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1.
This paper addresses a novel fuzzy adaptive control method for a class of uncertain nonlinear multi-input multi-output (MIMO) systems with unknown dead-zone outputs and immeasurable states. The immeasurable states under consideration are estimated by designing a fuzzy state observer. Based on the properties of the Nussbaum-type function, the difficulty of fuzzy adaptive control caused by the unknown dead zone outputs of MIMO nonlinear uncertain systems is overcome. The presented design algorithm not only guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, but also ensures that the outputs of the MIMO system converge to a small neighborhood of the desired outputs. The main contributions of this research lie in that the developed MIMO systems are more general, and an efficient design method of output-feedback controller is investigated for the studied MIMO systems, which is more applicable in practical environment. Simulation results illustrate the effectiveness of the proposed scheme. 相似文献
2.
In this paper, we develop a direct adaptive control framework for adaptive stabilization of the MIMO nonlinear uncertain systems, which can be represented as discrete-time normal form with input-to-state zero dynamics. The framework is Lyapunov-based and guarantees partial stability of the closed-loop systems, such that the adaptation of the feedback gains can stabilize the closed-loop system without the knowledge of the system parameters. In addition, our results show that the adaptive feedback laws can be characterized by Kronecker calculus. Two numerical examples are given to demonstrate the efficacy of the proposed framework. 相似文献
3.
This article presents a multi-lagged-input based data-driven adaptive iterative learning control (M-DDAILC) method for nonlinear multiple-input-multiple-output (MIMO) systems by virtue of multi-lagged-input iterative dynamic linearization (IDL). The original nonlinear and non-affine MIMO system is equivalently transformed into a linear input-output incremental counterpart without loss of dynamics. The proposed learning law utilizes the desired trajectory to cancel the influence from iteration-by-iteration variations, as well as additional multi-lagged inputs to improve control performance. The developed iterative estimation law is more effective and also makes estimation of the unknown parameters easier because the dynamics for each parameter to represent are decreased by dividing the system into multiple components in the multi-lagged-input IDL formulation. Moreover, the proposed M-DDAILC does not need an explicit and accurate model. It is proved to be iteratively convergent with rigorous analysis. Both a numerical example and a practical application to a permanent magnet linear motor are provided to verify the validity and applicability of the proposed method. 相似文献
4.
This paper studies the adaptive fuzzy fault-tolerant control design problem for a class of stochastic multi-input and multi-output (MIMO) nonlinear systems in pure-feedback form. The nonlinear systems under study contain unknown functions, unmeasured states and actuator faults, which are described by the loss of effectiveness and lock-in-place modes. With the help of fuzzy logic systems identifying uncertain stochastic nonlinear systems, a fuzzy state observer is established for estimating the unmeasured states. Based on the backstepping design technique with the nonlinear tolerant-fault control theory, an adaptive fuzzy output feedback faults-tolerant control approach is developed. It is proved that the proposed fault-tolerant control approach can guarantee that all the signals of the resulting closed-loop system are bounded in probability. Moreover, the observer errors and tracking errors can be regulated to a small neighborhood of the origin by choosing design parameters appropriately. A simulation example is provided to show the effectiveness of the proposed approach. 相似文献
5.
多输入多输出技术(MIMO)是多天线技术和信号处理技术中,越来越受关注的新一代关键技术。本文将该MIMO技术应用在地下矿井中,建立了矿井环境下的MIMO系统的GBDB散射和直射分量信道模型,推导了该模型的空时相关函数。仿真结果表明本文方法有效。 相似文献
6.
A semi-blind adaptive space–time equaliser (STE) has recently been proposed based on a concurrent gradient-Newton (GN) constant modulus algorithm (CMA) and soft decision-directed (SDD) scheme for dispersive multiple-input multiple-output (MIMO) systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, is used to provide a rough initial estimate of the STE's weight vector. The concurrent GN based CMA and SDD blind adaptive scheme is then adopted to adapt the STE. This semi-blind STE has a complexity similar to that of the training-based recursive least squares (RLS) algorithm. For stationary MIMO channels, it has been demonstrated that this semi-blind adaptive STE is capable of converging fast to the optimal minimum mean square error STE solution. In this contribution, we investigate the performance of this semi-blind adaptive STE operating in Rayleigh fading MIMO systems. Our results obtained show that the tracking performance of this semi-blind adaptive algorithm is close to that of the training-based RLS algorithm. Thus, this semi-blind adaptive STE offers an effective and practical means to successfully operate under the highly dispersive and fading MIMO environment. 相似文献
7.
