Adaptive control with optimal tracking performance

This paper provides a way to optimise the steady-state tracking performance of the adaptive control system in the presence of unknown external disturbances. A-priori knowledge of the dynamic model of the reference signal to be tracked is not completely known. Especially, the updatable non-empty admissible model set, which is consistent to the a-priori knowledge of the plant parameter and the online measurements, is computed. Treating the overall system performance as the criteria, the nominal model is optimally chosen within the admissible model set. The optimal nominal model is subsequently used to synthesise the optimal closed-loop controller that minimises the steady-state absolute value of the tracking error. Combining the above two aspects, an optimal adaptive control scheme is proposed. Because of the consistency of the identification criteria and control object, the adaptive control scheme proposed in this paper can achieve the overall optimal steady-state tracking performance, and the effect of the interplay between the identification and control of the adaptive system can be handled effectively. In addition, the computable optimal performance is also provided.     

不确定性系统的自适应鲁棒跟踪控制

针对存在未知干扰和未建模动态等不确定性的系统的自适应鲁棒跟踪控制问题进行了 探讨.首选将l1优化控制器的有限拍设计方法结合给出了最优鲁棒稳态跟踪控制器的设计方法． 然后利用集员辨识的思想,将名义模型的参数和未建模动态及干扰的大小作为未知参数,提出了 一种递推参数估计方法.最后将上述研究结果结合起来提出了一种自适应鲁棒跟踪控制策略,证 明了自适应算法的全局收敛性并给出了鲁棒跟踪性能指标的一下较紧的上界.与现有的结果相 比,本文提出的自适应控制具有非保守的鲁棒稳定性,具有渐近最优的鲁棒跟踪性能.     

基于RBF辨识的神经元PID直线伺服控制

针对永磁直线同步电动机(PMLSM)直接驱动的伺服系统,提出了一种基于RBF神经网络辨识的单神经元PID模型参考自适应的优化跟踪控制策略,解决了系统快速精确地跟踪与抗扰性能之间的矛盾.利用RBF神经网络作为辨识器,实现对被控对象Jacobian信息精确辨识,以基于二次型性能指标学习算法的单神经元自适应PID作为控制器,从而保证系统具有较强的鲁棒性能.仿真结果表明,该方案在保证伺服系统快速跟踪性能的同时,对系统参数变化和负载扰动具有很强的鲁棒性.     

An adaptive disturbance rejection control scheme for multivariable nonlinear systems

An adaptive disturbance rejection control scheme is developed for uncertain multi-input multi-output nonlinear systems in the presence of unmatched input disturbances. The nominal output rejection scheme is first developed, for which the relative degree characterisation of the control and disturbance system models from multivariable nonlinear systems is specified as a key design condition for this disturbance output rejection design. The adaptive disturbance rejection control design is then completed by deriving an error model in terms of parameter errors and tracking error, and constructing adaptive parameter-updated laws and adaptive parameter projection algorithms. All closed-loop signals are guaranteed to be bounded and the plant output tracks a given reference output asymptotically despite the uncertainties of system and disturbance parameters. The developed adaptive disturbance rejection scheme is applied to turbulence compensation for aircraft fight control. Simulation results from a benchmark aircraft model verify the desired system performance.     

基于定量反馈理论的时滞系统自抗扰控制参数整定

工业过程对象普遍存在时滞、模型参数不确定性和外部扰动多等特点,传统Smith预估控制方法难以设计出满足期望性能的鲁棒控制器.针对模型参数不确定性和外部扰动,本文采用自抗扰控制技术进行估计和补偿.针对系统存在时滞的特点,本文提出改进Smith预估器结构,提升扩张状态观测器对于扰动估计的实时性.在此基础上,本文以一阶时滞系统为例提出了控制器参数整定方法.首先根据最优参数选取准则确定预估器模型,然后在等效模型框架下采用定量反馈理论整定自抗扰控制器参数,确保控制系统达到预期性能指标.在仿真实验中,将所提出方法与几种常见时滞系统控制方法进行比较,通过设定值跟踪、抗扰及蒙特卡罗实验验证了所提出方法具有良好抗扰能力与鲁棒性.     

Performance analysis and improvement in model reference adaptivecontrol

The purpose of this paper is to address the issue of performance by using two additional criteria to assess performance in the ideal and nonideal situations. They are the mean square tracking error criterion and the L_∞ tracking error bound criterion. We use these criteria to examine the performance of a standard model reference adaptive controller and motivate the design of a modified scheme that can have an arbitrarily improved nominal performance in the ideal case and in the presence of bounded input disturbances. It is shown that for these cases the modified scheme can provide an arbitrarily improved zero-state transient performance and an arbitrary reduction in the size of possible bursts that may occur at steady state. As in every robust control design, the nominal performance has to be traded off with robust stability and therefore the improvement in performance achieved by the proposed scheme is limited by the size of the unmodeled dynamics, as established in the paper     

Optimal disturbance rejection control approach based on a compound neural network prediction method

A new optimal disturbance rejection control method is proposed for the system with disturbances via a compound neural network prediction approach in this paper. The disturbances caused by external disturbances and model mismatches can be estimated by a disturbance observer, and the estimation of disturbances is introduced into the neural network predictive model to make the predictive output more accurate. Then based on the new compound neural network predictive model, a controller, which ensures both optimal performance by the receding horizon optimization and strong disturbance rejection ability, is obtained. The proposed scheme is applied to control the temperature of a simplified jacketed stirred tank heater (JSTH). Simulation results demonstrate the effectiveness of the proposed control method.     

