Non-zero set-point affine feedback control of impulsive systems with application to biomedical processes

Linear impulsive systems have been extensively studied in the last decades, mainly in the field of biomedical research. However, a proper characterisation of the equilibria of such a systems - when they are out of the origin - and its use by optimising control strategies is still a matter of discussion. In this work, a novel characterisation of the system equilibria and invariant regions - derived from the definition of two underlying discrete-time systems - is given, and based on this characterisation impulsive affine feedback control strategies for non-zero set-points are designed. The closed-loop performance and benefits of the strategies are assessed through two biomedical examples: the Lithium ions distribution in the human body and the HIV treatment.     

一种具有设定值加权的IMC-PID控制方法

提出了一种具有模糊逻辑设定值加权的内模PID控制方法.该方法通过模糊系统的输出在线修正内模PID控制器比例作用部分设定值的加权系数及人工调节控制器的滤波器系数,使系统的目标值跟踪特性和干扰抑制特性均得到明显的改善.改进了常规内模PID 控制的不足,而且控制算法简单、参数调整方便,理论分析和仿真试验研究结果表明了它的有效性.     

基于速度调节的无人帆船机器人自适应航向保持控制

为了解决实际海洋环境下,无人帆船机器人(USR)航向保持控制任务中存在的模型结构未知、参数不确定和航行速度难以控制等问题,本文提出一种具有速度调节性能的鲁棒自适应航向保持控制算法.该算法采用径向基(RBF)神经网络对系统结构不确定进行逼近,由于引入鲁棒神经阻尼技术和动态面控制技术,使得闭环控制系统仅需要两个自适应参数对执行器的增益不确定部分进行在线补偿,并且不需要对神经网络权重参数进行学习更新.所提出的控制算法能够有效控制无人帆船以期望的航行速度达到设定航向.利用Lyapunov稳定性理论证明了所提出控制器能够保证闭环控制系统中相关误差变量满足半全局一致最终有界(SGUUB)收敛.通过在模拟海洋环境干扰下进行计算机仿真研究,验证了所提出算法具有良好的速度调节性能和鲁棒性.     

Multivariable PID control with set-point weighting via BMI optimisation

The paper focuses on the design of multivariable PID controllers with set-point weighting. The advantage of this PID structure is that the responses of the system to disturbances and to changes in the set-point can be adjusted separately. The proposed design methods rely on the transformation of the tuning of the controller gains into a static output feedback (SOF) problem. Hence, multivariable PID controllers can be designed by solving an optimisation problem with bilinear matrix inequalities (BMIs). The paper addresses the design of both time-invariant and gain-scheduled robust controllers. All of the tuning methods discussed through the paper are based on a PID structure with filtered derivative term, thus guaranteeing the well-posedness of the closed loop system.     

Application of the VFO method to set-point control for the N-trailer vehicle with off-axle hitching

In this article the cascaded set-point feedback controller has been proposed for kinematics of the articulated mobile vehicle equipped with arbitrary number of off-axle hitched trailers. The concept results from geometrical features of the vehicle model, and from application of the Vector-Field-Orientation method in the outer loop of the cascaded controller. Solution presented in this article is formulated in the original configuration space of the vehicle not involving any auxiliary state or input transformation. It guarantees convergence of the last vehicle segment to the prescribed neighbourhood of a desired posture along with approximate straightening of a vehicle kinematic chain during approaching the destination. Practical limits of the control input amplitudes have been taken into account during the control law derivation by the application of a simple on-line scaling procedure to the nominally computed control functions. Results of numerical simulations illustrate effectiveness of the method for a 3-trailer vehicle.     

Adaptive variable structure set-point control of underactuatedrobots

Control of underactuated mechanical systems (robots) represents an important class of control problem. In this correspondence, a model-based adaptive variable structure control scheme is introduced, where the uncertainty bounds only depend on the inertia parameters of the system. Global asymptotic stability is established in the Lyapunov sense. Numerical simulations are conducted to validate the theoretical analysis     

具有数据包丢失的网络控制系统保性能控制

本文研究了一类具有数据包丢失的网络控制系统(NCSs)的建模和保性能控制问题.通过用两个马尔可夫链分别来描述前向通道和反馈通道的丢包过程,将闭环网络控制系统建模成具有两个模式的马尔可夫随机切换系统.基于线性矩阵不等式技术和李亚普诺夫方法得到了闭环系统随机稳定的充分条件,并给出了状态反馈保性能控制器的设计方法.最后通过数值算例验证本文结果的有效性.     

