Optimal structured static state-feedback control design with limited model information for fully-actuated systems

We introduce the family of limited model information control design methods, which construct controllers by accessing the plant’s model in a constrained way, according to a given design graph. We investigate the closed-loop performance achievable by such control design methods for fully-actuated discrete-time linear time-invariant systems, under a separable quadratic cost. We restrict our study to control design methods which produce structured static state feedback controllers, where each subcontroller can at least access the state measurements of those subsystems that affect its corresponding subsystem. We compute the optimal control design strategy (in terms of the competitive ratio and domination metrics) when the control designer has access to the local model information and the global interconnection structure of the plant-to-be-controlled. Finally, we study the trade-off between the amount of model information exploited by a control design method and the best closed-loop performance (in terms of the competitive ratio) of controllers it can produce.     

Robust Stabilization of Uncertain Fuzzy Systems Using Variable Structure System Approach

Based on the variable structure system (VSS) theory, we develop a fuzzy control system design method for a class of uncertain nonlinear multivariable systems that can be represented by a Takagi-Sugeno fuzzy model. We make the first attempt to relax the restrictive assumption that each nominal local system model shares the same input channel, which is required in the traditional VSS-based fuzzy control design methods. As the local controller we use a sliding mode controller with a switching feedback control term. In terms of linear matrix inequalities (LMIs), we derive a sufficient condition for the existence of linear sliding surfaces guaranteeing asymptotic stability of the reduced-order equivalent sliding mode dynamics. We present an LMI characterization of such sliding surfaces. We also give an LMI-based algorithm to design the switching feedback control term so that a stable sliding motion is induced in finite time. Finally, we give a numerical design example.     

Integrating structure and control design to achieve mixed H2/H performance

Present technology in structure design (smart structures, civil structures and aerospace structures) includes the use of feedback control. While retrofitting such active elements can be useful in existing structures, future designs will require something more than retrofitting technology. Future technology will certainly require a more systematic integration of the design of a structure and its active elements. This paper provides a step in that direction. We seek to integrate the design of the structure with its active elements to achieve mixed H2/H performance for the controlled structural system (closed loop system). More specificaly, the approach presented here solves a mixed passive control (structure design) and active control (feedback control law design) problems with performance characterized by system norms such that H performance bounds are guaranteed with les active energy. This approach al ows us to answer the question 'what is an optimal distribution of mass, stiffness, damping and control energy throughout a structure?' The main conclusion drew in this paper is that control design tools can be useful for structure design.     

Adaptive H∞ control with application to systemswith structural flexibility

We develop a robust adaptive control algorithm using a combination of H_∞ design and system identification. We derive frequency dependent bounds for the tolerated unmodeled dynamics and show that the approach gives a closed-loop system with bounded l_∞ , and l₂ gain when the model mismatch is small in the frequency range where the control gain is large. Our application focus is systems with structural flexibility. We present a parameter estimation algorithm that uses constrained least squares with prefiltering to overcome the problem of identifying lightly damped antiresonances (a common problem in identification of flexible systems). The estimation and control design are executed at a low frequency and only when parameter updating is needed. This allows us to apply computationally expensive control design and signal processing algorithms. It also eliminates many of the problems of earlier adaptive controllers (such as bursting, parameter drift, etc.) by turning off the estimator. We show results from the application of adaptive H_∞ control to a high-fidelity model of the Martin Marietta flexible beam testbed     

Adaptive controller design for uncertain fuzzy systems using variable structure control approach

Based on the variable structure control (VSC) theory, we develop an adaptive fuzzy control system design method for uncertain Takagi-Sugeno fuzzy models with norm-bounded uncertainties. We relax the restrictive assumptions that each nominal local system model shares the same input channel and the norm bound of the uncertainty is known, which are usually invoked in the traditional VSC-based fuzzy control design methods. As the local controller we use a VSC law with a switching feedback control term and an adaptation law to account for the norm-bounded uncertainties. In terms of LMIs, we derive a sufficient condition for the existence of linear sliding surfaces guaranteeing the asymptotic stability. We present an LMI characterization of such sliding surfaces. We also give an LMI-based design algorithm, together with a numerical design example.     

Robust control method for the inverted pendulum system with structured uncertainty caused by measurement error

In this article, we propose a design method for an inverted pendulum system with a structured uncertainty. We consider that such an uncertainty is caused by a measurement error in the rotation angle of the pendulum and effects on the system structure that cannot be included in the nominal elements. For the uncertain system obtained, we apply an integral tracking control and the guaranteed cost control to design a robust, stable, tracking control system. Finally, we show the effectiveness of our method through a numerical example.     