Observer-based robust optimal control for helicopter with uncertainties and disturbances

Unknown model uncertainties and external disturbances widely exist in helicopter dynamics and bring adverse effects on control performance. Optimal control techniques have been extensively studied for helicopters, but these methods cannot effectively handle flight control problems since they are sensitive to uncertainties and disturbances. This paper proposes an observer-based robust optimal control scheme that enables a helicopter to fly optimally and reduce the influence of unknown model uncertainties and external disturbances. A control Lyapunov function (CLF) is firstly constructed using the backstepping method, then Sontag's formula is utilized to design an inverse optimal controller to stabilize the nominal system. Furthermore, it is stressed that the radial basis function (RBF) neural network is introduced to establish an observer with adaptive laws, approximating and compensating for the unknown model uncertainties and external disturbances to enhance the robustness of the closed-loop system. The uniform ultimate boundedness of the closed-loop system is ensured using the presented control approach via Lyapunov stability analysis. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control strategy.     

基于改进卡尔曼滤波器的扰动抑制无模型自适应控制方案

针对无模型自适应控制方法在测量扰动作用下控制效果不佳的问题, 本文提出了一种新的扰动抑制无模 型自适应控制方案. 首先基于受控系统的动态线性化数据模型及测量扰动的统计特性, 在最小方差估计准则下推导 了基于系统输入输出数据的改进卡尔曼滤波器. 然后基于此滤波器给出了一种新的扰动抑制无模型自适应控制方 案. 该方案仅需用到受控系统的输入输出数据, 即可实现在强测量扰动作用下系统的无模型自适应控制. 仿真结果 显示, 相比现有的扰动抑制无模型自适应控制方案, 该方案在系统跟踪常值参考信号、时变参考信号时均能有效地 抑制测量扰动, 适用性更好的同时可以获得更小的跟踪误差及更大的数据信噪比.     

Non-linear adaptive sliding mode switching control with average dwell-time

In this article, an adaptive integral sliding mode control scheme is addressed for switched non-linear systems in the presence of model uncertainties and external disturbances. The control law includes two parts: a slide mode controller for the reduced model of the plant and a compensation controller to deal with the non-linear systems with parameter uncertainties. The adaptive updated laws have been derived from the switched multiple Lyapunov function method, also an admissible switching signal with average dwell-time technique is given. The simplicity of the proposed control scheme facilitates its implementation and the overall control scheme guarantees the global asymptotic stability in the Lyapunov sense such that the sliding surface of the control system is well reached. Simulation results are presented to demonstrate the effectiveness and the feasibility of the proposed approach.     

Dynamic Balance Control Based on an Adaptive Gain-scheduled Backstepping Scheme for Power-line Inspection Robots

This paper presents an adaptive gain-scheduled backstepping control (AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection (PLI) robotic system with two degrees of freedom and a single control input. First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points, an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control (GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.      

A multi-iteration pseudo-linear regression method and an adaptive disturbance model for MPC

This paper proposes an MPC method that uses an adaptive disturbance model to improve the accuracy of prediction. In unmeasured disturbance model identification, a novel multi-iteration pseudo-linear regression (MIPLR) method is used which is more accurate and has faster convergence than traditional recursive identification methods. The adaptive disturbance model is used in an MPC scheme for improved performance in disturbance rejection. The method is demonstrated by the simulation of a distillation column and also tested on the real process. The test results show that the proposed MPC scheme can not only increase control performance, but also increase robustness.     

运行控制系统集中式容错控制方法

本文针对运行控制系统,建立了运行优化控制过程的双层结构模型.在此基础上,通过建立相应的自适应故障诊断算法,提出了保证在系统有故障和干扰时仍能渐近优化指标的集中式容错控制方法,利用李雅普诺夫稳定性理论分析了自适应故障诊断算法的构建.已证明:该方法通过调整已优化的设定值来保证在回路控制层出现故障时整个运行控制仍可收敛到其原有的优化控制效果.该方法属于非完备容错控制,仿真结果验证了所提方法的有效性.     

