Design of force control system using tendon‐driven mechanism including linear springs and ultrasonic motor

Actuators that have characteristics such as light weight, high thrust to weight ratio, and force controllability are desirable for applications such as finger robots and power assist suits. Recently, an indirect force control method using an ultrasonic motor (USM) and a spring has been attracting considerable attention because this method has the potential to satisfy all of the desirable characteristics, namely light weight, high thrust to weight ratio, and force controllability. The USM controls the length of the spring, and the tensional force of the spring is indirectly controlled by the length. As the resonance frequencies of indirect force control systems are low due to the springs, it is difficult to select a high feedback gain. This research therefore proposes a novel resonance ratio controller for indirect force control systems. The validity of the controller is verified by simulation and experiment. The overshoot ratio of the conventional controller is more than 10%, while that of the proposed controller is around 1%, when the rise time is 0.30 s.     

Model‐based vs data‐driven adaptive control: An overview

In this paper, we present an overview of adaptive control by contrasting model‐based approaches with data‐driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model‐based adaptive control and data‐driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield.     

Anti‐windup synthesis for a model predictive control system

In this paper, an anti‐windup design problem for a model predictive control system is studied. The plant is assumed to be stable. First, we propose the structure of an output feedback model predictive controller with an anti‐windup compensator. Then we show a design method of the anti‐windup compensator that guarantees closed‐loop stability and improves the transient response. The design problem of the anti‐windup compensator is reduced to a linear matrix inequality (LMI) optimization problem. Further, it is shown that there always exists an anti‐windup compensator that ensures global asymptotic stability. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Model‐free fault‐tolerant control approach for uncertain state‐constrained linear systems with actuator faults

This paper investigates the robust adaptive fault‐tolerant control problem for state‐constrained continuous‐time linear systems with parameter uncertainties, external disturbances, and actuator faults including stuck, outage, and loss of effectiveness. It is assumed that the knowledge of the system matrices, as well as the upper bounds of the disturbances and faults, is unknown. By incorporating a barrier‐function like term into the Lyapunov function design, a novel model‐free fault‐tolerant control scheme is proposed in a parameter‐dependent form, and the state constraint requirements are guaranteed. The time‐varying parameters are adjusted online based on an adaptive method to prevent the states from violating the constraints and compensate automatically the uncertainties, disturbances, and actuator faults. The time‐invariant parameters solved by using data‐based policy iteration algorithm are introduced for helping to stabilize the system. Furthermore, it is shown that the states converge asymptotically to zero without transgression of the constraints and all signals in the resulting closed‐loop system are uniformly bounded. Finally, two simulation examples are provided to show the effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Application of model‐free LQG subspace predictive control to TCP congestion control

We investigate the application of a model‐free linear quadratic Gaussian (LQG) subspace‐based predictive controller to Internet congestion control. Specifically, we consider a classically designed LQG linear congestion controller with a non‐standard performance index and determine whether a model‐free controller is a viable alternative in this instance. We employ the model‐free subspace predictive controller methodology which we customize for end‐to‐end transmission control protocol (TCP) congestion control. A series of network simulations support the use of the more easily implementable model‐free controller over its classical analogue. We further demonstrate that the model‐free controller provides increased stability under transient network conditions when compared with the first feedback congestion controller, TCP Vegas. Copyright © 2007 John Wiley & Sons, Ltd.     

Accommodation of unknown actuator faults using output feedback‐based adaptive robust control

In this paper, we solve the problem of output tracking for linear uncertain systems in the presence of unknown actuator failures using discontinuous projection‐based output feedback adaptive robust control (ARC). The faulty actuators are characterized as unknown inputs stuck at unknown values experiencing bounded disturbance and actuators losing effectiveness at unknown instants of time. Many existing techniques to solve this problem use model reference adaptive control (MRAC), which may not be well suited for handling various disturbances and modeling errors inherent to any realistic system model. Robust control‐based fault‐tolerant schemes have guaranteed transient performance and are capable of dealing with modeling errors to certain degrees. But, the steady‐state tracking accuracy of robust controllers, e.g. sliding mode controller, is limited. In comparison, the backstepping‐based output feedback adaptive robust fault‐tolerant control (ARFTC) strategy presented here can effectively deal with such uncertainties and overcome the drawbacks of individual adaptive and robust controls. Comparative simulation studies are performed on a linearized Boeing 747 model, which shows the effectiveness of the proposed scheme. Copyright © 2011 John Wiley & Sons, Ltd.     

