Control Input Information Research Articles

Event-triggered distributed predictive cooperation control for multi-agent systems subject to bounded disturbances

This paper presents an event-triggered distributed predictive cooperation strategy for dynamically decoupled subsystems subject to bounded disturbances. Different from the traditional cooperative control using coupled constraints related to the agent’s state information, a synchronization parameter related to the agent’s control input information is introduced to design the parameterized synchronization constraint for the cooperation of multi-agent subsystems. An event-triggering condition involving the error between the system state and its optimal prediction is first designed for each agent, and the event-triggered distributed predictive control algorithm that is established on the triggering mechanism and the dual-mode approach is then designed. In such a framework, the distributed optimization problem is solved, and the parameterized information is exchanged between agents only when the triggering condition is satisfied, reducing the computation and communication load more efficiently. Moreover, the theoretical results guaranteeing the feasibility and closed-loop stability are developed. Finally, a formation control example for multiple mobile robots is given to verify the effectiveness of the proposed strategy.

Quantized-Output-Based Exponential Stabilization of Fuzzy DPSs With Time-Varying Delay and Nonlinearities

The exponential stabilization of nonlinear distributed parameter systems (DPSs) with time-varying delay is studied in this paper. With the employment of the Takagi-Sugeno (T-S) fuzzy model, the nonlinear DPSs are described as a fuzzy parabolic partial differential equation (PDE). Then, with the consideration of the measurement output and control input information quantized by a kind of dynamic quantizers, fuzzy static and dynamic output feedback controllers are proposed. The time-delayed feedback controllers are also provided in the forms of static and dynamic. On the basis of the derived T-S fuzzy PDE model, Lyapunov-based control design strategies are proposed with respect to the effect of quantization. Moreover, by constructing appropriate Lyapunov functionals, sufficient conditions for designing the output feedback control gains and the quantizers' adjusting parameters which guarantee the exponential stability of the fuzzy closed-loop system are provided in terms of standard linear matrix inequalities (LMIs). Finally, we explore the output feedback controllers into the FitzHugh-Nagumo (FHN) equation and Fisher equation as applications. The efficiency of the design strategies is indicated through the simulation results.

Appointed-Time and Attack-Free Bipartite Synchronization of Generic Linear Multiagent Systems Over Directed Switching Networks

This article derives a solution to the appointed-time and attack-free bipartite synchronization problem for generic linear multiagent systems over directed switching networks accommodating cooperative and antagonistic interactions. Herein, appointed-time means that the settling time is independent of any parameters and is preappointed in advance. Attack-free requires that the communication channel is protected, which means that no state and observer information exchange is allowed, and only measured relative output information is available when designing protocols. Firstly, by virtue of Pontryagin’s maximum principle, a distributed appointed-time state-feedback protocol is developed for directed switching networks, which tackles the appointed-time bipartite synchronization problem from a motion-planning approach based on sampling measurements, but takes the risk of facing cyber attacks on information exchange channels. Then, to be free of network attacks, a sampling-based attack-free observer using relative output information is designed to observe bipartite synchronization errors at an appointed time. By combining the appointed-time state-feedback protocol with the sampling-based attack-free observer, an attack-free output-feedback protocol is developed without requiring the state or control input information. The attack-free appointed-time bipartite synchronization problem is thus resolved. Both the state-feedback and attack-free protocols show the ability of disturbance rejection to the nonidentical bounded disturbances. To our knowledge, it is the first time to solve the appointed-time and attack-free bipartite synchronization problem for generic linear multiagents based on state- and output-feedback information, respectively. Finally, simulations verify the effectiveness of the two protocols.

Consensus for Second-Order Multiagent Systems Under Two Types of Sampling Mechanisms: A Time-Varying Gain Observer Method.