This paper investigates the adaptive fuzzy control design problem of multi-input and multi-output (MIMO) non-strict feedback nonlinear systems. The considered control systems contain unknown control directions and dead zones. Fuzzy logic systems (FLSs) are utilized to approximate the unknown nonlinear functions, and the state observers are designed to estimate immeasurable states. By constructing a dead zone compensator and introducing a Nussbaum gain function into the backstepping technique, an adaptive fuzzy output feedback control method is developed. The proposed adaptive fuzzy controller is proved to guarantee the semi-globally uniformly ultimately bounded (SGUUB) of the closed-loop system, and can solve the control design problems of unmeasured states, unknown control directions and dead zones. The simulation results are given to demonstrate the effectiveness of the proposed control method. 相似文献
8.
《Journal of The Franklin Institute》2022,359(14):7337-7364
This work aims to design a neural network-based fractional-order backstepping controller (NNFOBC) to control a multiple-input multiple-output (MIMO) quadrotor unmanned aerial vehicle (QUAV) system under uncertainties and disturbances and unknown dynamics. First, we investigated the dynamic of QUAV composed of six inter-connected nonlinear subsystems. Then, to increase the convergence speed and control precision of the classical backstepping controller (BC), we design a fractional-order BC (FOBC) that provides further degrees of freedom in the control parameters for every subsystem. Besides, designing control is a challenge as the FOBC requires knowledge of accurate mathematical model and the physical parameters of QUAV system. To address this problem, we propose an adaptive approximator that is a radial basis function neural network (RBFNN) included in FOBC to fix the unknown dynamics problem which results in the new approach NNFOBC. Furthermore, a robust control term is introduced to increase the tracking performance of a reference signal as parametric uncertainties and disturbances occur. We have used Lyapunov's theorem to derive adaptive laws of control parameters. Finally, the outcoming results confirm that the performance of the proposed NNFOBC controller outperforms both the classical BC , FOBC and a neural network-based classical BC controller (NNBC) and under parametric uncertainties and disturbances. 相似文献
9.
This paper proposes four resource (subcarriers-and-bits) allocation methods for OFDMA-based multiuser MIMO system. We employ adaptive modulation according to the channel state information (CSI) of each user to meet the symbol error rate (SER) requirement. The first scheme is based on transmit spatial diversity (TSD), in which the vector channel with the highest gain between the base station and specific antenna at remote terminal is chosen for transmission. The second scheme assigns subcarrier to the best user and employs spatial multiplexing on the MIMO system to further enhance the throughput. The space-division multiple-access (SDMA) scheme assigns single subcarrier simultaneously to the remote terminals with pairwise “nearly orthogonal” spatial signatures. In the fourth scheme, we propose to design the transmit beamformers based on the zero-forcing (ZF) criterion such that the multi-user interference (MUI) is completely removed. Moreover, spatial multiplexing technique is jointly exploited to achieve throughput multiplication. Numerical results demonstrate that all the proposed algorithms are simple and reliable and the fourth scheme is the best since all users are allowed to share single subcarrier. 相似文献
10.
《Journal of The Franklin Institute》2019,356(8):4304-4334
In practice, many controlled plants are equipped with MIMO non-affine nonlinear systems. The existing methods for tracking control of time-varying nonlinear systems mostly target the systems with special structures or focus only on the control based on neural networks which are unsuitable for real-time control due to their computation complexity. It is thus necessary to find a new approach to real-time tracking control of time-varying nonlinear systems. In this paper, a control scheme based on multi-dimensional Taylor network (MTN) is proposed to achieve the real-time output feedback tracking control of multi-input multi-output (MIMO) non-affine nonlinear time-varying discrete systems relative to the given reference signals with online training. A set of ideal output signals are selected by the given reference signals, the optimal control laws of the system relative to the selected ideal output signals are set by the minimum principle, and the corresponding optimal outputs are taken as the desired output signals. Then, the MTN controller (MTNC) is generated automatically to fit the optimal control laws, and the conjugate gradient (CG) method is employed to train the network parameters offline to obtain the initial parameters of MTNC for online learning. Addressing the time-varying characteristics of the system, the back-propagation (BP) algorithm is implemented to adjust the weight parameters of MTNC for its desired real-time output tracking control by the given reference signals, and the sufficient condition for the stability of the system is identified. Simulation results show that the proposed control scheme is effective and the actual output of the system tracks the given reference signals satisfactorily. 相似文献
11.
