minimized, and system performance is more desirable simultaneously. The proposed algorithm is applied in simulation and our desired subjects are reached. The number of control law polyhedral reduces from 42 to 10 and the liquid height in both tanks reaches the desired certain value in 189 seconds. Search time and apply control law in 25 seconds.&lt;/span&gt;

Performance Analysis Using Full Duplex Discovery Mechanism in 5G-V2X Communication Networks

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

Scaling beyond Bandwidth Limitations: Wireless Control with Stability Guarantees under Overload

An important class of cyber-physical systems relies on multiple agents that jointly perform a task by coordinating their actions over a wireless network. Examples include self-driving cars in intelligent transportation and production robots in smart manufacturing. However, the scalability of existing control-over-wireless solutions is limited as they cannot resolve overload situations in which the communication demand exceeds the available bandwidth. This article presents a novel co-design of distributed control and wireless communication that overcomes this limitation by dynamically allocating the available bandwidth to agents with the greatest need to communicate. Experiments on a real cyber-physical testbed with 20 agents, each consisting of a low-power wireless embedded device and a cart-pole system, demonstrate that our solution achieves significantly better control performance under overload than the state of the art. We further prove that our co-design guarantees closed-loop stability for physical systems with stochastic linear time-invariant dynamics.

France and the Treaty of Riga: The Problem of Guaranteeing the Eastern Border of Poland

Seen from Paris, the Treaty of Riga brought a welcome end to the Soviet-Polish war and completed the drawing of Europe’s new borders. However, there remained no guarantee of stability in Eastern Europe. From 1918, successive French governments supported a ‘strong Poland’ to further their strategic aims in Europe. But both French and Polish diplomatic archives reveal the crux of the problem: the Treaty of Riga could sow the seeds of future con-icts with Russia (still considered a European power). Moreover, the events of the early 1920s jeopardized the relationship between Poland and Lithuania. For these reasons France remained unwilling to guarantee the boundaries that issued from the Treaty of Riga and was similarly reluctant to support Warsaw’s regional plans.