This article considers the consensus of second-order multiagent systems (MASs) under a directed communication topology. By introducing time-varying gains into observers, two types of novel continuous-discrete-time observers are presented to estimate state information of agents under two sampling mechanisms, respectively, namely: 1) a synchronous nonuniform sampling (SNS) mechanism and 2) an asynchronous nonuniform sampling (ANS) mechanism. A new design method of the time-varying gain observer is proposed, in that it can allow for one to select a consensus protocol with lower cost observers to attain the consensus task. Only sampled position information needs to be transmitted in designing consensus protocols for the MSAs, and neither velocity information nor control input information is required. Under the SNS mechanism, a consensus condition is obtained and it is shown that the introduction of time-varying gains in the observers indeed can improve their convergence. Under the ANS mechanism, the corresponding consensus protocol allows the sampling and transmission of different agents to be asynchronous and aperiodic, which means that each agent may sample and transmit its information in line with its own sampling requirements. Furthermore, each sampling period can be arbitrarily selected within a certain range while ensuring the consensus of the MASs. Finally, numerical examples are given to illustrate the effectiveness of the obtained results.

Consensus-based iterative learning of heterogeneous agents with application to distributed optimization

This paper deals with the distributed iterative learning control (ILC) problem of leaderless consensus in a network of heterogeneous nonlinear agents. For a general case that the topology graph is dynamically changing with respect to both iteration and time axes, an ILC-based consensus protocol is designed for each agent by utilizing its control input and neighboring information from the last iteration. It is shown that under a basic joint spanning tree condition, the states of all agents can exponentially agree on a common trajectory along the iteration axis. Interestingly, by the appropriate design of the agents’ dynamics, it is found that the consensus trajectory and the network state can approach the unique optimal point of a convex optimization problem as the time evolves. Simulations demonstrate the validity of the proposed algorithms and theoretical analysis.

Q-Learning for Feedback Nash Strategy of Finite-Horizon Nonzero-Sum Difference Games.

In this article, we study the feedback Nash strategy of the model-free nonzero-sum difference game. The main contribution is to present the Q -learning algorithm for the linear quadratic game without prior knowledge of the system model. It is noted that the studied game is in finite horizon which is novel to the learning algorithms in the literature which are mostly for the infinite-horizon Nash strategy. The key is to characterize the Q -factors in terms of the arbitrary control input and state information. A numerical example is given to verify the effectiveness of the proposed algorithm.

On low-complexity control design to spacecraft attitude stabilization: An online-learning approach

This paper studies the spacecraft attitude stabilization problem with external disturbances. A new control scheme entitled online-learning control is proposed to achieve a robust, accurate, and simple-structure control algorithm. Compared with the conventional control design, an obvious distinction of the online-learning control algorithm is that it together utilizes the previous control input information and the system's current state information, as if learning experience from previous control input. In contrast, the conventional control scheme does not fully use the existing information and chooses to discard the previous control input information when generating control instructions. Due to the learning strategy, the utility of adaptive- or observer-based tools can be avoided when designing a robust control law, making a simple, effective algorithm, moreover saving system resources. The proposed control law can stabilize the attitude system by achieving the uniformly ultimately bounded convergence.

Distributed fixed-time attitude coordinated tracking for multiple rigid spacecraft via a novel integral sliding mode approach

In this paper, the distributed fixed-time attitude coordinated tracking control problem is investigated for multiple rigid spacecraft in the presence of external disturbances. A novel integral sliding mode control scheme is proposed for second order systems with external disturbances, which provides bounded convergence time independent of initial conditions. By constructing a sliding-mode estimator for each spacecraft, the requirement of the neighbours’ control input information is removed, which reduces the communication flow. A fixed-time disturbance observer is introduced to compensate external disturbances. Then, the composite attitude tracking control laws are developed to guarantee tracking errors converge to zero in fixed time. The fixed-time stability of the closed-loop system is proven by employing the Lyapunov method. Finally, the performance of the proposed control approach is illustrated by numerical simulations.