Model-driven migration of supervisory machine control architectures

Supervisory machine control is the high-level control in advanced manufacturing machines that is responsible for the coordination of manufacturing activities. Traditionally, the design of such control systems is based on finite state machines. An alternative, more flexible approach is based on task-resource models. This paper describes an approach for the migration of supervisory machine control architectures towards this alternative approach. We propose a generic migration approach based on model transformations that includes normalisation of legacy architectures before their actual transformation. To this end, we identify a number of key concerns for supervisory machine control and a corresponding normalised design idiom. As such, our migration approach constitutes a series of model transformations, for which we define transformation rules. We illustrate the applicability of this model-driven approach by migrating (part of) the supervisory control architecture of an advanced manufacturing machine: a wafer scanner developed by ASML. This migration, towards a product-line architecture, includes a change in architectural paradigm from finite state machines to task-resource systems.     

线性时滞系统的耗散控制

研究了一类线性时滞系统的二次耗散控制问题,基于线性矩阵不等式（LMI）方法导出了耗散控制器存在的充分条件,通过线性矩阵不等式的可行解构造出耗散态状态反馈和动态输出反馈控制律,相应的闭环系统是二次稳定和严格（Q,S,R）耗散的,本文的主要贡献是统一了线性时滞系统现有的H∞控制和无源控制结果。     

一类不确定性非线性网络控制系统的扰动抑制

研究受外部持续扰动的一类不确定性非线性网络控制系统的扰动抑制问题.提出一种@状态变量代换,将控制时滞转移到闭环控制回路之外,从而消除了时滞部分对控制系统稳定性的影响.利用内模原理给出了系统无静差扰动抑制补偿器的设计方法,运用Lyapunov稳定性理论和线性矩阵不等式技术,证明了保成本控制律的存在条件,并给出了无静差保成本控制器的设计方法.仿真结果验证了该控制算法的有效性.     

Ontological reasoning in the design space exploration of advanced cyber–physical systems

Cyber–physical systems are becoming increasingly complex. In these advanced systems, the different engineering domains involved in the design process become more and more intertwined. Therefore, a traditional (sequential) design process becomes inefficient in finding good design options. Instead, an integrated approach is needed where parameters in multiple different engineering domains can be chosen, evaluated, and optimized to achieve a good overall solution. However, in such an approach, the combined design space becomes vast. As such, methods are needed to mitigate this problem.In this paper, we show a method for systematically capturing and updating domain knowledge in the context of a co-design process involving different engineering domains, i.e. control and embedded. We rely on ontologies to reason about the relationships between parameters in the different domains. This allows us to derive a stepwise design space exploration workflow where this domain knowledge is used to quickly reduce the design space to a subset of likely good candidates. We illustrate our approach by applying it to the design space exploration process for an advanced electric motor control system and its deployment on embedded hardware.     

Sliding mode control based on a modified linear control input

In this study, we explain and demonstrate a design method of sliding mode control based on a modified linear control input. In the proposed method, the optimal gain matrix is derived such that it does not depend on the plant parameters. We confirmed the robustness of the proposed method by applying input-side disturbances and plant parameter deviations to plants and the effectiveness of the proposed method by performing a DC motor position control experiment     

Control co-design of 13 MW downwind two-bladed rotors to achieve 25% reduction in levelized cost of wind energy

Wind energy is recognized worldwide as cost-effective and environmentally friendly and is among the fastest-growing sources of electrical energy. To further decrease the cost of wind energy, wind turbines are being designed at ever larger scales, which is challenging due to greater structural loads and deflections. Large-scale systems such as modern wind turbines increasingly require a control co-design approach, whereby the system design and control design are performed in a more integrated fashion. We overview a two-bladed downwind morphing rotor concept that is expected to lower the cost of energy at wind turbine sizes beyond 13 megawatts (MW) compared with continued upscaling of traditional three-bladed upwind rotor designs. We describe an aero-structural-control co-design process that we have used in designing such extreme-scale wind turbines, and we discuss how we were able to achieve a 25% reduction in levelized cost of energy for our final turbine design compared to a conventional upwind three-bladed rotor design.     

LMI‐based robust sliding control for mismatched uncertain nonlinear systems using fuzzy models

We propose a robust sliding control design method for uncertain Takagi–Sugeno fuzzy models. The uncertain fuzzy systems under consideration have mismatched parameter uncertainties in the state matrix and external disturbances. We make the first attempt to relax the restrictive assumption that each nominal local system model shares the same input channel, which is required in the traditional VSS‐based fuzzy control design methods. We derive the existence conditions of linear sliding surfaces guaranteeing the asymptotic stability in terms of constrained LMIs. We present an LMI characterization of such sliding surfaces. Also, an LMI‐based algorithm is given to design the switching feedback control term so that a stable sliding motion is induced in finite time. Finally, we give two simulation results to show the effectiveness of the proposed method. Copyright © 2011 John Wiley & Sons, Ltd.     

