An iterative approach to the optimal co-design of linear control systems

This paper investigates the optimal co-design of both physical plants and control policies for a class of continuous-time linear control systems. The optimal co-design of a specific linear control system is commonly formulated as a nonlinear non-convex optimisation problem (NNOP), and solved by using iterative techniques, where the plant parameters and the control policy are updated iteratively and alternately. This paper proposes a novel iterative approach to solve the NNOP, where the plant parameters are updated by solving a standard semi-definite programming problem, with non-convexity no longer involved. The proposed system design is generally less conservative in terms of the system performance compared to the conventional system-equivalence-based design, albeit the range of applicability is slightly reduced. A practical optimisation algorithm is proposed to compute a sub-optimal solution ensuring the system stability, and the convergence of the algorithm is established. The effectiveness of the proposed algorithm is illustrated by its application to the optimal co-design of a physical load positioning system.     

汽车电子节气门系统自适应固定时间预设性能控制

针对汽车电子节气门系统对暂稳态性能的较高要求以及系统固有的非线性和参数不确定性特性, 将预设性能控制策略与固定时间稳定性理论相结合, 应用自适应技术对系统参数的在线调节能力, 提出了一种自适应固定时间预设性能控制策略. 通过固定时间收敛理论与改进的预设性能函数设定及控制参数的实时调节, 保障了闭环控制系统对节气门开度任意参考信号的高性能位置跟踪及对运行过程中参数变化和负载扰动的鲁棒性. 控制器的有效性和优势通过MATLAB/Simulink仿真和基于dSPACE的硬件在环实验平台的实验得到验证.     

Flood control of the Demer by using Model Predictive Control

It is shown how Model Predictive Control can be used for flood control of river systems modelled with real data. A linear model for the Demer, a river in Belgium, is derived, which is used inside the optimisation problem solved by the controller. This optimisation problem is formulated such that the controller can be used for set-point and flood control. A Kalman filter is used as a state estimator. Closed loop simulations performed with a full hydrodynamic model of the Demer in combination with historical rainfall data show that the proposed control scheme outperforms the current control strategy.     

Intelligent robust control for uncertain nonlinear time-varying systems and its application to robotic systems.

This paper addresses the problem of designing adaptive fuzzy-based (or neural network-based) robust controls for a large class of uncertain nonlinear time-varying systems. This class of systems can be perturbed by plant uncertainties, unmodeled perturbations, and external disturbances. Nonlinear H(infinity) control technique incorporated with adaptive control technique and VSC technique is employed to construct the intelligent robust stabilization controller such that an H(infinity) control is achieved. The problem of the robust tracking control design for uncertain robotic systems is employed to demonstrate the effectiveness of the developed robust stabilization control scheme. Therefore, an intelligent robust tracking controller for uncertain robotic systems in the presence of high-degree uncertainties can easily be implemented. Its solution requires only to solve a linear algebraic matrix inequality and a satisfactorily transient and asymptotical tracking performance is guaranteed. A simulation example is made to confirm the performance of the developed control algorithms.     

Networked predictive control of constrained linear systems with input quantisation

This article investigates the problem of stabilising predictive control for constrained systems, wherein communication from the controller to the plant input is through a digital channel subject to quantisation and delay. A novel model with structured norm-bounded uncertainties is proposed to describe control system with input quantisation. Under a multirate scheme, a delay compensation strategy is presented. The networked predictive control synthesis approach is developed by solving a finite receding horizon optimisation problem with free control moves. It is shown that the proposed predictive controller not only efficiently reduces the negative effects of the quantisation and communication delays but also guarantees the closed-loop stability and satisfies constraints. Finally, a simulation example illustrates the effectiveness of the derived method.     

基于反演算法的严格反馈非线性系统固定时间跟踪控制

针对一类严格反馈非线性系统,研究固定时间跟踪控制问题.基于反演控制策略及Lyapunov稳定性理论,给出使系统全局固定时间稳定的充分条件和设计步骤.所提出的反演控制方案可以消除控制器存在的奇点问题,保证系统的跟踪误差在固定时间内收敛于原点的一个小邻域内,且收敛时间与系统的初始状态无关.最后,通过一个数值仿真示例验证了所提出设计方案的有效性.     