时滞复杂动态网络的保性能控制

针对具有时滞的复杂网络,研究了此类系统的保性能控制问题。利用Lyapunov稳定性定理和线性矩阵不等式工具,得到了时滞复杂网络保性能控制存在的充分条件。设计的具有增益摄动的状态反馈控制器,既可以保证系统渐近稳定,又可使系统的性能指标满足一定的要求,最后通过数值算例验证了设计方法的可行性。     

输入受限的非仿射无人帆船航向系统自适应动态面控制

针对输入受限和控制方向未知的无人帆船航向控制问题,考虑系统模型存在动态不确定和未知外界扰动的情况,本文提出一种基于非仿射航向运动数学模型的最小参数自适应递归滑模动态面控制策略.该策略通过Taylor展开方法将非仿射模型转化为具有线性结构的仿射时变系统,采用最小参数学习(minimal learning parameter,MLP)神经网络逼近无人帆船模型不确定部分,并利用双曲正切函数处理控制输入饱和现象,引入Nussbaum函数处理系统中未知控制方向问题,同时综合考虑帆船艏摇角速度误差和航向误差之间关系设计递归滑模动态面舵角控制律,并设计参数自适应律对神经网络逼近误差与复合干扰总和的界进行估计.选取李雅普诺夫函数证明了所设计控制器能够保证航向闭环系统内所有信号的一致最终有界性.最后,基于一艘12 m无人帆船进行仿真验证,结果表明无人帆船航向控制响应速度快,所设计的控制器能有效地处理模型不确定项和风浪等外界扰动,具有较强的鲁棒性.     

不确定时延网络控制系统的保性能控制

在工程实际网络控制系统中,不仅要求系统能够保持稳定,还要系统具有一定的性能指标,采用状态反馈控制是一种非常有效的控制方式。针对一类线性不确定时延网络控制系统,研究了在有记忆状态反馈控制器下的保性能控制问题。利用Lyapunov函数和线性矩阵不等式的方法,给出了网络控制系统保性能控制律存在的条件和相应的状态反馈控制器的设计方法。最后,通过Matlab数值仿真算例验证了该方法的有效性和正确性。     

具有传感器故障的网络控制系统保性能可靠控制

基于存在时延和丢包的网络传输环境.针对具有参数不确定性的网络化控制系统,研究了其在传感器故障条件下的保性能可靠控制问题.根据Lyapunov稳定性理论和线性矩阵不等式(LMIs)方法,推导出使闭环网络控制系统在传感器故障条件下渐近稳定且保证综合性能指标满足要求的充分条件,并利用LMIs提出了保性能可靠控制率的设计方法.该控制算法在提高网络化控制系统可靠性的同时有利于系统综合体性能的优化.数值仿真验证了该方法的可行性和有效性.     

数据包丢失网络控制系统的保成本控制

针对同时具有不确定短时延及数据包丢失的网络控制系统,引入丢包补偿器补偿丢包对系统的影响,当发生数据包丢失时,控制器采用丢包补偿器产生的预估状态代替系统的真实状态计算控制量。同时考虑不确定短时延的影响,将网络控制系统建模为一类具有参数不确定的离散切换系统．利用线性矩阵不等式(linear matrix inequality,简称LMI)方法给出了系统的保成本控制器和丢包补偿器的协同设计方法,仿真结果表明所提出的方法是有效的。     

高性能无人帆船软硬件设计

设计了一套基于嵌入式系统PC/104和ARM微处理器的无人帆船控制系统,该系统可实时采集无人帆船运动控制所需多路传感器信号,经处理后生成运动控制序列指令,由ARM产生PWM信号或者高低电平信号,驱动执行器动作,完成无人帆船运动的精确控制。采用PC/104作为中央处理器,可有效节省布置空间,保证较高的可靠性和实时性,优于当前普遍使用的无人帆船控制系统。采用5 GHz无线路由器构建局域网,覆盖范围广,信道纯净,干扰较少。结果表明,该控制系统能够达到良好的运动控制效果。且该套系统具备高度可移植性和扩展性,可作为无人帆船控制实验平台的参考,也可实用化为海上超长航程复合任务平台。     

Guaranteed cost control of systems with norm bounded uncertainties

Feedback controller design based on an imprecisely known plant model may cause performance degradation and even instability of the closed-loop system. It is therefore important to design robust controllers using a priori information on the model uncertainties. The method presented in this paper considers uncertainties satisfying bounding constraints, and ensures closed-loop stability as well as satisfactory performances characterized by a quadratic cost functional that remains bounded in the presence of uncertainties of the above class.     

