Cooperative r-Passivity Based Control for Mechanical Systems

In this paper the state estimation techniques are investigated over customized wireless network for a continuous-time plant. It is assumed that the plant is connected to the controller over the proposed network. The feedback control over wireless networks includes limited bandwidth, time-varying and unknown delays with a high probability of data loss. Reasonably, some of these issues are deduced from the wireless networks structures. In order to deal with these problems the customized wireless network architecture is proposed for this Wireless Networked Control System (WNCS) and the problem of transmission delays and packet losses which induced by this scheme is studied. The time-varying delays of the TCP based shared network is estimated by fuzzy state estimation technique. Thereafter the kalman filtering is applied to address the problem of optimal filtering for this continuous-time plant with time-varying delays. The re-organized innovation analysis approach is applied to tackle the network induced time-varying delays. The simulation results show the applicability of the proposed approach.

The inevitable presence of non-ideal communication conditions in open wireless communication networks leads to insufficient coordination and optimization among mixed traffic flows during cloud-based centralized cooperative scheduling and control in intersection systems, thereby compromising traffic safety and operational efficiency. This situation imposes heightened requirements for coordinated operation between mixed vehicle platoons composed of conventional Human-Driven Vehicles (HDVs) and Intelligent Connected Vehicles (ICVs) under imperfect communication conditions. To address these challenges, this paper proposes a multi-vehicle model-free adaptive prediction and cooperative control method for intersection scenarios that considers time-varying delays and false data injection attacks. By employing a compact-form dynamic linearization prediction scheme and an enhanced model-free receding horizon predictive control technique, we construct a networked adaptive prediction and cooperative controller for mixed vehicle clusters. This framework enables the estimation and compensation of multi-vehicle state information within mixed vehicle groups, followed by the distribution of calculated adjustment speeds to target controlled ICVs. The proposed approach guides mixed vehicle platoons to achieve dynamic formation adjustments in response to environmental variations while enhancing coordinated control performance under time-varying delays and false data attacks through regulation of vehicle queue speeds. Simulation results verify that this methodology effectively improves the traffic efficiency at intersections based on safety.

In the field of systems and control, many cooperative control problems of multi-agent systems have been actively studied in the past two decades. This article aims to organize extensive existing work on different cooperative control problems into three categories, based on three different types of graph Laplacian matrices involved. A Laplacian matrix is an important representation of graph topology, and depending on the field of its entries, there are three types: ordinary Laplacian (non-negative diagonal entries and non-positive off-diagonal entries), signed Laplacian (arbitrary real entries), and complex Laplacian (arbitrary complex entries). Each type of graph Laplacian is useful in modeling and solving a different set of cooperative control problems. In particular, their algebraic properties are fundamental in characterizing stability and performance of the respective solution algorithms. To our best knowledge, organizing the literature on multi-agent cooperative control through the lens of different graph Laplacians is new.

In this paper, we study cooperative control problems with a multi-UAV system. Specifically, we propose a c consensus-based cooperative formation control strategy with collision-avoidance capability for a group of multiple unmanned aerial vehicles (UAVs). To cooperatively fly in formation, a consensus-based algorithm and a leader-follower structure are applied to the UAVs. Collisions among the UAVs can occur while they are flying with the cooperative control algorithm. Therefore, we describe how the cooperatively controlled UAVs avoid collisions while they are flying to desired position for formation. The collision-avoidance strategy is based on an artificial potential approach. The convergence is guaranteed even when the cooperative formation control algorithm and the collision-avoidance control algorithm are simultaneously applied to the quadrotors. Finally, the proposed approach is validated by some simulations.

Input-to-state stable cooperative attitude regulation of spacecraft formation flying

We introduce a mathematical model for cooperative control problems for multiple uninhabited autonomous vehicles (UAVs). Then, we introduce a computationally tractable “distributed agreement” approach to incorporate real-time decision making into a cooperative control problem. We also identify a type of optimal agreement strategy, discuss its complexity, and compare, via Monte Carlo simulations, its performance to our computationally tractable “heuristic” approach. This shows that our heuristic approach compares favorably with an optimal solution, yet is scalable for more sophisticated cooperative control problems.

ABSTRACTThis paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.