An engine control systems design for low‐emission vehicles by generalized predictive control based on identified model

Conservation of the environment has become critical to the automotive industry. Recently, requirements for on‐board diagnostic and engine control systems have been strictly enforced. In the present paper, in order to meet the requirements for low‐emission vehicles, a novel method for construction of the air‐fuel ratio (A/F) control system is proposed. The method of construction of the system is divided into two steps. The first step is to design the A/F control system for the engine based on an open loop design. The second step is to design the A/F control system for the catalyst system. The design method is based on generalized predictive control in order to assure robustness to open loop control as well as model uncertainty. The effectiveness of the proposed A/F control system is verified through experiments using full‐scale products. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 169(3): 47–55, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20907     

Data‐driven model reference control with asymptotically guaranteed stability

This paper presents a data‐driven controller tuning method that includes a set of constraints for ensuring closed‐loop stability. The approach requires a single experiment and can also be applied to nonminimum‐phase and unstable systems. The tuning scheme generates an estimate of the closed‐loop output error that is used to minimize an approximation of the model reference control problem. The correlation approach is used to deal with the influence of measurement noise. For linearly parameterized controllers, this leads to a convex optimization problem. A sufficient condition for closed‐loop stability is introduced, which can be included in the optimization problem for control design. As the data length tends to infinity, closed‐loop stability is guaranteed. The quality of the estimated controller is analyzed for finite data length. The effectiveness of the proposed method is demonstrated in simulation as well as experimentally on a laboratory‐scale mechanical setup. Copyright © 2010 John Wiley & Sons, Ltd.     

An output‐feedback adaptive control architecture for mitigating actuator attacks in cyber‐physical systems

In this paper, we present a control design framework wherein an adaptive‐based corrective signal is augmented to the output of the nominal controller in order to suppress or counteract the effect of malicious actuator attacks. Due to the unavailability of full‐state measurements, a nonminimal controllable realization of the nominal closed‐loop system is used to design the corrective signal predicated on partial state information. Two illustrative numerical examples are given to demonstrate the efficacy of the proposed adaptive control architecture.     

A design method of a generalized predictive control system based on parametrization of two‐degree‐of‐freedom integral controllers

A new design method for a generalized predictive control (GPC) system based on parametrization of two‐degree‐of‐freedom integral controllers has been proposed. The objective is to guarantee stability of the control system without depending on the design parameters and to achieve low sensitivity against the plant perturbation and the disturbance. The design procedure consists of two steps. First, we design a basic integral controller for a nominal plant using the linear quadratic Gaussian (LQG) method and parametrize a class of two‐degree‐of‐freedom stabilizing controllers. Next, we tune the feedforward controller to incorporate the GPC method into our control structure. A numerical example is presented to show the effectiveness of the proposed method by comparing it with the conventional GPC method. © 1999 Scripta Technica, Electr Eng Jpn, 129(2): 62–70, 1999     

基于小波基的非线性抛物型系统模型预测控制

针对一类由非线性抛物型描述的分布参数系统,研究了一种基于小波分解的模型降阶和预测控制方法。利用小波配点方法,分别将一阶和二阶空间偏导数投影到拟Shannon小波基上,不需要求解系统的主导极点,得到系统的低阶常微分方程逼近模型;采用前向Eular方法离散化时间变量,将得到的差分方程组模型作为系统的预测模型,选择标准二次优化性能指标,设计相应的非线性预测控制器;将此方法应用到由一个放置在反应器中的细长催化棒组成的传输-反应系统的温度场控制问题中,取得了满意的控制效果。     

A comparison of model‐based and data‐driven controller tuning

In many industrial applications, finding a model from physical laws that is both simple and reliable for control design is a hard and time‐consuming undertaking. When a set of input/output measurements is available, one can derive the controller directly from data, without relying on the knowledge of the physics. In the scientific literature, two main approaches have been proposed for control system design from data. In the ‘model‐based’ approach, a model of the system is first derived from data and then a controller is computed‐based on the model. In the ‘data‐driven’ approach, the controller is directly computed from data. In this work, the previous approaches are compared from a novel perspective. The main finding of the paper is that, although from the standard perspective of parameter variance analysis the model‐based approach is always statistically more efficient, the data‐driven controller might outperform the model‐based solution for what concerns the final control cost. Copyright © 2013 John Wiley & Sons, Ltd.     

Output shaping in continuous‐time predictive control

In many control applications, it is expected that the system output tracks a desired reference signal. This paper describes a new approach in which the tracking problem is formulated as an output shaping problem. The approach uses the framework of predictive control and imposes the desired reference signal as a constraint on the output. Some simulation results and a real‐time application are also given to illustrate the effectiveness of the proposed method. Copyright © 2007 John Wiley & Sons, Ltd.     