Distributed Consensus Tracking of Networked Agent Systems Under Denial-of-Service Attacks

Distributed consensus tracking problem of networked agent systems with directed topologies under denial-of-service (DoS) attacks is investigated. The considered networked agent network consists of a physical layer with fixed physical links and a cyber layer with cyber control units. Both the communication network connecting these two layers and the cyber communication network within the cyber layer may subject to malicious DoS attacks. These two types of attacks have different impacts on the networked system; the former one affects the timely update of control inputs, and the latter influences the connection weights of the cyber communication graph. First, for DoS signals occurring in the communication network which transmits the state and control input information between the two layers, the distributed control protocol based on the event-triggered scheme and locally deployed estimators are designed. Efficient algorithms for selecting event-triggered control parameters and Zeno-free triggered parameters are given to ensure the mean-square consensus. The relationships between the system's parameters and the features of DoS attacks are successfully revealed. Second, corresponding theoretical analysis is derived for consensus tracking of the networked systems when DoS attacks are launched within the cyber layer. Conditions concerning the length of repairing time and indexes of DoS attacks are given by utilizing hybrid control theory. At last, the effectiveness of the obtained results is demonstrated by performing simulations on multirobot systems.

A predictive path-following controller for multi-steered articulated vehicles

Stabilizing multi-steered articulated vehicles in backward motion is a complex task for any human driver. Unless the vehicle is accurately steered, its structurally unstable joint-angle kinematics during reverse maneuvers can cause the vehicle segments to fold and enter a jack-knife state. In this work, a model predictive path-following controller is proposed enabling automatic low-speed steering control of multi-steered articulated vehicles, comprising a car-like tractor and an arbitrary number of trailers with passive or active steering. The proposed path-following controller is tailored to follow nominal paths that contains full state and control-input information, and is designed to satisfy various physical constraints on the vehicle states as well as saturations and rate limitations on the tractor’s curvature and the trailer steering angles. The performance of the proposed model predictive path-following controller is evaluated in a set of simulations for a multi-steered 2-trailer with a car-like tractor where the last trailer has steerable wheels.

A distributed leader‐following tracking controller using delayed control input information from neighbors

SummaryIn this paper, we propose a simple, continuous, and distributed controller for the second‐order multiagent system to achieve leader‐following trajectory tracking, by exploiting the control input information of neighbors (CIIN) and using proportional‐derivative (PD) control in terms of local neighborhood synchronization error. A constant time delay is introduced in the CIIN as a design parameter to avoid the algebraic loop issue arising from the control input coupling. We develop an easily testable condition on the PD gains to ensure that the resulting neutral‐type error system is input‐to‐state stable for an arbitrary bounded delay, and prove that when the leader's acceleration is a Lipschitz continuous function with respect to time, the ultimate bound of tracking errors is strictly increasing with respect to the introduced time delay. Moreover, we analyze the robustness of the controller with respect to model uncertainties and show its potential advantages over two existing controllers in balancing the steady‐state tracking precision, the communication cost, and the continuity of controller signal. Finally, extensive simulations are conducted to show the effect of the delay on system stability, to verify the condition on PD gains, to confirm the robustness of the controller, and to demonstrate the detailed advantages.

Active Disturbance Rejection Controller Based Time-Varying Formation Tracking for Second-Order Multi-Agent Systems With External Disturbances

In the current paper, time-varying formation tracking (TVFT) problems for second-order multi-agent systems with external disturbances are studied. Based on the active disturbance rejection controller, a robust TVFT protocol is proposed, which contains the disturbance compensation. To restrain the influence of external disturbances, an extended state observer is designed to estimate the disturbance compensation using the control input and output information of the system. For the case that the desired formation trajectories are known, an approach to give the time-varying formation with a stationary transient process is proposed by constructing a tracking-differentiator, which actually plan a motion taking the shortest time along the desired trajectories. An algorithm is designed to determine the control parameter matrix of the protocol, in which the formation tracking feasibility condition is given. Finally, to verify the theoretical results, two numerical simulation examples are presented.

