Robust adaptive consensus tracking for higher‐order multi‐agent uncertain systems with nonlinear dynamics via distributed intermittent communication protocol

This paper considers the robust adaptive consensus tracking for higher‐order multi‐agent uncertain systems with nonlinear dynamics via distributed intermittent communication protocol. The main contribution of this work is solving the robust consensus tracking problem without the assumption that the topology among followers is strongly connected and fixed. The focus is the problem of actuator with occasional failure inputs and communication resources constraints. A novel distributed intermittent communication framework is proposed via adaptive approach. In this framework, the underlying communication topologies switch among several directed graphs with a limited directed spanning tree rooted at a leader agent. Furthermore, by introducing a strategy of actuator fault compensation inputs, a combination of robust consensus tracking protocol is designed by the different adaptive feedback controllers. It is proved that the robust adaptive consensus tracking can be achieved by using local states information of neighboring agents if the communication retention rate condition is satisfied. Two examples are presented to demonstrate the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

Cooperative adaptive neural partial tracking errors constrained control for nonlinear multi‐agent systems

This paper considers the problem of partial tracking errors constrained for high‐order nonlinear multi‐agent systems in strict‐feedback form. In the control design, radial‐based function neural networks are utilized to identify uncertain nonlinear functions, and a cooperative adaptive dynamic surface control is proposed to avoid the explosion of complexity in the backstepping technique. Based on the minimal learning parameter technique and the predefined performance approach, a novel cooperative adaptive neural network control method is developed. The proposed controller is able to guarantee that all the closed‐loop network signals are cooperative semi‐globally uniformly ultimately bounded, and partial tracking errors confine all times within the predefined bounds. Finally, simulation example and comparative example with previous methods are given to verify and clarify the effectiveness of the new design procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

Robust neuro‐adaptive cooperative control of multi‐agent port‐controlled Hamiltonian systems

This paper presents the distributed cooperative tracking control of the multi‐agent port‐controlled Hamiltonian (PCH) systems that are networked through a directed graph. Controller is made robust against the parametric uncertainties using neural networks. Dynamics of the the proposed novel neural network tuning law is driven by both the position and the velocity errors owing to the information preserving filtering of the Hamiltonian gradient. In addition, the PCH structure of the closed‐loop system is preserved and the controller achieves the disturbance attenuation objective. Simulations are performed on a group of robotic manipulators to demonstrate the efficacy of the proposed controller. Copyright © 2015 John Wiley & Sons, Ltd.     

Output‐feedback adaptive tracking control of stochastic nonlinear systems using multi‐dimensional Taylor network

In this paper, an adaptive multi‐dimensional Taylor network (MTN) control scheme based on the backstepping and dynamic surface control (DSC) is developed to solve the tracking control problem for the stochastic nonlinear system with immeasurable states. The MTNs are used to approximate the unknown nonlinearities, and then based on the multivariable analog of circle criterion, an observer is first introduced to estimate the immeasurable states. By combining the adaptive backstepping technique and the DSC technique, an adaptive MTN output‐feedback backstepping DSC approach is developed. It is shown that the proposed controller ensures that all signals of the closed‐loop system are remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of probability. Finally, the effectiveness of the design approach is illustrated by simulation results.     

Direct adaptive control for non‐linear uncertain systems with exogenous disturbances

A direct adaptive non‐linear control framework for multivariable non‐linear uncertain systems with exogenous bounded disturbances is developed. The adaptive non‐linear controller addresses adaptive stabilization, disturbance rejection and adaptive tracking. The proposed framework is Lyapunov‐based and guarantees partial asymptotic stability of the closed‐loop system; that is, asymptotic stability with respect to part of the closed‐loop system states associated with the plant. In the case of bounded energy L₂ disturbances the proposed approach guarantees a non‐expansivity constraint on the closed‐loop input–output map. Finally, several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach. Copyright © 2002 John Wiley & Sons, Ltd.     

Average information‐weighted consensus filter for target tracking in distributed sensor networks with naivety issues

In the consensus‐based state estimation, multiple neighboring nodes iteratively exchange their local information with each other and the goal is to get more accurate and more convergent state estimation on each node. In order to improve network scalability and fault tolerance, the distributed sensor networks are desirable because the requirements of the fusion node are eliminated. However, the state estimation becomes challenging in the case of limited sensing regions and/or distinct measurement‐noise covariances. A novel distributed average information‐weighted consensus filter (AICF) is proposed, which does not require the knowledge of the total number of sensor nodes. Based on the weighted average consensus, AICF effectively addresses the naivety issues caused by unequal measurement‐noise covariances. Theoretical analysis and experimental verification show that AICF can approach the optimal centralized state estimation.     

Robust adaptive tracking control for nontriangular time-delay nonlinear systems with input dead-zone and multiple uncertainties

This article studies the robust adaptive tracking control problem of nontriangular nonlinear systems that are affected by multiple state delays rather than the input-delay. Different from the related studies, the considered systems involve input dead-zone and various uncertainties arising in the control coefficients, structure parameters, time delays, and disturbances. A new adaptive control strategy is presented by introducing a dynamic-gain-based Lyapunov-Krasovskii functional and by generalizing the tuning function method in the framework of time-delay system theory. All the states of the closed-loop system are bounded and the tracking error can be adjusted sufficiently small. In the simulation, the delayed chemical system is studied to demonstrate the validity of the strategy.     