Robust adaptive neural sliding mode control of MEMS triaxial gyroscope with angular velocity estimation

In this paper, a robust adaptive sliding mode control strategy of micro electro-mechanical system (MEMS) triaxial gyroscope using radial basis function (RBF) neural network is presented for the system identification of MEMS gyroscope. A key property of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. An adaptive RBF neural network controller is used to learn the unknown upper bound of model uncertainties and external disturbances. The adaptive RBF neural network is incorporated into the adaptive sliding mode control in the Lyapunov sense, and the stability of the proposed adaptive neural sliding mode control can be established. The dynamics and angular velocities of gyroscope can be identified in real time. Numerical simulations are investigated to verify the effectiveness of the proposed adaptive neural sliding mode control scheme, showing that the designed control system has better robust performance in its insensitivity to system nonlinearities; moreover, system parameters including angular velocity can be consistently estimated and tracking errors converge to zero asymptotically.     

An adaptive control strategy for mechanical manipulators

This paper focuses on the study of an adaptive perturbation control which tracks a desired time-based trajectory as close as possible for all times over a wide range of manipulator motion and payloads. The proposed adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component computes the nominal torques from the Newton-Euler equations of motion to compensate all the interaction forces among the various joints. The feedback component consisting of recursive least-square identification and an optimal adaptive self-tuning control algorithm for the linearized system computes the perturbation torques which reduce the position and velocity errors of the manipulator along the nominal trajectory. A computer simulation study was conducted to evaluate the performance of the proposed adaptive control.     

Composite predictive flight control for airbreathing hypersonic vehicles

The robust optimised tracking control problem for a generic airbreathing hypersonic vehicle (AHV) subject to nonvanishing mismatched disturbances/uncertainties is investigated in this paper. A baseline nonlinear model predictive control (MPC) method is firstly introduced for optimised tracking control of the nominal dynamics. A nonlinear-disturbance-observer-based control law is then developed for robustness enhancement in the presence of both external disturbances and uncertainties. Compared with the existing robust tracking control methods for AHVs, the proposed composite nonlinear MPC method obtains not only promising robustness and disturbance rejection performance but also optimised nominal tracking control performance. The merits of the proposed method are validated by implementing simulation studies on the AHV system.     

Neural networks stabilization and disturbance attenuation for nonlinear switched impulsive systems

In this paper, we address the problem of neural networks (NNs) stabilization and disturbance rejection for a class of nonlinear switched impulsive systems. An adaptive NN feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy based on NN. The NN is used to compensate for the nonlinear uncertainties of switched impulsive systems, and the approximation error of NN is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Impulsive controller is designed to attenuate effect of switching impulse. Under all admissible switching law, impulsive controller and adaptive NN feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall nonlinear switched impulsive system. Finally, a numerical example is given to demonstrate the effectiveness of the proposed control and stabilization methods.     

Robust Output Feedback Model Predictive Control: A Stochastic Approach

This paper addresses the robust explicit model predictive control scheme for linear systems with input and output constraint in the presence of disturbances and noise. Conditions for disturbance rejection are established by incorporating a full state/disturbance observer. The separation principle is applied to design an optimal observer in the unconstrained problem. Then, an efficient algorithm is developed to explicitly design observer gains by minimizing a quadratic performance criterion. It is shown that the solution includes a set of regions with piecewise affine functions of the state and reference vectors and a set of regions with optimal observers. In the proposed method, two sets of partitions associated with the control law and the observer gains are obtained. Therefore, the online computation includes finding the active regions of both observer and control law partitions in which the current state is located. The proposed technique is particularly attractive for a wide range of practical problems where the exact model of the actual system is not available.     

多电机驱动系统的保性能鲁棒Funnel控制

本文针对含参数不确定性的多电机驱动系统,提出一种基于最优保性能鲁棒的Funnel控制方法实现系统的规定跟踪性能.该控制方法通过构造Funnel函数对误差系统进行变换,并设计自适应反步控制器保证变换后系统的稳定性即可使跟踪误差的瞬态和稳态响应均被限制在给定的Funnel边界内.然而由于系统中存在的参数不确定性会影响系统的规定控制性能,本文在Funnel控制基础上又设计了最优保性能鲁棒控制器.它是通过将参数不确定性系统的保性能鲁棒控制问题转化为标称系统的最优控制问题,并求解新的黎卡提方程而得到的.因此所设计的控制器不但消除了参数不确定性对系统的影响并且能够使系统的性能指标达到一确定的上界.最后,对四电机驱动系统进行了仿真和实验验证,说明所提出控制方法的有效性.     

Adaptive Fuzzy Output Regulation for Unmanned Surface Vehicles with Prescribed Performance

Dynamics of an unmanned surface vehicle (USV) is usually hard to be modeled accurately due to system uncertainties and disturbances, which can significantly reduce system control performance. To guarantee a satisfied control performance under modeling uncertainties and disturbances, a novel control scheme combining adaptive fuzzy output regulation control and prescribed performance control is proposed in this paper. The unknown nonlinear dynamics of the USV is firstly approximated by a fuzzy logic system, and then an adaptive output regulation control law is developed using backstepping approach for the USV to track a reference system while rejecting disturbances and approximation errors induced by the fuzzy logic system. Meanwhile, the prescribed performance control technique is combined to the adaptive output regulation control design to reach a desired control performance in spite of the unknown system dynamics and disturbances. A simulation study is finally provided to demonstrate the effectiveness of the proposed approach.