欺骗攻击下网络化系统事件触发安全控制

针对网络控制系统受欺骗攻击问题,提出了事件触发器与安全控制器协同设计策略。提出了离散事件触发机制,克服了连续事件触发机制需要新增专用硬件且需要复杂计算避免芝诺现象局限。建立了有机融合欺骗攻击、事件触发机制、网络诱导延时及外部扰动多约束参数的闭环系统时滞模型,推导出了欺骗攻击下事件触发控制系统渐近稳定的充分条件,得到了事件触发器与安全控制器的协同设计条件。仿真验证了方法的有效性。     

基于事件触发非线性多智能体系统的固定时间一致性

本文研究了在有向拓扑下,带有非线性动力学多智能体系统的固定时间一致性问题.提出了一种新的基于事件触发机制的非线性控制策略,对于每个智能体给出了基于状态信息的事件触发条件,当状态误差满足所给条件时才触发事件,能有效的减小系统的能量耗散和控制器的更新频次.利用Lyapunov稳定性理论和代数图论,证明在该控制策略下,多智能体系统在固定时间能实现领导跟随一致性,且不存在Zeno行为.相较于有限时间一致性策略,采用固定时间一致性策略系统的收敛时间不再依赖于系统的初始状态.最后,仿真实例验证了理论结果的有效性.     

Buck型变换器固定时间自适应控制

针对Buck型DC/DC变换器系统,提出一种固定时间自适应降压控制方法.首先,针对系统参数已知的情况,设计固定时间滑模面和控制器,保证系统输出电压误差在固定时间内收敛到平衡点,且其收敛时间上界与系统初始状态无关,仅由控制参数决定.针对电阻、电感等参数值受温度及外部干扰影响发生变化且无法准确获取的问题,进一步设计自适应更新律估计系统所有未知参数,并在此基础上设计固定时间自适应控制器,保证输出电压误差在固定时间内收敛到平衡点附近的邻域内.最后通过仿真和实验对比结果验证所提出控制方法的有效性.     

基于有限时间指令滤波的非线性系统固定时间预设性能控制

针对一类严格反馈非线性系统,提出一种基于有限时间指令滤波的自适应固定时间预设性能控制策略.首先,引用非线性映射技术及适当的误差变换,建立等效的误差模型;其次,综合利用反步法、固定时间控制和自适应控制等方法,设计一种基于有限时间指令滤波的预设性能跟踪控制器.该策略应用指令滤波器解决了反步法中对虚拟控制律反复求导问题,减轻了计算负担.此外,预设性能控制和固定时间控制保证了系统的跟踪误差能够在固定时间内收敛到预设性能函数限定的范围内,其收敛时间与系统初始条件无关,且确保系统中全部信号在有限时间均达到有界区域.理论分析与仿真验证均表明了所提出设计方法的有效性.     

基于变速趋近律的机电伺服系统自适应滑模控制

针对带有摩擦力矩、负载力矩以及扰动力矩等不确定性的机电伺服系统,提出一种基于变速趋近律的自适应滑模控制方法.首先,构造双曲正切型辅助函数并设计新的变速趋近律,用以调节滑模变量的收敛速度,使其在到达减速点之前具有较快的收敛速度,而在到达减速点以后则能有效削弱抖振.在此基础上,构造自适应滑模控制器,保证系统位置输出能够快速稳定地跟踪期望轨迹.同时,设计参数更新律估计不确定项平方的上界,确保控制信号的连续性和减小控制信号抖振.基于李雅普诺夫稳定性定理给出误差收敛性分析,并给出仿真对比验证所提出方法的有效性.     

基于事件触发二阶多智能体系统的固定时间比例一致性

研究了在无向拓扑下,由多个子群组成的二阶多智能体系统的固定时间比例一致性问题,采用反推法设计了一种基于事件触发的固定时间非线性比例一致控制策略,该策略包含分段式事件触发函数:当智能体在追踪虚拟速度时,给出了基于速度信息的触发条件;当智能体速度与虚拟速度达到一致时,切换至基于位置信息的触发条件,可有效减少系统能量耗散及控...     

Fixed-time backstepping distributed cooperative control for multiple unmanned aerial vehicles

This paper proposes a fixed-time backstepping distributed cooperative control scheme based on fixed-time extended state observer (FxTESO) for multiple unmanned aerial vehicles (UAVs). A fixed-time ESO, which is convergent independently of initial conditions, is designed to estimate and compensate the external disturbances in tracking process. Moreover, to eliminate the “explosion of complexity” in the traditional backstepping architecture, a nonlinear first-order filter is adopted to construct the distributed fixed-time control scheme. Based on the local information of neighboring UAVs, a fixed-time backstepping cooperative controller is designed. The proposed formation algorithm can be shown practicable for the UAV control system by using of Lyapunov stability theory and graph theory. Simulation results are given to demonstrate the effectiveness of the proposed control scheme.     