Decentralized optimal tracking control for large-scale nonlinear systems with tracking error constraints

In this article, a decentralized optimal tracking control strategy is proposed for a class of nonlinear systems with tracking error constraints by utilizing adaptive dynamic programming (ADP). It should be noted that ADP technology cannot be directly used to solve decentralized optimal tracking problem of large-scale interconnected nonlinear system with nonzero equilibrium points, since that an infinite domain performance index function may result in an unsolvable solution. In addition, by introducing a smooth function, the constrained tracking error is transformed into an unconstrained one. Then, the error dynamics and a new infinite domain performance index function are designed, such that ADP technology can be used. Following the designed performance index function, the tracking error can be ensured within a small neighborhood of zero. Finally, the feasibility and the effectiveness of the proposed decentralized optimal control scheme are verified through two simulation examples.     

模型不确定的下肢康复机器人轨迹跟踪自适应控制

为了实现康复训练过程中高精度的轨迹跟踪控制,针对下肢康复机器人的模型参数和外界干扰等不确定性因素对其轨迹跟踪造成严重影响,提出一种模型不确定的下肢康复机器人轨迹跟踪自适应控制方法。根据所提方案,设计了相应的轨迹跟踪自适应控制器;并进行了轨迹跟踪控制仿真实验对比分析,结果表明,计算力矩控制方法在系统模型不确定时,膝关节的最大角度跟踪误差高达11.3°,髋关节最大稳态误差4.6°;而轨迹跟踪自适应控制方法在模型不确定的情况下,髋关节和膝关节的角度跟踪稳态误差均收敛于零;轨迹跟踪自适应控制方法可以显著提高下肢康复机器人轨迹跟踪的精度。     

Robust adaptive tracking control of uncertain discrete time systems

In this paper, the problem of robust adaptive tracking for uncertain discrete‐time systems is considered from the slowly varying systems point of view. The class of uncertain discrete‐time systems considered is subjected to both 𝓁_∞ to 𝓁_∞ bounded unstructured uncertainty and external additive bounded disturbances. A priori knowledge of the dynamic model of the reference signal to be tracked is not completely known. For such problem, an indirect adaptive tracking controller is obtained by frozen‐time controllers that at each time optimally robustly stabilize the estimated models of the plant and minimize the worst‐case steady‐state absolute value of the tracking error of the estimated model over the model uncertainty. Based on 𝓁_∞ to 𝓁_∞ stability and performance of slowly varying system found in the literature, the proposed adaptive tracking scheme is shown to have good robust stability. Moreover, a computable upper bound on the size of the unstructured uncertainty permitted by the adaptive system and a computable tight upper bound on asymptotic robust steady‐state tracking performance are provided. Copyright © 2005 John Wiley & Sons, Ltd.     

Decentralized adaptive multi‐dimensional Taylor network tracking control for a class of large‐scale stochastic nonlinear systems

This paper investigates the problem of adaptive multi‐dimensional Taylor network (MTN) decentralized tracking control for large‐scale stochastic nonlinear systems. Minimizing the influence of randomness and complex nonlinearity, which increases computational complexity, and improving the controller's real‐time performance for the stochastic nonlinear system are of great significance. With combining adaptive backstepping with dynamic surface control, a decentralized adaptive MTN tracking control approach is developed. In the controller design, MTNs are used to approximate nonlinearities, the backstepping technique is employed to construct the decentralized adaptive MTN controller, and the dynamic surface control technique is adopted to avoid the “explosion of computational complexity” in the backstepping design. It is proven that all the signals in the closed‐loop system remain bounded in probability, and the tracking errors converge to a small residual set around the origin in the sense of a mean quartic value. As the MTN contains only addition and multiplication, the proposed control method is more simplified and of good real‐time performance, compared with the existing control methods for large‐scale stochastic nonlinear systems. Finally, a numerical example is presented to illustrate the effectiveness of the proposed design approach, and simulation results demonstrate that the method presented in this paper has good real‐time performance and control quality, and the dynamic performance of the closed‐loop system is satisfactory.     

