Output‐feedback co‐ordinated decentralized adaptive tracking: The case of MIMO subsystems with delayed interconnections

Exact decentralized output‐feedback Lyapunov‐based designs of direct model reference adaptive control (MRAC) for linear interconnected delay systems with MIMO subsystems are introduced. The design process uses a co‐ordinated decentralized structure of adaptive control with reference model co‐ordination which requires an exchange of signals between the different reference models. It is shown that in the framework of the reference model co‐ordination zero residual tracking error is possible, exactly as in the case with SISO subsystems. We develop decentralized MRAC on the base of a priori information about only the local subsystems gain frequency matrices without additional a priori knowledge about the full system gain frequency matrix. To achieve a better adaptation performance we propose proportional, integral time‐delayed adaptation laws. The appropriate Lyapunov–Krasovskii type functional is suggested to design the update mechanism for the controller parameters, and in order to prove stability. Two different adaptive DMRAC schemes are proposed, being the first asymptotic exact zero tracking results for linear interconnected delay systems with MIMO subsystems. Copyright © 2005 John Wiley & Sons, Ltd.     

Output feedback adaptive fault-tolerant compensation tracking control for linear systems based on the closed-loop reference model

This paper investigates the problem of output feedback adaptive compensation tracking control for linear systems subject to external disturbances and actuator failures including loss of effectiveness faults and bias faults. The impact of actuator faults on the transient performance of systems can be mitigated predicated on the closed-loop reference model with an additional degrees of design freedom. Using the estimation information provided by the adaptive mechanism, an output feedback adaptive fault-tolerant control strategy is developed to track closed-loop reference model systems. It is shown that all the signals of the resulting closed-loop system are bounded. Finally, simulation results are given to demonstrate the effectiveness of the proposed fault-tolerant tracking control method.     

Adaptive control strategy‐based reference model for spacecraft motion trajectory

In aerospace field, the economic realization of a spacecraft is one of the main objectives that should be accomplished by conceiving the optimal propulsion system and the best control algorithms. This paper focuses on the development of a viable adaptive control approach (ACA) for spacecraft motion trajectory (SMT). The proposed strategy involves the nonlinear mathematical model of SMT expressed in the central field, which is linearized by the Taylor expansion, and the second Lyapunov method to offer a high rate and unfailing performance in the functioning. The adaptive control system is composed of the cascade of adaptation loop and feedback control loop. When the spacecraft deviates from its reference trajectory model, the ACA acts on the control system to correct this deviation and follow the optimal reference trajectory. Therefore, when the states of the adjustable model are different from its reference values, then the error signal is provided as an input to the adaptation law, which contains the adaptation algorithm. The output will be the state variable feedback control matrix, which will be used to calculate the new control law vector. The efficiencies of the linearization procedure and the control approach are theoretically investigated through some realistic simulations and tests under MATLAB. The steady‐state errors of control between the reference model and the adjustable model of SMT converge to zero. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive robust output‐feedback motion control of hydraulic actuators

Most previous advanced motion control of hydraulic actuators used full‐state feedback control techniques. However, in many cases, only position feedback is available, and thus, there are imperious demands for output‐feedback control for hydraulic systems. This paper firstly transforms a hydraulic model into an output feedback–dependent form. Thus, the K‐filter can be employed, which provides exponentially convergent estimates of the unmeasured states. Furthermore, this observer has an extended filter structure so that online parameter adaptation can be utilized. In addition, it is a well‐known fact that any realistic model of a hydraulic system suffers from significant extent of uncertain nonlinearities and parametric uncertainties. This paper constructs an adaptive robust controller with backstepping techniques, which is able to take into account not only the effect of parameter variations coming from various hydraulic parameters but also the effect of hard‐to‐model nonlinearities such as uncompensated friction forces, modeling errors, and external disturbances. Moreover, estimation errors that come from initial state estimates and uncompensated disturbances are dealt with via certain robust feedback at each step of the adaptive robust backstepping design. After that, a detailed stability analysis for the output‐feedback closed‐loop system is scrupulously checked, which shows that all states are bounded and that the controller achieves a guaranteed transient performance and final tracking accuracy in general and asymptotic output tracking in the presence of parametric uncertainties only. Extensive experimental results are obtained for a hydraulic actuator system and verify the high‐performance nature of the proposed output‐feedback control strategy.     