《Journal of The Franklin Institute》2022,359(4):1457-1485
This paper investigates the problem of event-triggered adaptive neural network (NN) control for multi-input multi-output (MIMO) switched nonlinear systems with output and state constraints and non-input-to-state practically stable (ISpS) unmodeled dynamics. A nonlinear mapping is firstly utilized to deal with output and state constraints. Also, by developing a new switching signal with persistent dwell-time (PDT) and a switching dependent dynamic signal, the difficulty caused by some non-ISpS unmodeled dynamics is overcome. Then, a type of switching event-triggering mechanisms (ETMs) and event-triggered adaptive NN controllers of subsystems are designed, which handle the issue of asynchronous switching without requiring any known restriction on maximum asynchronous time. A piecewise constant introduced into this ETM effectively ensures a strict positive lower bound of inter-event times. Zeno behavior is thus ruled out. Finally, by proposing a novel class of switching signals with reset PDT, it is ensured that all output and state constrains are never violated and all signals of the switched closed-loop system are semi-global uniform ultimate boundedness (SGUUB). A two inverted pendulum system and a numerical example are provided for illustrating the applicability and validity of the proposed method. 相似文献
12.
This paper proposes an adaptive scheme of designing sliding mode control (SMC) for affine class of multi-input multi-output (MIMO) nonlinear systems with uncertainty in the systems dynamics and control distribution gain. The proposed adaptive SMC does not require any a priori knowledge of the uncertainty bounds and therefore offers significant advantages over the non-adaptive schemes of SMC design. The closed loop stability conditions are derived based on Lyapunov theory. The effectiveness of the proposed approach is demonstrated via simulations considering an example of a two-link robot manipulator and has been found to be satisfactory. 相似文献
13.
Dezhi Xu Yuchen Dai Chengshun Yang Xinggang Yan 《Journal of The Franklin Institute》2019,356(4):1880-1898
This study focuses on the control of islanded photovoltaic (PV) microgrid and design of a controller for PV system. Because the system operates in islanded mode, the reference voltage and frequency of AC bus are provided by the energy storage system. We mainly designed the controller for PV system in this study, and the control objective is to control the DC bus voltage and output current of PV system. First, a mathematical model of the PV system was set up. In the design of PV system controller, command-filtered backstepping control method was used to construct the virtual controller, and the final controller was designed by using sliding mode control. Considering the uncertainty of circuit parameters in the mathematical model and the unmodeled part of PV system, we have integrated adaptive control in the controller to achieve the on-line identification of component parameters of PV system. Moreover, fuzzy control was used to approximate the unmodeled part of the system. In addition, the projection operator guarantees the boundedness of adaptive estimation. Finally, the control effect of designed controller was verified by MATLAB/Simulink software. By comparing with the control results of proportion-integral (PI) and other controllers, the advanced design of controller was verified. 相似文献
14.
《Journal of The Franklin Institute》2022,359(10):4727-4750
This paper investigates the multiple model adaptive control problem of affine systems with unknown parameters. Firstly, an adaptive controller with resettable parameters and an adaptive law with projection function are designed to ensure the asymptotic tracking for the reference system and the boundedness of parameters. Secondly, a transformation of system is given to enable a finite-time parameter estimator to calculate the uncertain parameters in the system matrix and the affine item simultaneously. Then, a novel performance index to describe the error between the controlled plant and the identification model is given to orchestrate switchings among identification models aiming to choose the best one. Next, the sufficient condition of the asymptotic convergence for the system error is given. Finally, all designs are evaluated in a hardware-in-the-loop simulation platform of an aero-engine control system and compared with three other methods, the effectiveness and superiority are verified. 相似文献
15.
多入多出(MIMO)系统在发射端和接收端分别设置多副天线,采用MIMO技术可以提高信道容量和信道可靠性,降低误码率。正交频分复用(OFDM)是一种特殊的多载波传输方案,各子载波在整个符号周期上正交,各子载波信号子频谱可以互相重叠,提高了频带利用率。MIMO-OFDM技术是OFDM与MIMO技术结合形成的一种新技术,该技术是在OFDM传输系统中采用阵列天线实现空间分集,提高了信号质量。本文中全面介绍了MIMO技术和OFDM技术及两者的结合,分析了实现MIMO-OFDM技术的框架,未来的工作是如何用硬件来仿真实现这个系统。 相似文献
16.