On the control applications of a determinant equality related to eigenvalue computation

A determinant equality known in linear algebra is shown to be an effective tool for control engineers in reducing complexity of eigenvalue computation and increasing insight into system behavior. Included are its applications to matrix products and singular matrices, to the study of systems with poles at the origin, and to the problem of finding the characteristic equation of an optimal regulator problem.     

Guaranteed cost control of uncertain discrete systems with delays

In this paper, we address the problems of robust performance analysis and control synthesis for a class of discrete-time systems with parameter uncertainty and unknown state-delay. The parameter uncertainty is real time-varying normbounded and the state-delay is a constant within a prescribed interval. For the robust performance analysis problem, we adopt an appropriate notion of robust stability for discrete-time uncertain time-delay systems and show that this notion guarantees an upper bound on a linear quadratic cost function. For the control synthesis problem, we show that a robust state-feedback controller can be constructed to render the closed-loop system robustly stable while guaranteeing a prescribed level of performance. The developed results are expressed in terms of algebraic matrix inequalities. A numerical example is presented to illustrate the theory.     

具有随机长时延的网络控制系统保性能控制

本文研究了一类具有随机长时延离散时间网络控制系统(NCSs)的建模和保性能控制问题.用两个马尔可夫链分别来描述反馈通道和前向通道的网络诱导时延,基于采用现有最新数据的原则,将闭环NCS建模成一个与当前时刻和过去时刻网络诱导时延有关的马尔可夫随机时滞系统.应用线性矩阵不等式技术和李亚普诺夫方法得到了闭环NCS随机稳定且具有二次性能指标上界的充分条件,并给出了状态反馈保性能控制器的设计方法.最后,通过数值算例验证了本文方法的有效性.     

Online set-point optimisation cooperating with predictive control of a yeast fermentation process: A neural network approach

Online set-point optimisation which cooperates with model predictive control (MPC) and its application to a yeast fermentation process are described. A computationally efficient multilayer control system structure with adaptive steady-state target optimisation (ASSTO) and a suboptimal MPC algorithm are presented in which two neural models of the process are used. For set-point optimisation, a steady-state neural model is linearised online and the set-point is calculated from a linear programming problem. For MPC, a dynamic neural model is linearised online and the control policy is calculated from a quadratic programming problem. In consequence of linearisation of neural models, the necessity of online nonlinear optimisation is eliminated. Results obtained in the proposed structure are comparable with those achieved in a computationally demanding structure with nonlinear optimisation used for set-point optimisation and MPC.     

Guaranteed cost control for multi-inventory systems with uncertain demand

In this paper we consider the problem of controlling a multi-inventory system in the presence of uncertain demand. The demand is unknown but bounded in an assigned compact set. The control input is assumed to be also constrained in a compact set. We consider an integral cost function of the buffer levels and we face the problem of minimizing the worst-case cost. We show that the optimal cost of a suitable auxiliary problem with no uncertainties is always an upper bound for the original problem. In the special case of minimum-time control, this upper bound is tight, namely its optimal cost is equal to the worst-case cost for the original system. Furthermore, the result is constructive, since the optimal control law can be explicitly computed.     

Optimal inversion-based control for the set-point regulation ofnonminimum-phase uncertain scalar systems

We propose an optimal inversion-based control for the set-point constrained regulation of nonminimum-phase uncertain scalar systems. This approach is based on a new control architecture where the main purpose of the feedback is to reduce the sensitivity to parametric plant uncertainties permitting in such a way the effective use of a feedforward action determined via a stable dynamic inversion. Essential constituents of the architecture are a parameterized family of “transition” polynomials to shape ideal output transfers and a parameterized controller ensuring the internal model principle. The methodology is then centered on the optimal combined design of the feedback controller and of the inversion-based command signal in order to minimize the worst-case settling time subject to an amplitude constraint on the control variable and to arbitrarily assigned overshoot and undershoot bounds. Finally, an approximate or suboptimal solution to the resulting nonlinear optimization problem can be obtained by genetic algorithms. A worked example highlights the effectiveness of the overall methodology