Sequential tuning methods of LQ/LQI controllers for multivariable systems and their application to hot strip mills

In this study, we propose such sequential tuning methods of multivariable optimal regulators that can be applied to the tuning of control systems under operation. In such tuning, it is desirable to change feedback gains only step by step, confirming that the control performance is actually improved in each step. The first method we propose is such that we design an optimal single-input regulator in each step, by paying attention to only one input of the plant while the feedback laws to other inputs are fixed to those obtained in the previous sequential tuning steps. On the other hand, the second method is such that all elements of the feedback gain are changed at once, while we are given the design freedom about how much we are to change the gain. These two methods as well as their combined use are shown to lead to the optimal gain as a multivariable control system eventually, provided that the sequential tuning steps are repeated sufficiently many times. We apply these methods to the tuning of LQI servo systems, and carry out the simulation study of the control of a hot strip mill to illustrate the tuning law and show its effectiveness.     

Nonlinear inferential multi-rate control of Kappa number at multiple locations in a continuous pulp digester

The continuous pulp digester represents a large-scale, distributed parameter system. Control of the spatial profile of degree of cooking, characterized by the Kappa number, rather than its endpoint value can effectively control properties that are dependent on the history of cooking. However, profile control of such large-scale distributed parameter systems throws up new challenges in estimation and control. We design a nonlinear model predictive controller using a multi-rate extended Kalman filter to infer and control discrete points along the Kappa number profile. Both, the plant and controller models are based on first principles. The design is tested for significant mismatches in parameters, initial state errors, and stochastic disturbances in the entering wood composition.     

非线性系统鲁棒耗散控制

基于二次型供给率,研究了不确定非线性系统的鲁棒耗散控制问题。不确定项用界范数来刻画。基于Hamilton-Jacobi不等式,得到了实现鲁棒耗散控制的充分条件及控制器的设计算法。     

Tracking control for switched linear systems with time‐delay: a state‐dependent switching method

Tracking control for switched linear systems with time‐delay is investigated in this paper. Based on the state‐dependent switching method, sufficient conditions for the solvability of the tracking control problem are given. We use single Lyapunov function technique and a typical hysteresis switching law to design a tracking control law such that the H_∞ model reference tracking performance is satisfied. The controller design problem can be solved efficiently by using linear matrices inequalities. Since convex combination techniques are used to derive the delay independent criteria, some subsystems are allowed to be unstable. It is highly desirable that a non‐switched time‐delay system can not earn such property. Simulation example shows the feasibility and validity of the switching control law. Copyright © 2009 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Robust iterative learning controller design using the performance weighting function of feedback control systems

Iterative learning controllers combined with existing feedback controllers have prominent capability of improving tracking performance in repeated tasks. However, the iterative learning controller has been designed without utilizing effective information such as the performance weighting function to design a feedback controller. In this paper, we deal with a robust iterative learning controller design problem for an uncertain feedback control system using its explicit performance information. We first propose a robust convergence condition in the ?₂-norm sense for an iterative learning control (ILC) scheme. We present a method to design an iterative learning controller using the information on the performance of the existing feedback control system such as performance weighting functions and frequency ranges of desired trajectories. From the obtained results, several design criteria for iterative learning controller are provided. Through analysis on the remaining error, the loop properties before and after learning are compared. We also show that, in the ?₂-norm sense, the remaining error can be less than the initial error under certain conditions. Finally, to show the validity of the proposed method, simulation studies are performed.     

Nonlinear internal model control using neural networks: applicationto processes with delay and design issues

We propose a design procedure of neural internal model control systems for stable processes with delay. We show that the design of such nonadaptive indirect control systems necessitates only the training of the inverse of the model deprived from its delay, and that the presence of the delay thus does not increase the order of the inverse. The controller is then obtained by cascading this inverse with a rallying model which imposes the regulation dynamic behavior and ensures the robustness of the stability. A change in the desired regulation dynamic behavior, or an improvement of the stability, can be obtained by simply tuning the rallying model, without retraining the whole model reference controller. The robustness properties of internal model control systems being obtained when the inverse is perfect, we detail the precautions which must be taken for the training of the inverse so that it is accurate in the whole space visited during operation with the process. In the same spirit, we make an emphasis on neural models affine in the control input, whose perfect inverse is derived without training. The control of simulated processes illustrates the proposed design procedure and the properties of the neural internal model control system for processes without and with delay.