Parameter‐dependent Riccati equation–based output feedback adaptive control

A parameter‐dependent Riccati equation approach is proposed to design and analyze the stability properties of an output feedback adaptive control law design. The adaptive controller is intended to augment an existing fixed‐gain observer‐based output feedback control law. Although the formulation is in the setting of model reference adaptive control, the realization of the adaptive controller does not require implementing the reference model. In this regard, the increased complexity of implementing the adaptive controller, above that of a fixed‐gain control law, is less than that of other methods. The error signals are shown to be uniformly ultimately bounded, and an estimate for the ultimate bound is provided. The issue of sensor noise is addressed by introducing an error filter. The control design process and the theoretical results are illustrated using a model for wing rock dynamics.     

Adaptive transfer function-based control of nonlinear process. Case study: Control of temperature in industrial methane tank

The state model-based transfer function models are applied for adaptation of linear controller and disturbance compensator in a feedback/feed-forward control system of nonlinear process. An advantage of the presented adaptation method is the avoidance of artificial disturbances or iterative identification procedures for on-line estimation of process dynamic parameters. The adaptation is based on linearization of the process model at each sampling time about the current state point, independent of the process being at steady-state or transient conditions. The linear time-varying dynamics model is updated on-line using measured values of process variables and reduced to the first-order plus time delay transfer function models in order to directly apply well-developed controller tuning rules. Computational aspects of the adaptation method are discussed and computation algorithms are presented. The adaptive feedback/feed-forward control system was applied for controlling temperature in industrial methane tank, dynamic parameters of which vary in a wide range due to variations of methane-tank process load and external conditions. The heat balance-based process state model is developed and validated using observation data of real plant. Computer simulation of the proposed control system performance under extreme operating conditions demonstrates fast adaptation of controller parameters, robust behaviour and significant improvement in the controllers' performance compared to that of fixed-gain controllers. Copyright © 2007 John Wiley & Sons, Ltd.     

基于模型参考模糊自适应控制的永磁同步电机控制器设计

基于模型参考模糊自适应控制(MRFAC)方法设计永磁同步电机(PMSM)速度控制器.该控制器具有传统模型参考自适应控制构架.传统模型参考自适应控制系统中的反馈控制器和常规自适应机构分别由主模糊控制器、模糊自适应机构替代,模糊逆模型结合自适应调整算法构成的模糊自适应机构对主控制器参数进行实时调整,以达到快速适应对象参数和状态变化的目的.利用模块化建模工具Matlab/SimuIink建立PMSM控制系统模型.仿真结果表明了所设计控制器运行平稳,具有良好的动、静态特性.     

Accommodation of unknown actuator faults using output feedback‐based adaptive robust control

In this paper, we solve the problem of output tracking for linear uncertain systems in the presence of unknown actuator failures using discontinuous projection‐based output feedback adaptive robust control (ARC). The faulty actuators are characterized as unknown inputs stuck at unknown values experiencing bounded disturbance and actuators losing effectiveness at unknown instants of time. Many existing techniques to solve this problem use model reference adaptive control (MRAC), which may not be well suited for handling various disturbances and modeling errors inherent to any realistic system model. Robust control‐based fault‐tolerant schemes have guaranteed transient performance and are capable of dealing with modeling errors to certain degrees. But, the steady‐state tracking accuracy of robust controllers, e.g. sliding mode controller, is limited. In comparison, the backstepping‐based output feedback adaptive robust fault‐tolerant control (ARFTC) strategy presented here can effectively deal with such uncertainties and overcome the drawbacks of individual adaptive and robust controls. Comparative simulation studies are performed on a linearized Boeing 747 model, which shows the effectiveness of the proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

Decentralized robust adaptive-output feedback controller for power system load frequency control

In this paper, a new model reference-decentralized robust adaptive-output feedback controller is proposed for the load frequency control (LFC) of large-scale power systems with unknown parameters. This control strategy requires only local input–output data and can follow random changes in the operating conditions. The controller is designed such that the trajectory errors and the control gains of each area remain uniformly bounded. In the proposed method, firstly an adaptive observer is designed to estimate the state variables and system parameters using local data only. Then a locally linear combination of the estimated states and the model reference states are used to design a robust adaptive-output feedback controller for each area. Simulation results for a three-area power system show that the proposed controller achieves good performance even in the presence of plant parameter changes and system non-linearities. Received: 18 October 2001/Accepted: 24 October 2001     

An adaptive control scheme using multiple reference models

This paper develops an extended model reference adaptive control scheme to expand the capacity of state feedback state tracking adaptive control to handle the plant‐model matching uncertainties for single‐input LTI systems. The extended scheme is developed, using multiple reference model systems (only one of which is required to be able to match the controlled plant), and multiple controllers (which are updated from adaptive laws generated from multiple reference model systems based estimation errors), as two key features of such design to relax a plant‐model matching condition. A switching mechanism is constructed using those multiple estimation errors, capable of selecting the suitable control input from the multiple control signals, to achieve the desired system performance. An aircraft flight control example is presented to show the capacity of such design in relaxing a practical design condition. Copyright © 2013 John Wiley & Sons, Ltd.     