输入受限的非线性系统模型预测控制

基于模糊T—S模型对输入受限的非线性离散系统,提出了模型预测控制,导出了预测控制性能指标上界,将稳定性约束、输入约束变换成容易求解的线性矩阵不等式(LMIs)形式。采用了状态反馈控制器和并行补偿分布控制器(PDC),基于李雅普诺夫函数和线性矩阵不等式方法给出滚动时域优化的充分条件,证明了闭环系统的稳定性。仿真结果验证了所提方法的有效性。     

单元机组协调系统的模型预测控制

单元机组中的锅炉-汽轮机协调系统,具有滞后、耦合、时变以及模型不确定性等特点。针对该协调过程,设计了一种预测控制策略,它利用预测模型与滚动优化策略,减小了模型不确定性、时变以及滞后等因素的影响。最后通过300 MW单元机组协调控制系统的仿真实验,验证了所设计控制算法的性能。结果表明在机组工况发生变化或受到干扰时,被控系统具有快速的负荷适应性、良好的抗扰动能力和很强的鲁棒性,是一种具有实用价值的单元机组协调控制方法。     

适用于Boost三电平变换器的模型预测控制方法

相比于传统的双闭环控制,模型预测控制(MPC)具有动态响应快、无需调节PI参数以及可以增加系统状态变量约束等优点,被广泛应用于DC-DC变换器的控制中。而在大多数的研究中,一般采用的是有限控制集模型预测控制(FCS-MPC)方法,但这种控制方法无法提供固定的开关频率。因此,针对Boost三电平变换器,提出了一种基于连续控制集模型预测控制(CCS-MPC)的固定开关频率控制方法,其可同时实现输出电压控制以及平衡输出侧中点电位2个控制目标,并且在评价函数中无需调节不同控制目标的权重系数,简化了控制器参数设计难度。通过MATLAB/Simulink软件和dSPACE实时仿真系统进行仿真与实验,结果都证明了理论分析的正确性。     

基于预测模型的STATCOM功率控制策略建模与仿真

将基于预测模型的功率控制策略(Predictive power control,PPC)应用于静止同步补偿器(Static synchronous compensator,STATCOM)中,来解决传统直接功率控制策略中存在的功率脉动过大、开关频率不固定等问题。在分析STATCOM基本结构的基础上,结合实际数字处理系统离散运行的特点,推导出STATCOM系统的功率预测方程,构建一个使k+1采样点功率跟踪误差最小为目标的占空比求取函数,然后利用SVPWM技术调制出波形控制(Voltage source inverter,VSI),以达到系统的稳定运行。最后,使用Matlab搭建了STATCOM系统仿真模型,仿真结果表明所提PPC策略有效地抑制了系统的功率脉动、恒定系统的开关频率,同时其保留了传统DPC策略高动态响应的优异特性。     

基于广域测量信息的互联电网模型预测阻尼控制

提出一种基于模型预测控制的新型阻尼控制器设计方法。为抑制区间低频振荡,该控制器由2级PSS构成,其输入信号由广域测量信号和本地测量信号组成。采用留数矩阵法选择广域控制回路,通过模式辨识进行2级PSS控制。将常规励磁系统中传统的自动电压调节器(automatic voltage regulator,AVR)用模型预测控制器(model predictive controller,MPC)替代,并与2级PSS协调作用,通过模型预测和优化求解,得到励磁控制器的最优控制输入,以实现电压调节和增强阻尼之间的动态协调。典型的四机两区系统仿真结果表明,基于模型预测控制的2级PSS设计的控制效果明显优于传统的阻尼控制,可以快速调节机端电压,显著改善互联电网阻尼。     

Model predictive control based switching of multimodal controllers in switched systems

In real systems, there exist physical constraints such as voltage limitations imposed on actuators or state restriction due to their structure. Violation constraints in systems may cause instability, or in the worst case, result in malfunction. In this paper, a switching algorithm of multimodal controllers in switched systems is proposed. The method is based on the model predictive control. Both simulation and experimental results illustrate the effectiveness of the proposed method. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 166(3): 73–81, 2009; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20702     

Refinement of inverter model considering dead‐time for performance improvement in predictive instantaneous current control

Model predictive instantaneous‐current control (MPIC), which was proposed in our earlier works, enables us to achieve better instantaneous current control using mathematical models of an inverter and permanent magnet synchronous motors (PMSMs). However, dead‐time to avoid short breakdown in the inverter is not usually considered in a general inverter model. Such an unmodeled part in the inverter model prevents accurate prediction of current evolution in motor systems based on the model predictive control. Therefore, in this paper, we analyze current response resulting from the dead‐time in the MPIC, and propose a refined inverter model considering the dead‐time so that control performance is improved. The effectiveness of the proposed method is verified through simulation and experiments. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.