Autonomous decentralized voltage profile control of super‐distributed energy system using multi‐agent technology

A super‐distributed energy system is a future energy system in which a large part of its demand is fed by a huge number of distributed generators. At one time some nodes in the super‐distributed energy system behave as load, whereas at other times they behave as generator—the characteristic of each node depends on the customers' decision. In such situation, it is very difficult to regulate the voltage profile over the system due to the complexity of power flows. This paper proposes a novel control method of distributed generators that can achieve autonomous decentralized voltage profile regulation by using multi‐agent technology. The proposed multi‐agent system employs two types of agent: a control agent and a mobile agent. Control agents generate or consume reactive power to regulate the voltage profile of neighboring nodes and mobile agents transmit the information necessary for VQ‐control among the control agents. The proposed control method is tested through numerical simulations. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 164(4): 43–52, 2008; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20484     

Passivity‐based control for stabilization,regulation and tracking purposes of a class of nonlinear systems

In this paper, a new passivity‐based control (PBC) scheme based on state feedback is proposed in order to solve tracking, regulation and stabilization problems for a class of multi‐input multi‐output (MIMO) nonlinear systems expressed in the normal form, with time‐invariant parameters and locally bounded reference weakly minimum phase. For the proposed control scheme two new different state feedbacks, one non‐adaptive for the case when the system parameters are assumed to be known and the other adaptive for the case of unknown parameters, are developed. For the adaptive case it is assumed that the unknown parameters appear linearly in the equations. Analysis of the transient behaviour of the proposed control schemes is presented through the simulation of two examples. Copyright © 2006 John Wiley & Sons, Ltd.     

Double closed‐loop control with adaptive strategy for automotive engine speed tracking system

In order to improve the transient and static performances of an engine speed control system in a wide speed range, this paper presents an adaptive double closed‐loop control strategy. The control scheme possesses intake manifold pressure inner closed‐loop adaptive proportional‐integral control and engine speed outer closed‐loop adaptive proportional‐integral control for achieving the tracking precision in a wide range of speed, as well as adaptive nonlinearity and feedforward compensators for overcoming parameter uncertainty and nonlinearity. The whole closed‐loop system's stability and the speed tracking convergence are ensured theoretically by the Lyapunov stability theory and the LaSalle invariant principle. The effectiveness of the proposed control strategy is validated through the operation results on the simulator of a V6 engine exploited by the Research Committee of the Society of Instrument and Control Engineers of Japan.     

Input constrained adaptive tracking for a nonlinear distributed parameter tubular reactor

In this paper we address the problem of local λ tracking of a pre‐specified profile temperature for exothermic chemical reaction in a tubular reactor. In contrast to the various models in the literature, which are usually ordinary differential equations in finite‐dimensional spaces, we use a nonlinear distributed parameter model for both the evolution of the temperature and the reactant concentration. We show under nonrestrictive conditions that an adaptive output feedback controller achieves approximate asymptotic tracking where the pre‐specified asymptotic tracking accuracy, quantified by the design parameter λ>0, is guaranteed. Only a feasibility assumption in terms of the reference temperature and the input constraints is considered. Numerical simulations have been performed to illustrate the performance of the proposed approach. Copyright © 2009 John Wiley & Sons, Ltd.     

A new model reference adaptive control for linear time‐varying systems

Recent results on the adaptive control of linear time‐varying systems have considered mostly the case in which the range or rate of parameter variations is small. In this paper, a new state feed‐back model reference adaptive control is developed for systems with bounded arbitrary parameter variations. The important feature of the proposed adaptive control is an uncertainty estimation algorithm, which guarantees almost zero tracking error. Note that the conventional parameter estimation algorithm in the adaptive control guarantees only bounded tracking error. Copyright © 2000 John Wiley & Sons, Ltd.     

Hybrid adaptive control for non‐linear uncertain impulsive dynamical systems

A direct hybrid adaptive control framework for non‐linear uncertain hybrid dynamical systems is developed. The proposed hybrid adaptive control framework is Lyapunov‐based and guarantees partial asymptotic stability of the closed‐loop hybrid system; that is, asymptotic stability with respect to part of the closed‐loop system states associated with the hybrid plant states. Furthermore, hybrid adaptive controllers guaranteeing attraction of the closed‐loop system plant states are also developed. Finally, two numerical examples are provided to demonstrate the efficacy of the proposed hybrid adaptive stabilization approach. Copyright © 2004 John Wiley & Sons, Ltd.     

一类不确定非线性系统的自适应模糊跟踪控制

针对一类不确定非线性连续系统，用模糊系统对未知函数进行逼近的基础上，利用Lyapunov稳定性理论，提出了一种新的自适应模糊跟踪控制算法，此算法的特点是，无论取多少条模糊系统的规则，自适应学习的参数只有一个，便于实现，而且还能确保闭环系统渐近稳定，并使系统的跟踪误差为零，仿真研究表明，所提出的算法是有效的。     

Adaptive output tracking for a class of non‐linear time‐delay systems

In this paper the output tracking control problem for a class of non‐linear time delay systems with some unknown constant parameters is addressed. Such a problem is solved in the case that the non‐linear time‐delay system has full delay relative degree and stable internal dynamics. It is supposed moreover that the output and its time derivatives until n?1, where n is the length of the state vector (euclidean part), do not depend explicitly on the unknown parameters. This work is the first step towards the application of the methodologies of adaptive control for non‐linear delayless systems, based on tools of differential geometry, to non‐linear time‐delay systems too. Copyright © 2004 John Wiley & Sons, Ltd.     

Self-triggered adaptive prescribed-time tracking control for stochastic nonlinear systems

This paper proposes a self-triggered (ST) adaptive prescribed-time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one-to-one mapping and backstepping technique, an adaptive prescribed-time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed-loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.