Intelligence based on adaptive control

This paper considers the problem of intelligence in control from the viewpoint of adaptive control theory. First, intelligence in control is treated here as the inverse problem. There are two ways to solve the inverse problem. One is an inverse calculation, subtraction to addition, division to multiplication, and so on. The other is successive estimation. This is suitable for adaptive control because online estimation is frequently used in adaptive control, and the error is significant for intelligence. The other intelligence related to adaptive control can be found in an interaction between estimation and control, so we introduce here three types of adaptive control system, certainty equivalence, caution, and dual-type adaptive system. Moreover, we consider an interaction between adaptive systems based on decentralized adaptive control systems, and we reveal that self-similarity is crucial for intelligence in control. This work was presented, in part, at the Third International Symnposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998.     

L1 adaptive control with sliding-mode based adaptive law

This paper presents an adaptive control scheme with an integration of sliding mode control into the $\mathcal{L}_1$ adaptive control architecture, which provides good tracking performance as well as robustness against matched uncertainties. Sliding mode control is used as an adaptive law in the $\mathcal{L}_1$ adaptive control architecture, which is considered as a virtual control of error dynamics between estimated states and real states. Low-pass filtering mechanism in the control law design prevents a discontinuous signal in the adaptive law from appearing in actual control signal while maintaining control accuracy. By using sliding mode control as a virtual control of error dynamics and introducing the low-pass filtered control signal, the chattering effect is eliminated. The performance bounds between the close-loop adaptive system and the closed-loop reference system are characterized in this paper. Numerical simulation is provided to demonstrate the performance of the presented adaptive control scheme.     

On robustness to noise of least squares based adaptive control

Current engineering practice for adaptive control schemes is to base the design on globally convergent schemes for simple plant models. An important class of such schemes uses least squares estimation of assumed simple input-output models and constructs the controller using the parameter estimates. This paper studies the robustness of such schemes to the presence of unmodelled plant coloured noise. Such noise is sometimes an adequate model for unmodelled plant dynamics.The theory of the paper makes a connection between the least squares parameter error equations and those associated with extended least squares using a posteriori noise estimates for which there are known global convergence results. For the case of adaptive minimum variance control of minimum phase plants, this connection permits stronger convergence results than those hitherto derived from the theory of extended least squares based on a priori noise estimates.     

An approximate dynamic programming based approach to dual adaptive control

In this paper, an approximate dynamic programming (ADP) based strategy is applied to the dual adaptive control problem. The ADP strategy provides a computationally amenable way to build a significantly improved policy by solving dynamic programming on only those points of the hyper-state space sampled during closed-loop Monte Carlo simulations performed under known suboptimal control policies. The potentials of the ADP approach for generating a significantly improved policy are illustrated on an ARX process with unknown/varying parameters.     

From robust control to adaptive control

The paper emphasizes the interaction between robust control, identification in closed loops and adaptive control. Robust control and recent algorithms developed for plant model identification in closed loops have led to new designs of adaptive control systems. Their performances are further enhanced by the use of multiple-model adaptive control, based on switching and tuning. These developments are illustrated by their application to the control of a flexible transmission system.     

Optimization‐based adaptive sliding mode control with application to vehicle dynamics control

This paper presents the design of a new adaptive optimization‐based second‐order sliding mode control algorithm for uncertain nonlinear systems. It is designed on the basis of a second‐order sliding mode control with optimal reaching, with the aim of reducing the control effort while maintaining all the positive aspects in terms of finite‐time convergence and robustness in front of matched uncertainties. These features are beneficial to guarantee good performance in case of vehicle dynamics control, a crucial topic in the light of the increasing demand of semiautonomous and autonomous driving capabilities in commercial vehicles. The new proposal is theoretically analyzed, as well as verified relying on an extensive comparative study, carried out on a realistic simulator of a 4‐wheeled vehicle, in the case of a lateral stability control system.     