Robust Quantized Consensus of Discrete Multi-Agent Systems in the Input-to-State Sense

In this paper, we consider the quantized consensus problem of multiple discrete-time integrator agents which suffer from additive noise. Due to the limited communication resources, each agent can only exchange quantized information of finite length with its neighbors through unreliable channels, where the transmitted signals may be delayed or even lost. Quantization errors, network delay, and packet loss result in quantization mismatch. To handle the additive noise and quantization mismatch, a quantized consensus protocol is proposed by implementing dynamic encoding and decoding policies at a finite bit rate. Particularly, the proposed consensus protocol introduces an internal saturation function into all the controllers of agents so that the control inputs of neighbor agents can be predicted locally by each agent even under distributed control law and quantization mismatch. Based on such shared control input information, the proposed consensus protocol can guarantee the achievement of the approximate consensus of all agents in the input-to-state sense, i.e., the consensus errors of agents are bounded from above by the sum of a class $\mathcal {K}_\infty $ function of the upper bound of the additive noise and a class $\mathcal {KL}$ function of the upper bound on the initial states of agents. It is shown that such approximate consensus in the input-to-state sense can be guaranteed at an as low bit rate as 1 bit per time step for each agent without requiring any global information, except an upper bound of the number of agents. Two specific situations, namely packet loss, and network delay, are further analyzed with the explicit expressions of quantization mismatch. The simulations are done to confirm the effectiveness of the proposed quantized consensus protocol.

A distributed approach to model-predictive control of radiant comfort delivery systems in office spaces with localized thermal environments

This paper introduces a new multi-agent system approach to optimal control of high performance buildings and presents algorithms for both distributed system identification and distributed model predictive control (DMPC). For the system identification, each thermal zone is divided into sub-systems, and a parameter set for each sub-system is first estimated individually, and then integrated into an inverse model for the whole thermal zone using the dual decomposition algorithm. For the DMPC, a distributed optimization algorithm inspired by the Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM) is deployed and multiple MPCs run iteratively while exchanging control input information until they converge. The developed algorithms are tested using field data from an occupied open-plan office space with a radiant floor system with distributed sensing, control, and data communication capabilities for localized comfort delivery. With this tractable approach, agents solve individual optimization problems in parallel, through information exchange and broadcasting, with a smaller scale of the input and constraints, facilitating optimal solutions with improved efficiency that are scalable to different building applications. Using a data-driven model and weather forecast, the DMPC controller is implemented to optimize the operation of an air-cooled chiller while providing different operative temperature bounds for each radiant floor loop. The radiant comfort delivery system with predictive control is capable of providing localized thermal environments while achieving significant energy savings. For the system and climate under consideration, results from the building operation during the cooling season, show 27% reduction in electricity consumption compared to baseline feedback control.

Reinforcement Q-learning based on Multirate Generalized Policy Iteration and Its Application to a 2-DOF Helicopter

In this paper, we propose a novel Q-learning method based on multirate generalized policy iteration (MGPI) for unknown discrete-time (DT) linear quadratic regulation (LQR) problems. Q-learning is an effective scheme for unknown dynamical systems because it does not require any knowledge of the system dynamics to solve optimal control problems. By applying the MGPI concept, which is an extension of basic GPI with multirate time horizon steps, a new Q-learning algorithm is proposed for solving the LQR problem. Further, it is proven that the proposed algorithm converges to an optimal solution i.e., it learns the optimal control policy iteratively using the states and the control-input information. Finally, we employ the two degree-of-freedom helicopter model to verify the effectiveness of the proposed method and investigate its convergence properties.

