Simplified parameter adaptive control

A simple parameter adaptive controller design methodology is introduced in which steady-state servo tracking properties provide the major control objective. This is achieved without cancellation of process zeros and hence the underlying design can be applied to non-minimum phase systems. As with other self-tuning algorithms, the design (user specified) polynomials of the proposed algorithm define the performance capabilities of the resulting controller. However, with the appropriate definition of these polynomials, the synthesis technique can be shown to admit different adaptive control strategies, e.g. self-tuning PID and self-tuning pole-placement controllers. The algorithm can therefore be thought of as an embodiment of other self-tuning design techniques. The performances of some of the resulting controllers are illustrated using simulation examples and the on-line application to an experimental apparatus.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Necessary and sufficient condition for generalized passivity, passification and application to multivariable adaptive systems

Recently, a generalized passivity concept for linear multivariable systems was obtained which allows circumventing the restrictiveness of the usual passivity concept. The latter is associated with the classical SPR (Strictly Positive Real) condition whereas the new concept of passivity is associated with the so called WSPR condition and its advantage in multivariable systems is that it does not require a restrictive symmetry condition of SPR systems. As a result, it allows the design of multivariable adaptive control that, unlike some existing factorization approaches, does not imply in additional overparameterization of the adaptive controller. In this paper, we complete a previously established WSPR sufficient condition and prove that it is also necessary. We also propose some methods of passification by either premultiplying the system output tracking error vector or the system input vector by an adequate passifying matrix multiplier, so that the resulting input/output transfer function becomes WSPR. The efficiency of our proposals are illustrated by simulation utilizing a well known robotics adaptive visual servoing problem. © 2011 IFAC.

The application of H controller synthesis to high speed independent drive systems

This thesis describes work completed on the application of H controller synthesis to the design of controllers for single axis high speed independent drive design examples. H controller synthesis was used in a single controller format and in a self-tuning regulator, a type of adaptive controller. Three types of industrial design examples were attempted using H controller synthesis, both in simulation and on a Drives Test Facility at Aston University. The results were benchmarked against a Proportional, Integral and Derivative (PID) with velocity feedforward controller (VFF), the industrial standard for this application. An analysis of the differences between a H and PID with VFF controller was completed. A direct-form H controller was determined for a limited class of weighting function and plants which shows the relationship between the weighting function, nominal plant and the controller parameters. The direct-form controller was utilised in two ways. Firstly it allowed the production of simple guidelines for the industrial design of H controllers. Secondly it was used as the controller modifier in a self-tuning regulator (STR). The STR had a controller modification time (including nominal model parameter estimation) of 8ms. A Set-Point Gain Scheduling (SPGS) controller was developed and applied to an industrial design example. The applicability of each control strategy, PID with VFF, H, SPGS and STR, was investigated and a set of general guidelines for their use was determined. All controllers developed were implemented using standard industrial equipment.

A probabilistic indirect adaptive control for systems with input-dependent noise

A probabilistic indirect adaptive controller is proposed for the general nonlinear multivariate class of discrete time system. The proposed probabilistic framework incorporates input–dependent noise prediction parameters in the derivation of the optimal control law. Moreover, because noise can be nonstationary in practice, the proposed adaptive control algorithm provides an elegant method for estimating and tracking the noise. For illustration purposes, the developed method is applied to the affine class of nonlinear multivariate discrete time systems and the desired result is obtained: the optimal control law is determined by solving a cubic equation and the distribution of the tracking error is shown to be Gaussian with zero mean. The efficiency of the proposed scheme is demonstrated numerically through the simulation of an affine nonlinear system.

Alternative Formulation of the Discrete Linear Quadratic Regulator (DLQR)

In this paper, an alternative apriori and aposteriori formulation has been derived for the discrete linear quadratic regulator (DLQR) in a manner analogous to that used in the discrete Kalman filter. It has been shown that the formulation seamlessly fits into the available formulation of the DLQR and the equivalent terms in the existing formulation and the proposed formulation have been identified. Thereafter, the significance of this alternative formulation has been interpreted in terms of the sensitivity of the controller performances to any changes in the states or to changes in the control inputs. The implications of this alternative formulation to adaptive controller tuning have also been discussed.

How are internal models of unstable tasks formed?

The results of recent studies suggest that humans can form internal models that they use in a feedforward manner to compensate for both stable and unstable dynamics. To examine how internal models are formed, we performed adaptation experiments in novel dynamics, and measured the endpoint force, trajectory and EMG during learning. Analysis of reflex feedback and change of feedforward commands between consecutive trials suggested a unified model of motor learning, which can coherently unify the learning processes observed in stable and unstable dynamics and reproduce available data on motor learning. To our knowledge, this algorithm, based on the concurrent minimization of (reflex) feedback and muscle activation, is also the first nonlinear adaptive controller able to stabilize unstable dynamics.

