On parameter convergence in adaptive control

It is well known that the parameter error as well as the model-plant mismatch error in a model reference adaptive scheme tends exponentially to zero iff a certain sufficient richness condition holds for signals inside the time-varying plant control loop. In this paper we give conditions on the reference signal (the exogenous input to the adaptive loop) — namely, that it have as many spectral lines as there are unknown parameters, in order to guarantee parameter convergence.     

同参数估计对偶的自适应控制算法

本文把线性和非线性系统统一处理。从自适应控制算法与参数估计算法的对偶性出发,提出了自适应控制算法的一种统一格式。这种格式算法简单,并在一定的条件下,能使控制误差一致的足够小。     

Pattern recognition: An alternative to parameter identification in adaptive control

Practical models used in identification of process control processes must be too simplistic to give precise control information. However, these models can be used for adaptation if they are continuously readapted. But the identification then lacks the precision which might justify the analytic elaboration. One alternative has been to use pattern recognition as a means for allowing a computer system to characterize transient response computing readapted parameters which cause the control behavior to approach a desired transient ‘shape’. The paper summarizes work using pattern features as a basis for practice and theory.     

Some remarks on adaptive stabilization of infinite-dimensional systems

It is the purpose of this note to show that a first-order adaptive controller stabilizes a large class of infinite-dimensional systems described by strongly continous semigroups. It is assumed that the plant is minimum-phase and has invertible high-frequency gain. Knowledge of the sign of the high-frequency gain is not required.     

A novel robust adaptive control algorithm with finite-time online parameter estimation of a humanoid robot arm

A novel robust adaptive control algorithm is proposed and implemented in real-time on two degrees-of-freedom (DOF) of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) arm in joint-space. In addition to having a significant robustness property for the tracking, the algorithm also features a sliding-mode term based adaptive law that captures directly the parameter estimation error. An auxiliary filtered regression vector and filtered computed torque is introduced. This allows the definition of another auxiliary matrix, a filtered regression matrix, which facilitates the introduction of a sliding mode term into the adaptation law. Parameter error convergence to zero can be guaranteed within finite-time with a Persistent-Excitation (PE) condition or Sufficient Richness condition for the demand. The proposed scheme also exhibits robustness both in the tracking and parameter estimation errors to any bounded additive disturbance. This theoretical result is then exemplified for the BERT II robot arm in simulation and for experiments.     

Integrated direct/indirect adaptive robust contouring control of a biaxial gantry with accurate parameter estimations

This paper presents an integrated direct/indirect adaptive robust contouring controller (DIARC) for an industrial biaxial high-speed gantry that achieves not only excellent contouring performance but also accurate parameter estimations for secondary purposes such as machine health monitoring and prognosis. Contouring control problem is first formulated in a task coordinate frame. A physical model-based indirect-type parameter estimation algorithm is then developed to obtain accurate on-line estimates of unknown model parameters. A DIARC controller possessing dynamic-compensation-like fast adaptation is subsequently constructed to preserve the excellent transient and steady-state contouring performance of the direct adaptive robust controller (DARC) designs. The proposed DIARC along with previously developed DARC contouring controllers are implemented on a high-speed industrial biaxial gantry to test their achievable performance in practice. Comparative experimental results verify the improved contouring performance and the accurate physical parameter estimates of the proposed DIARC algorithm.     

Tracking and parameter identification for model reference adaptive control

We provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.     

具有状态参数约束的鲁棒自适应最优控制设计

在传统Backstepping 鲁棒自适应设计中, 虚拟控制函数的设计参数缺乏约束, 对此, 采用新的设计方法得到了新坐标系下的系统模型和参数替换律, 结合直接反馈线性化和最优控制策略, 最终给出了具有参数约束的非线性鲁棒自适应最优控制的设计方法. 以具体励磁系统参数为例进行仿真实验研究, 所得结果表明, 该鲁棒自适应控制方法可以实现状态参数的最优约束, 并能有效控制状态参数的收敛速度.     

基于不确定逼近的机械手间接自适应鲁棒预测控制

为了使机械手系统在含有模型不确定项时具有良好的跟踪性能和较强的抗干扰能力,提出了一种间接自适应鲁棒预测控制。首先,针对机械手模型设计出非线性鲁棒预测控制器;然后,基于三次样条函数逼近控制律中因模型不确定性产生的未知项,并在控制律中引入一个D-控制项抑制外部干扰。理论证明了所设计的控制器能够使跟踪误差收敛到原点。仿真验证了所提方法的有效性。     

Persistency of excitation, sufficient richness and parameter convergence in discrete time adaptive control

The main result of this paper is to give new techniques for deriving explicit conditions on the exogenously specified reference trajectory to guarantee parameter convergence for a class of either single-input single-output or multi-input multi-output discrete time adaptive control schemes. Other interesting results are relations between persistently exciting signals and sufficiently rich signals.     