基于ESO的船舶航向鲁棒自适应控制

针对具有内部未建模动态和外部不确定扰动的水面船舶设计一种鲁棒自适应航向控制器,并同时解决转向过程中的漂角补偿问题.基于二阶非线性Nomoto模型和一阶漂角模型,建立非积分链结构的漂角-航向非线性状态空间模型,将航向控制系统未建模动态与外部不确定扰动合并为复合扰动,应用扩张状态观测器估计模型中的未测量状态和系统复合扰动.基于Lyapunov稳定性理论和自适应反步法设计航向状态反馈控制规律,为避免反步法控制过程中的微分爆炸问题,采用动态面控制技术获取虚拟控制信号的近似导数.所提出的扩张状态观测器和航向控制算法能够保证闭环系统内所有误差信号一致最终有界,提高航向保持和转向过程中的航向跟踪精度.仿真结果验证了所提出的航向控制规律的有效性.     

Induction motor control based on adaptive passivity

In this paper two new schemes for induction motor control are proposed and compared. Both approaches are based on the concept of adaptive passivity. First, a technique using the scheme of field oriented control (FOC) is proposed, and by means of an adaptive state feedback, a passive equivalent system is obtained. Furthermore, making use of the novel torque‐flux control principle (TFCP), the proposed scheme is greatly simplified. Second, a technique based on energy shaping approach, which does not make use of the FOC scheme, is proposed. The technique is based on interconnection and damping assignment (IDA) control transforming the original system into a passive one. Since this technique does not use the FOC scheme, it gives more flexibility in the implementation. Both techniques are then implemented at laboratory level and compared from experimental viewpoint using as benchmark the standard FOC scheme with PI controllers. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

基于无模型自适应控制的自动泊车方案

提出一种基于无模型自适应控制的自动泊车方案.首先,通过车载传感器采集车周环境信息用于规划期望路径;然后,将自动泊车跟踪问题转化为预瞄偏差角跟踪问题,通过设计相应的无模型自适应控制算法实现自动泊车.该方案设计的优点是仅使用自动泊车过程中生成的前轮转角输入数据和预瞄偏差角输出数据,没有使用任何被控车辆的信息,因此可适用于不同车型. Matlab仿真以及与PID控制方案和Fuzzy控制方案的对比仿真结果验证了所提出方案的可行性.     

一种基于元策略的自适应移动切换控制方案

为有效保障移动用户不同业务的服务质量,拓展策略机制的应用,利用策略形式的业务QoS属性及移动节点移动和资源需求的预测信息,用元策略自动生成相应的控制策略,实现切换过程自适应的资源管理。通过分析和仿真,该自适应切换方案在保证切换掉线率的前提下,最小化新呼叫阻塞率,为用户提供较好的QoS保证,其特点在于自适应性,可以根据当前网络状况自动进行重要参数的调整,另外,使用策略,可提供不同的优先级,能方便地进行算法定义和选择,相对于传统方法更灵活。     

Enabling control software generation by using mechatronics modeling primitives

Mechatronic systems are characterized by the synergetic integration of mechanic, electronic, software and control design aspects. The development of control software requires data and information from all design domains in order to create the required integrated functionality. This paper proposes a method that combines function modeling and multi-domain modeling primitives to generate control software automatically. An architecture model, based on the Function-Behavior-State modeling paradigm, provides the decomposition and flow of both functionality and implementation, which serves as input to a knowledge-based engineering application. The control software is subsequently extracted from a virtual product model composed of instantiated modeling primitives. A case study of a mobile robot shows how for a specific application the modeling are defined and how a high-level function model for an environment mapping mission is translated into directly implementable software code. This approach could be extended to real-life mechatronic products, and will improve consistency and reduce development time and cost.     

基于分层模糊系统的直接自适应控制

为解决模糊控制器中规则数目随系统变量呈指数增长的问题，利用分层模糊系统设计了一类非线性系统的直接自适应模糊控制器，并证明了所提出的设计方法不但能保证闭环系统的一致有界性。而且可使跟踪误差收敛到原点的小领域内，通过对倒立摆控制的仿真研究验证了该方法的有效性。     

基于自适应阻抗控制的轴孔装配方法

提出了一种基于自适应阻抗控制的轴孔装配方法,它能根据得到的力信息,按照自适应调整律对机器人末端的参考位置进行修正,使得轴始终朝着减小卡阻力的方向运动,最终完成插轴入孔的动作.其算法简单,计算量小,修正量的计算可以在线进行,故能满足实时控制的要求.仿真结果表明了这种方法的有效性.     