对于具有不确定参数的Lorenz混沌系统，通过参数调节和自适应技术讨论了两个同结构Lorenz混沌系统的同步问题。自适应控制器和参数调节律均由Lyapunov稳定行理论来确定。数字仿真表明了该方法的有效性和实用性。 相似文献
17.
A vehicle system driven by two independent DC motors is presented here, one of which is used for the right wheel and the other is used for the left wheel. An adaptive compensator using Takagi-Sugeno fuzzy systems is proposed to control the vehicle system. The compensator includes an adaptive model identifier and adaptive controller. An online method is used to adjust the parameters of the identifier model to match the behavior model of the vehicle system. Then, the parameters of the identifier model are employed in a standard parallel-distributed compensator to provide asymptotically stable equilibrium for the closed-loop vehicle drive system, in which the velocity and direction angle of the vehicle are controlled. Results demonstrate that the proposed controller structure is robust to load changes and follows different trajectories very well. 相似文献
18.
The bi-directionally coupled Lorenz systems are linked to the modeling of a coupled double loop thermosyphon system where the mass momentum and heat exchange are both considered. As the key parameters of the system, known as Rayleigh numbers, increase, the system of differential equations predicts typical flow dynamics in a thermosyphon from heat conduction to time-dependent chaos. In many applications including the thermosyphon systems, there are uncertainties associated with mathematical models such as unmodeled dynamics and parameter variations. Also, under the high heat environment for a thermosyphon, there exist external disturbances quantitatively linked to the Rayleigh numbers. All these sources constitute uncertainties to the dynamical system. Our objective is to design adaptive controllers to stabilize the chaotic flow in each thermosyphon loop with unknown system parameters and existence of uncertainties. The controllers consist of a proportional controller with an adaptive gain and a wavelet network that reconstructs the unknown functions representing the uncertainties. Explicit stability bounds and adaptive laws for the control parameters are obtained so that the coupled Lorenz systems are globally stabilized. 相似文献
19.
仿生学是在认知概念上使我们得以最恰当地把握人与自然的关系，通过科学活动的现代结构体系，加以快速发展的学科。在航天科技的发展过程中，科学探索和技术应用同时存在，大量的科学问题转化为实现的方法和工程技术问题。认识的超前、系统的庞大、投入的巨大和技术转化滞后，会在技术上呈现出螺旋式上升的超前性和延迟效应。系统的复杂性和解决问题的难度，需要边缘学科、交叉学科的渗透和融入来解决创新与突破的问题。通过仿生科学对航天技术的启示分析，仿生学的发展为技术的突破和复杂问题的解决，提供了一条有效的途径和最大的可能。由航天技术的发展认识仿生科学的价值和重要性，对仿生学的发展有着重要的启示作用。 相似文献
20.
《Journal of The Franklin Institute》2014,351(12):5274-5309
This paper focuses on the problem of direct adaptive neural network (NN) tracking control for a class of uncertain nonlinear multi-input/multi-output (MIMO) systems by employing backstepping technique. Compared with the existing results, the outstanding features of the two proposed control schemes are presented as follows. Firstly, a semi-globally stable adaptive neural control scheme is developed to guarantee that the ultimate tracking errors satisfy the accuracy given a priori, which cannot be carried out by using all existing adaptive NN control schemes. Secondly, we propose a novel adaptive neural control approach such that the closed-loop system is globally stable, and in the meantime the ultimate tracking errors also achieve the tracking accuracy known a priori, which is different from all existing adaptive NN backstepping control methods where the closed-loop systems can just be ensured to be semi-globally stable and the ultimate tracking accuracy cannot be determined a priori by the designers before the controllers are implemented. Thirdly, the main technical novelty is to construct three new nth-order continuously differentiable switching functions such that multiswitching-based adaptive neural backstepping controllers are designed successfully. Fourthly, in contrast to the classic adaptive NN control schemes, this paper adopts Barbalat׳s lemma to analyze the convergence of tracking errors rather than Lyapunov stability theory. Consequently, the accuracy of ultimate tracking errors can be determined and adjusted accurately a priori according to the real-world requirements, and all signals in the closed-loop systems are also ensured to be uniformly ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness and merits of the two proposed adaptive NN control schemes. 相似文献