A novel adaptive backstepping design of turbine main steam valve control

The problem of transient stability for a single machine infinite bus system with turbine main steam valve control is addressed by means of a novel adaptive backstepping method in this paper. The recursive design procedure of the proposed controller is much simpler than that of the existing controller based on conventional adaptive backstepping method. In the system, the damping coefficient is measured inaccurately, and the reactance of transmission line also contains a few uncertainties. A nonlinear robust controller and parameter updating laws are obtained simultaneously. The system does not need to be linearized, and the closed-loop error system is guaranteed to be asymptotically stable. The design procedure and simulation results demonstrate the effectiveness of the proposed design.     

Closed loop parameter identifiability and adaptive control of a linear stochastic system

We show that if the parameters of a linear stochastic control system are identifiable using a persistently existing input then the same systems remains parameter identifiable if the system operates under closed loop in a certain way. We assume that the controller itself depends on the test value of the system parameters, and as usual the control signal is dithered. The estimation problem is formulated as the problem of minimizing an appropriate asymptotic cost function. It is shown that a suitable modification of the gradient of the cost function converts our problem into another problem for which Ljung's scheme can be applied. Thus the theorem provides a general method for the solution of the local adaptive control problem.     

Some comments on the use of normalization in robust adaptive control

This paper discusses the role of normalization with respect to robust parameter estimation for discrete-time adaptive control in the presence of unmodeled dynamics. It is pointed out that the normalizing signal may be brought into the adaptive law in two distinct ways; (i) via normalization of the regressor and the error signal in the standard parameter update law, and (ii) by replacement of the error signal with a function of the normalizing signal and the error. The convergence properties for both approaches are derived and shown to be similar.     

Multivariable adaptive dynamic surface control based on norm estimation of unknown parameter matrices

In this article, a novel output-feedback adaptive dynamic surface control scheme is proposed for linear time-invariant multivariable plants based on the norm estimation of unknown parameter matrices. Besides avoiding the explosion of complexity problem in traditional multivariable backstepping design, the proposed scheme has the following features: (1) only one parameter needs to be updated on-line regardless of the plant order and input–output dimension, (2) only the Hurwitz condition is required for the high-frequency gain matrix and (3) the ?_∞ performance of the tracking error can be guaranteed. It is shown that all signals of the closed-loop system are semi-globally uniformly bounded. Simulation results are given to illustrate the effectiveness of the proposed scheme.     

基于参数空间寻优的超低空空投L1自适应控制器参数优化

针对基于L_1自适应控制的超低空空投纵向控制器,利用基于参数空间寻优(parameter space investigation,PSI)的多准则优化方法,以空投任务性能等级为指标,对L_1自适应控制器中状态估计器短周期阻尼比、自然频率以及低通滤波器带宽这3个参数进行优化,通过两次迭代,最终求得合适的Pareto最优解以完成参数优化的过程.仿真验证了参数寻优过程的有效性和适用性,证明了经参数优化后,超低空空投任务性能等级由"适度"改善至"期望", L_1控制器的动态性能和鲁棒性能提升显著,可有效保证运输机的安全性和空投任务的顺利完成.     

On parameter convergence in least squares identification and adaptive control

Least squares estimation is appealing in performance and robustness improvements of adaptive control. A strict condition termed persistent excitation (PE) needs to be satisfied to achieve parameter convergence in least squares estimation. This paper proposes a least squares identification and adaptive control strategy to achieve parameter convergence without the PE condition. A modified modeling error that utilizes online historical data together with instant data is constructed as additional feedback to update parameter estimates, and an integral transformation is introduced to avoid the time derivation of plant states in the modified modeling error. On the basis of these results, a regressor filtering–free least squares estimation law is proposed to guarantee exponential parameter convergence by an interval excitation condition, which is much weaker than the PE condition. And then, an identification‐based indirect adaptive control law is proposed to establish exponential stability of the closed‐loop system under the interval excitation condition. Illustrative results considering both identification and control problems have verified the effectiveness and superiority of the proposed approach.     

Direct adaptive control using an adaptive reference model

Direct model reference adaptive control is considered when the plant-model matching conditions are violated due to large changes in the plant or incorrect knowledge of the plant's mathematical structure. Because of the mismatch, the plant can no longer track the original reference model, but may be able to track a modified reference model that still provides satisfactory performance. The proposed approach uses a time-varying ‘adaptive’ reference model that reflects the achievable performance of the changed plant. The approach consists of direct adaptation of state feedback gains for state tracking and simultaneous estimation of the plant-model mismatch. The reference model adapts to the changed plant, and is redesigned if the estimated plant-model mismatch exceeds a bound determined via robust stability and/or performance criteria. The resulting controller offers asymptotic state tracking in the presence of plant-model mismatch as well as matched parameter deviations.     

Nonlinear averaging theorems, and the determination of parameter convergence rates in adaptive control

The paper presents nonlinear averaging theorems for two-time scale systems, where the dynamics of the fast system are allowed to vary with the slow system. The results are applied to the Narendra-Valavani adaptive control algorithm, and estimates of the parameter convergence rates are obtained which do not rely on a linearization of the system around the equilibrium, and therefore are valid in a larger region in the parameter space.