Deterministic adaptive control based on Laguerre series representation

The behaviour of adaptive controllers in the presence of unmodelled dynamics, and the need for reduced a priori information have led us to abandon the usual ARMA transfer function representation for a representation by an orthonormal series. The appeal of our new approach is that it eliminates the need for assumptions about the plant order and the time delay. The plant is modelled by an orthonormal Laguerre network put in state-space form. A simple predictive control law is proposed. An explicit deterministic adaptive controller is then designed. Simulations show that it is easy to use, able to handle non-minimum phase plants, and more robust than the conventional model-based approach. Although we chose Laguerre functions, other orthonormal functions may be used. We have already tested some with success.     

基于后推设计的直接自适应模糊控制

针对一类严格反馈不确定非线性动态系统，提出一种直接鲁棒自适应模糊控制新方案．利用模糊系统的逼近能力、后推设计方法和积分型李亚普诺夫函数，依次确定各虚拟控制及模糊系统中可调参数的自适应律，并最终确定出控制律．为改善控制系统的性能，引入逼近误差的自适应补偿项．通过李亚普诺夫方法，证明了闭环系统是一致终结有界的．仿真结果表明了该方法的有效性。     

基于遗传算法的多模型自适应控制

利用多个固定模型（或元素模型）来逼近含未知参数的被控系统,基于每一个元素模型建立最优控制器,并由各局部模型控制器的加权和构成被控系统的控制器。对于这种多模型自适应控制器,在每一个采样时刻,每一个元素模型的权值将由遗传算法计算得出。仿真结果表明,采用该文提出的控制器,当被控对象的模型参数剧烈变化时系统输出依然可以很好地跟踪设定值。     

基于强化学习的模型参考自适应控制

提出了一种基于强化学习的模型参考自适应控制方法,控制器采用自适应启发评价算法,它由两部分组成:自适应评价单元及联想搜索单元.由参考模型给出系统的性能指标,利用系统反馈的强化信号在线更新控制器的参数.仿真结果表明:基于强化学习的模型参考自适应控制方法可以实现对一类复杂的非线性系统的稳定控制和鲁棒控制,该控制方法不仅响应速度快,而且具有较高的学习速率,实时性较强.     

Localization based switching adaptive control for time-varyingdiscrete-time systems

In this paper a new systematic switching control approach to adaptive stabilization of linear time-varying (LTV) discrete-time systems is presented. A feature of the localization based method is its high model falsification capability, which in the case of LTI systems is manifested as the rapid convergence of the switching controller. We believe that the proposed method may help pave the way for design of practical adaptive switching controllers applicable to a wide range of linear time-invariant and time-varying systems     

Offshore crane control based on adaptive external models

An adaptive controller for cranes employed in heavy-lift offshore marine operations is presented. The control objective is to reduce the hydrodynamic slamming load acting on a payload at water entry of moonpool operations, while letting the payload track a given velocity profile. The adopted solution relies upon the use of an adaptive observer and two adaptive external models of the disturbance, employed to recover the unavailable information about the error to be regulated. As a result, the closed-loop system is rendered adaptive with respect to both the plant parameters and the frequencies of the harmonic disturbances affecting the system. A certainty-equivalence controller which makes use of the estimated parameters and the reconstructed tracking error is proposed, and the performance of the overall scheme is verified experimentally on a scale-model. Results show a remarkable improvement over a previous approach based on an observer-based internal model control of fixed structure.     

Strong tracking filter based adaptive generic model control

Generic Model Control (GMC) is a control algorithm capable of using nonlinear process model directly. Parameters in GMC controllers are easily tuned, and measurable disturbances can be compensated effectively. However, the existence of large modeling errors and unmeasurable disturbances will make the performance of GMC deteriorate. In this paper, based on the theory of Strong Tracking Filter (STF), a new approach to Adaptive Generic Model Control (AGMC) is proposed. Two AGMC schemes are developed. The first is a parameter-estimation-based AGMC. After introducing a new concept of Input Equivalent Disturbance (IED), another AGMC scheme called IED-estimation-based AGMC is further proposed. The unmeasurable disturbance and structural process/model mismatches can be effectively overcome by the second AGMC scheme. The laboratory experimental results on a three-tank-system demonstrate the effectiveness of the proposed AGMC approach.