基于未知系统动态估计器的Buck型变换器快速固定时间控制

针对存在参数不确定、输入电压波动以及负载变化等未知动态的Buck型变换器系统,提出一种基于未知系统动态估计器的快速固定时间控制方法.首先,设计基于一阶低通滤波器的估计器,实现对系统未知动态的前馈补偿.在此基础上,基于输出电压误差和未知动态估计值设计固定时间滑模面和反馈控制器,保证输出电压快速收敛至参考电压附近邻域内,且...     

Hybrid Scheduling and Quantized Output Feedback Control for Networked Control Systems

A novel co-design scheme of hybrid scheduling strategy, adaptive logarithmic quantizer and dynamic robust H-infinity output feedback controller for a class of networked control system (NCS)with communication constraints and time delay is proposed. The hybrid scheduling scheme integrates dead zone scheduling and Try Once Discard (TOD) scheduling so as to get the stronger adaptability and flexibility than the single scheduling. In this scheme, dead zone scheduling which updates the threshold according to mode-dependent control strategy is used for single node of NCS to reduce the network bandwidth utilization while TOD scheduling is used for the whole node of NCS in order to meet the requirements of communication constraints and guarantee the overall system performance.We develop the integrated design for the hybrid scheduling strategy, adaptive quantizer and dynamic robust output feedback controller to maintain asymptotic stability of the closed-loop NCS by using the multiple-Lyapunov function and switched system theory. The proposed method can improve the the quality of service (QoS) meanwhile ensure the quality of control (QoC) of overall systems, which make a better trade-off between network utilization and control performance. An simulation example demonstrates the efficiency of the proposed method.     

An analysis and design method for a class of nonlinear systems with nested saturations

This paper is concerned with the analysis and design of a class of nonlinear systems subject to nested saturations. The proposed controller incorporates both an extended state observer (ESO), which is utilised to estimate the nonlinear dynamics of the plant, as well as a set of observer-based feedbacks. We first present analysis results for systems with nonlinear ESOs and show that local stabilisation can be achieved in a region including the origin. Then, in the case that the ESO is in linear form (LESO), the conditions for determining the estimate of the domain of attraction of the resulting closed-loop system are formulated as a convex optimisation problem. A linear matrix inequality based algorithm is then established to design the feedback gains and the LESO gain. An illustrative example is given to show the effectiveness of the proposed approach.     

Aggregation-based fuzzy dual-mode control for nonlinear systems with mixed constraints

A new receding horizon dual-mode control method is proposed for a class of discrete-time nonlinear systems represented by Takagi–Sugeno (T–S) fuzzy models subject to mixed constraints including hard input constraint and soft state constraint. On the one hand, our receding horizon scheme is based upon an online optimisation that utilises optimised sequence plus local linear feedback. On the other hand, due to the consideration of computation burden, an amplitude decaying aggregation strategy is introduced to reduce the number of optimisation variables. The proposed controller is obtained using semi-definite programming, which can be easily solved by means of linear matrix inequalities. A numerical example is given to verify the feasibility and efficiency of the proposed method.     

Neural network control of multivariable processes with a fast optimisation algorithm

A radial basis function (RBF) neural network model based predictive control scheme is developed for multivariable nonlinear systems in this paper. A fast convergence algorithm is proposed and employed in multidimensional optimisation in the control scheme to reduce the computing time and save required computer memory. The scheme is applied to a simulated two-input two-output nonlinear process for set-point tracking control. Simulation results demonstrate the effectiveness of the control strategy and the fast learning algorithm for multivariable non-linear processes. Comparison of the performance with PID control is included.     

Nonlinear feedback design for fixed-time tracking of a class of nonlinear systems

Fixed-time control is more preferable than finite-time control in practical applications since the settling time is independent of the system initial condition in a fixed-time control problem. Moreover, systems in real world usually suffer from unfavourable factors, such as unknown control coefficients and external disturbance. In this paper, we consider the fixed-time tracking control for a class of nonlinear systems by considering the aforementioned two points. A strategy for specifying the control input is established by the method of adding a power integrator. The designed control law guarantees that the reference signal can be followed in a fixed time. As an application, the bank-to-turn missile system is used to show the efficiency of the proposed fixed-time tracking scheme.     

Adaptive nonlinear control of pH neutralization processes using fuzzy approximators

In this paper, an adaptive control scheme, based on fuzzy logic systems, for pH control is addressed. For implementation of the proposed scheme no composition measurement is required. Stability of the closed-loop system is established and it is shown that the solution of the closed-loop system is uniformly ultimately bounded and under a certain condition, asymptotical stability is achieved. Effectiveness of the proposed controller is tested through simulation and experimental studies. Results indicate that the proposed controller has good performances in set-point tracking and load rejection and much better than that of a tuned PI controller.