基于模糊神经网络水下机器人直接自适应控制

提出了基于广义动态模糊神经网络的水下机器人直接自适戍控制方法,该控制方法既不需要预先知道模糊神经结构,也不需要预先的训练阶段,完全通过在线自适应学习算法构建水下机器人的逆动力学模型.首先,本文提出了基于这种网络结构的水下机器人直接自适应控制器,然后,利用Lyapunov稳定理论,证明了基于该控制器的水下机器人控制系统闭环稳定性,最后,采用某水下机器人模型仿真验证了该控制方法的有效性。

深海机器人推进电机系统的混沌控制

深海机器人推进电机系统中出现的混沌现象,直接影响深海机器人稳定性、可靠性和安全性.采用自适应控制技术对其混沌行为加以控制,对该方法的可行性和有效性进行了证明.设计和构造了易于工程实施的混沌控制器,用于深海机器人推进电机系统混沌控制.仿真实验表明,推进电机系统在自适应控制器的作用下可迅速脱离混沌状态,并进入持续稳定状态,控制效果明显.可以为深海机器人推进电机系统中可能出现的混沌运行行为提供控制策略和抑制预案,有利于混沌控制嵌入软件的开发,确保深海机器人稳定、可靠和安全地运行,具有一定的实用价值.

柔性机械臂运动轨迹的鲁棒自适应控制

本文针对多连杆柔性机械臂的运动轨迹控制问题，讨论了动力学建模、控制系统结构设计以及鲁棒自适应控制算法，运用假设模态方法得到了柔性机械臂动力学近似方程，通过对柔性机械臂动力学特性分析，建立了等价动力学模型，依此提出了一种鲁棒自适应控制算法，并给出了仿真研究结果。

机器人多级自适应控制器鲁棒性分析

本文给出了机器人关节系统多级自适应控制器的鲁棒性分析.分析表明机器人多级自适应控制系统即使子系统间存在相互关联项和子系统存在未建模特性.采用σ-修正法也能保证系统的输出有界.

工业机器人机械手的小脑模型自适应控制

本文为动力学控制工业机器人机械手提出一种综合控制算法。该控制算法,利用小脑模型算术计算机模块模拟机器人机械手的动力学方程并计算实现期望运动所需力矩作为前馈力矩控制项;利用自适应控制器实现反馈控制,以消除由输入扰动和参数变化而引起的机器人机械手运动误差。这种控制方法在时间上是有效的,且很适合于定点实现。控制方法的有效性通过四自由度的直接驱动机器人前两个关节的计算机仿真实验得到验证。

机器人多级自适应控制器的设计与数字仿真

本文提出了用多级自适应控制的方法实现机器人关节系统的自适应控制,并给出了两级自适应控制器的设计方法.最后以某机器人关节系统为对象对两级自适应控制器进行了仿真分析.

Modelos e estratégias de controlo ambientais para estufas agrícolas

For a greenhouse with a double polyethylene cover, it will be presented a dynamic climate transfer function and an adaptive controller for the air temperature. The model employ data acquired from the outside weather and from the heating and cooling inputs.

Projecto de Controladores usando Redes Neuronais

Neste documento descreve-se o projeto desenvolvido na unidade curricular de Tese e Dissertação durante o 2º ano do Mestrado de Engenharia Eletrotécnica e de Computadores no ramo de Automação e Sistemas, no Departamento de Engenharia Eletrotécnica (DEE) do Instituto Superior de Engenharia do Porto (ISEP). O projeto escolhido teve como base o uso da tecnologia das redes neuronais para implementação em sistemas de controlo. Foi necessário primeiro realizar um estudo desta tecnologia, perceber como esta surgiu e como é estruturada. Por último, abordar alguns casos de estudo onde as redes neuronais foram aplicadas com sucesso. Relativamente à implementação, foram consideradas diferentes estruturas de controlo, e entre estas escolhidas a do sistema de controlo estabilizador e sistema de referência adaptativo. No entanto, como o objetivo deste trabalho é o estudo de desempenho quando aplicadas as redes neuronais, não se utilizam apenas estas como controlador. A análise exposta neste trabalho trata de perceber em que medida é que a introdução das redes neuronais melhora o controlo de um processo. Assim sendo, os sistemas de controlo utilizados devem conter pelo menos uma rede neuronal e um controlador PID. Os testes de desempenho são aplicados no controlo de um motor DC, sendo realizados através do recurso ao software MATLAB. As simulações efetuadas têm diferentes configurações de modo a tirar conclusões o mais gerais possível. Assim, os sistemas de controlo são simulados para dois tipos de entrada diferentes, e com ou sem a adição de ruído no sensor. Por fim, é efetuada uma análise das respostas de cada sistema implementado e calculados os índices de desempenho das mesmas.