This paper addresses the problem of adaptive tracking control for a class of stochastic nonlinear systems with time-varying input delays, and the nonlinear functions of the systems are with not only the unknown parameters but also the unknown state time-varying delays, which is different from the previous work. In this paper, through a state transformation, the system can be easily transformed into a system without the time-varying input delay; the appropriate Lyapunov–Krasovskii functionals are used to compensate the unknown time-varying delay terms, and the quadratic functions instead of the quartic functions often utilized in the existing results are used as Lyapunov functions to analyze the stability of systems and the hyperbolic tangent functions are introduced to deal with the Hessian terms. Fuzzy logic systems (FLSs) in Mamdani type are used to approximate the unknown nonlinear functions. Then, based on the backstepping technique, the adaptive fuzzy controller is designed. The three main advantages of the developed scheme are that (i) unlike the existing results which deal with the nonlinearly parameterized functions by using the separation principle, the nonlinearly parameterized functions are lumped into the continuous functions which can be approximated by using the FLS; (ii) the number of the adjusted parameters only depend on the order of the investigated systems, which can reduce the computational burden greatly; and (iii) the existence of the time-varying input delay such that the controller design becomes much more difficult, and in this paper, it can be dealt with by using an appropriate state transformation. It is proven that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded in probability, whereas the tracking error converges to a small neighborhood of the origin. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Aiming at the problem of cooperative speed control of dual motors in the wire stranding mechanism of overhead ground wire repair robots, this paper establishes a dynamic model of two-phase hybrid stepping motors considering unbalanced external load disturbances. A Linear Active Disturbance Rejection Controller is adopted to realize high-precision speed control of the two motors, and the traditional cross-coupling control structure is improved based on fuzzy control. An adaptive synchronization compensator is designed to dynamically compensate the speed error of the two motors, thereby effectively reducing the synchronization error and improving the cooperative control performance and anti-disturbance ability of the system.

In this paper, the cooperative control problem in the presence of communication time delays is addressed for a broad class of multi-input-multi-output dynamical systems in the canonical form with arbitrary relative degrees. A new sampled-data predictive cooperative control is proposed by only using the delayed output feedback information and a set of less restrictive conditions on the connectivity of time varying sensor/communication networks are established. No fixed leader or time invariant sensor/communication networks are assumed. In particular, through the elaborated switching between the decoupled control and the proposed predictive cooperative control, the convergence of the overall system is rigorously proved under a mild condition on the choice of sampling period. Simulation results on the cooperative consensus for a group of mobile robots are provided to illustrate the effectiveness of the proposed approach

We present a view of cooperative control using the language of learning in games. We review the game-theoretic concepts of potential and weakly acyclic games, and demonstrate how several cooperative control problems, such as consensus and dynamic sensor coverage, can be formulated in these settings. Motivated by this connection, we build upon game-theoretic concepts to better accommodate a broader class of cooperative control problems. In particular, we extend existing learning algorithms to accommodate restricted action sets caused by the limitations of agent capabilities and group-based decision making. Furthermore, we also introduce a new class of games called sometimes weakly acyclic games for time-varying objective functions and action sets, and provide distributed algorithms for convergence to an equilibrium.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

While control theory for bilateral teleoperation systems under constant time communication delay is well developed, the research on time-varying communication delay systems is still ongoing. Most wave-based approaches proposed in literature to deal with time-varying delay appear too conservative resulting in high degradation from the constant time delay case. We have already proposed a bilateral control scheme with energy balance monitoring over packet switched communication networks. In the previous work, we dealt with a simplify situation where the time-varying was in the communication channel from master to the slave. However, we did not implement and test the system with a more complex scenario in which the delay was in both communication paths. The paper presents a complete bilateral teleoperation control scheme to compensate nonlinearity introduced by an Internet like communication channel. The system implements intelligent strategies to maintain the system passivity while it deals with position drift compensation, packets loss and blackouts management. Experimental results using the proposed control scheme are shown.

This chapter provides an overview of topics, technical challenges, and methods involved in cooperative control for multiple air vehicles. This is a broad area, with many subtopics and technical disciplines potentially involved. Subtopics of cooperative control include: cooperative motion and formation control, cooperative search and area coverage, task assignment and scheduling, and cooperative task execution. The particular technical challenges involved can vary depending on the exact type of cooperative control problem discussed, but certain difficulties are ubiquitous, particularly uncertainty, limited communication, and task coupling. This discussion focuses on technical challenges and solution methods related to assignment and scheduling problems for cooperative teams.

Markovian jumping systems have arisen naturally in the mathematical modeling of phenomena spanning disciplines in the social sciences, natural sciences, and engineering. This kind of stochastic dynamical systems can be employed to model the dynamics when parameters are subject to random abrupt changes due to sudden environment changes, subsystem switching, system noises, executor faults, and so forth. Much attention has been given to modeling, optimization, and real applications of such stochastic dynamical systems in the literature in recent years. As the advanced control and optimization will provide a basis for the design and application of such stochastic systems, these advanced techniques would result in substantial and sustainable benefits. The accepted papers in this special issue include stochastic stability, stabilization, stochastic control optimization, system modeling and identification methods, predictive control, signal processing, robust filtering, multiagent systems, networked control systems, time-delayed systems, neural networks, the Takagi-Sugeno fuzzy systems, simulated annealing, and fault detection methods.