Layered event-triggered control for group consensus with both competition and cooperation interconnections

In this paper, the problem of group consensus of multi-agent systems that exchange information through two-layer directed communication network is investigated. Both competition and cooperation interconnections are considered, which is more in line with the reality. Based on layered event-triggered control strategy, agents only update control input and broadcast information to their neighborhood in discrete time instants according to the designed triggering conditions. Since the triggering conditions within two layers are different, the whole consensus process is asynchronous. We prove that the agents can achieve group consensus with the proposed control protocol and there is no Zeno behavior by choosing appropriate parameters.

Robust Time-Delayed Control of a Class of Uncertain Nonlinear Systems

Abstract In this paper, the tracking control problem of a class of uncertain nonlinear systems is addressed. The conventional Time-Delayed Control (TDC) approach, as reported in literature, does not provide any selection criterion for sampling interval and controller gains. This design issue of TDC is solved in this paper through a new stability approach based on the Lyapunov-Krasvoskii method and a relation between sampling interval and controller gains has been established. Moreover, a new Robust Time-Delayed Control (RTDC) strategy has been proposed to negotiate the time-delayed error that arises from the approximation of uncertainties in TDC. The RTDC methodology, unlike the existing control methodologies, does not require any predefined bound of the uncertainties. The switching gain of RTDC attempts to circumvent the approximation error through the control input and feedback information of past instances. The maximum allowable length of past data is evaluated from the stability analysis of TDC. Experimental result of the proposed methodology using a nonholonomic wheeled mobile robot (WMR) is presented and improved tracking accuracy of the proposed control law is noted compared to time-delayed control.

Fuzzy-Model-Based Fault Detection for a Class of Nonlinear Systems With Networked Measurements

This paper is concerned with the fuzzy-model-based fault detection for a class of nonlinear systems with networked measurements where there are significant uncertainties on information. A unified model is proposed to capture four sources of these uncertainties, namely, the sensor saturation, the signal quantization, the general medium access constraint, and the multiple packet dropouts. A simultaneous consideration of these issues reflects the practical networked systems much more closely than the existing works. The goal of this paper is to design a fault detector such that, for all unknown input, control input, and uncertain information, the estimation error between the residual and the fault is minimized. Using the switched system approach and some stochastic analyses, a sufficient condition for the existence of desired fault detector is established and the fault detector gains are computed by solving an optimization problem. Two numerical examples are given to show the effectiveness of the proposed design.

Containment control of multi-agent systems by exploiting the control inputs of neighbors

SUMMARY This paper studies the containment control problem for multi-agent systems consisting of multiple leaders and followers connected as a network. The objective is to design control protocols so that the leaders will converge to a certain desired formation while the followers converge to the convex hull of the leaders. A novel protocol is proposed by exploiting the control input information of neighbors. Both continuous-time and discrete-time systems are considered. For continuous-time systems, it is proved that the protocol is robust to any constant delays of the neighbors' control inputs. For discrete-time systems, a sufficient condition on the feedback gain for the containment control is given in terms of the time delay and graph information. Some numerical examples are given to demonstrate the results. Copyright © 2013 John Wiley & Sons, Ltd.

Synchronization of multi-agent systems with delayed control input information from neighbors

In this paper, we consider the synchronization of continuous multi-agent systems with integrator node dynamics. We comparatively study two control protocols, a standard local voting protocol, and a protocol that includes information about the control inputs of neighbors. It is shown that using control input information from neighbors improves performance in two aspects. First, the performance is improved with respect to a certain quadratic performance index defined herein and second, speed of convergence can be selected arbitrarily, independently of the graph topology. Realistically, control input information from neighbors can only be received after a time delay. It is therefore shown that the second protocol using time-delayed control input information from neighbors guarantees the synchronization of the multi-agent system for any nonnegative delay. Furthermore, it is shown that the convergence rate using time-delayed control inputs can still be selected with considerable freedom, and remains superior to the performance of the standard local voting protocol which depends on the graph topology. Some numerical examples and experiments are given to demonstrate the effectiveness of the new protocols.