Improving transient performance of discrete-time model reference adaptive control architectures

Discrete-time adaptive control algorithms can be executed directly in embedded code unlike their continuous-time counterparts, which require discretization. However, their designs predicated on quadratic Lyapunov-based frameworks are quite intricate due to the resulting complexity in the Lyapunov difference expressions. Therefore, a wide array of available continuous-time results addressing transient performance issues using adaptive control algorithms cannot be applied or readily extended to the discrete-time case. In this article, we present a new model reference adaptive control architecture for discrete-time uncertain dynamical systems. Specifically, the proposed architecture consists of a command governor mechanism that adjusts the trajectory of a given command during the closed-loop transient response. It is shown that this mechanism is effective in improving transient performance of discrete-time model reference adaptive control architectures. Using a logarithmic Lyapunov function, we prove Lyapunov stability of the closed-loop system as well as asymptotic convergence of the system error states involving the difference between the states of the uncertain dynamical system and the states of the reference model, as well as driving the command governor signal to zero.     

Adaptive controller design with prescribed performance for switched nonstrict feedback nonlinear systems with actuator failures

This article is concerned with the adaptive output-feedback control of switched nonstrict feedback nonlinear systems. By introducing a novel error surface, an adaptive control strategy is proposed for the general case where the nonlinear functions and the control gain functions are unknown, and the states are unmeasurable. The considered switched nonlinear system contains unknown actuator failures, which are modeled as both loss of effectiveness and lock-in-place. In order to improve the transient performance in the presence of unknown actuator failures, the prescribed performance approach is used. The “explosion of complexity” problem is avoided through using low-pass filters. The stability of the closed-loop system under arbitrary switching is shown using Lyapunov stability theory, based on which, the tracking error is shown to converge to a small residual set with the prescribed performance bounds. The advantages of the proposed technique are verified through simulations of two numerical and practical examples.     

永磁同步电机非线性黄金分割自适应转速控制

针对永磁同步电机系统复杂设计高精确度的控制器比较困难的问题,采用id=0的矢量控制策略,基于永磁同步电机的特征模型,设计一个以非线性黄金分割自适应控制为主的控制方案。通过安排过渡过程和特征模型参数的在线辨识实现了该控制方案下控制器参数的在线自适应调节。实验结果表明该控制方案不仅能够实现永磁同步电机较高精确度的转速控制,与一阶自抗扰控制、增广状态反馈控制和传统的PI控制相比,具有更加优越的暂态和稳态性能。     

水轮机调速系统的非线性自适应控制

水门开度控制不仅对电力系统暂态稳定性的改善有极其重要的作用,而且对抑制电力系统低频振荡、改善动态品质也有不可低估的作用。国内外水轮机调速器的控制器设计通常是基于具有准确参数的理想水轮机模型。论文考虑了水轮发电机固有的非线性以及水轮机参数的不确定性,基于微分几何理论和自适应控制方法推导了水轮机调速的非线性自适应控制律(NAC)。数字仿真试验表明,所设计的调速器控制律具有较强的参数自适应能力,在改善系统动态特性和提高系统大干扰稳定性方面优于未考虑参数不确定的非线性控制律(CNGC)。     

An indirect model reference adaptive controller based on the multirate sampling of the plant output

The use of sampled-data multirate-output controllers for model reference adaptive control of possibly non-stably invertible linear systems with unknown parameters is investigated. Multirate-output controllers contain a multirate sampling mechanism with different sampling period at each system output. Such a control allows us to assign an arbitrary discrete-time transfer function matrix for the sampled closed-loop system and does not make assumptions on the plant other than controllability, observability and the knowledge of two sets of structural indices, namely the controllability and the observability indices. An indirect adaptive control scheme based on these sampled-data controllers is proposed which estimates the unknown plant parameters (and consequently the controller parameters) on-line from sequential data of the inputs and the outputs of the plant, which are recursively updated within the time limit imposed by a fundamental sampling period T₀. Using the proposed adaptive algorithm, the model reference adaptive control problem is reduced to the determination of a fictitious static state feedback controller owing to the merits of multirate-output controllers. Known indirect model reference adaptive control techniques usually resort to the direct computation of dynamic controllers. The controller determination reduces to the simple problem of solving a linear algebraic system of equations, whereas in known indirect model reference adaptive control techniques, matrix polynomial Diophantine equations usually need to be solved. Moreover, persistent excitation of the continuous-time plant is provided without making any special richness assumption on the reference signals.     

Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

This paper provides a modified model reference adaptive control (MRAC) scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced. We first revisit the standard MRAC system and analyze the tracking error bound by using L₂‐norm and Cauchy‐Schwartz inequality. Based on this analysis, we suggest a feasible way to compensate the undesired transient dynamics induced by the gradient descent–based adaptive laws subject to sluggish convergence or even parameter drift. Then, a modified adaptive law with an alternative leakage term containing the parameter estimation error is developed. With this adaptive law, the convergence of both the estimation error and tracking error can be proved simultaneously. This enhanced convergence property can contribute to deriving smoother control signal and improved control response. Moreover, this paper provides a simple and numerically feasible approach to online verify the well‐known persistent excitation condition by testing the positive definiteness of an introduced auxiliary matrix. Comparative simulations based on a benchmark 3‐DOF helicopter model are given to validate the effectiveness of the proposed MRAC approach and show the improved performance over several other MRAC schemes.     

An adaptive feedback linearized model predictive controller design for a nonlinear multi-input multi-output system

In this work, an adaptive feedback linearized model predictive control (AFLMPC) scheme is proposed to compensate system uncertainty for a class of nonlinear multi-input multi-output system. Initially, a feedback linearization technique is used to transform the nonlinear dynamics into an exact linear model, thereafter, a model predictive control scheme is designed to obtain the desired tracking performance. A suitable constraint mapping algorithm has been developed to map input constraints to the new virtual input of the proposed control scheme. The proposed control scheme utilizes multiple estimation model and the concept of second-level adaptation technique Pandey et al. (2014) to handle the parametric uncertainty in real-time. Hence, the adaptive term in the control scheme is used to counteract the effect of model uncertainties and parameter adaptation errors. The effectiveness of the proposed AFLMPC control algorithm has been verified successfully in simulation as well as the experimental setup of the TRMS model. The unavailable states of the nonlinear system have been estimated using an extended Kalman filter based state observer. The performance of the proposed control algorithm has been compared with other existing nonlinear control techniques in simulation and experimental validation.     

Adaptive control in a heating vessel: A comparative study

This paper presents experimental results obtained in temperature adaptive control of a heating vessel. A non-linear model of the process showed that the feed flow rate causes severe variations in time constant and process gain. An extended horizon self-tuning controller and two different model reference adaptive controllers were tested. The influence of design parameters on control performance for each algorithm was assessed. The control system was disturbed with load and setpoint changes.     

Dual high‐gain‐based adaptive output‐feedback control for a class of nonlinear systems

We propose an adaptive output‐feedback controller for a general class of nonlinear triangular (strict‐feedback‐like) systems. The design is based on our recent results on a new high‐gain control design approach utilizing a dual high‐gain observer and controller architecture with a dynamic scaling. The technique provides strong robustness properties and allows the system class to contain unknown functions dependent on all states and involving unknown parameters (with no magnitude bounds required). Unlike our earlier result on this problem where a time‐varying design of the high‐gain scaling parameter was utilized, the technique proposed here achieves an autonomous dynamic controller by introducing a novel design of the observer, the scaling parameter, and the adaptation parameter. This provides a time‐invariant dynamic output‐feedback globally asymptotically stabilizing solution for the benchmark open problem proposed in our earlier work with no magnitude bounds or sign information on the unknown parameter being necessary. Copyright © 2007 John Wiley & Sons, Ltd.     

带恒功率负载的Boost变换器模糊自适应反步滑模控制

恒功率负载的负阻抗特性易导致DC/DC变换器系统输出电压不稳定。该文针对带恒功率负载的Boost变换器,提出一种模糊自适应反步滑模控制策略。首先应用精确反馈线性化将模型转化为布鲁诺夫斯基标准形式。然后在保证大信号稳定的前提下,将模糊自适应控制方法加入到反步滑模控制器的设计中,根据模糊自适应控制系统实时更新系统增益,利用李雅普诺夫理论证明整个闭环系统的稳定性。最后,仿真和实验结果表明,与传统的双闭环PI控制方法相比,该控制策略具有更好的动态调节性能和更强的鲁棒性。