基于扩张终端约束集的非线性双模预测控制

Aiming at a class of nonlinear systems with multiple equilibrium points, we present a dual-mode model predictive control algorithm with extended terminal constraint set combined with control invariant set and gain schedule. Local LQR control laws and the corresponding maximum control invariant sets can be designed for finite equilibrium points. It is guaranteed that control invariant sets are overlapped each other. The union of the control invariant sets is treated as the terminal constraint set of predictive control. The feasibility and stability of the novel dual-mode model predictive control are investigated with both variable and fixed horizon. Because of the introduction of extended terminal constrained set, the feasibility of optimization can be guaranteed with short prediction horizon. In this way, the size of the optimization problem is reduced so it is computationally efficient. Finally, a simulation example illustrating the algorithm is presented.     

Continuous output-feedback stabilization for a class of stochastic high-order nonlinear systems

This paper is concerned with the global stabilization via output-feedback for a class of high-order stochastic nonlinear systems with unmeasurable states dependent growth and uncertain control coefficients. Indeed, there have been abundant deterministic results which recently inspired the intense investigation for their stochastic analogous. However, because of the possibility of non-unique solutions to the systems, there lack basic concepts and theorems for the problem under investigation. First of all, two stochastic stability concepts are generalized to allow the stochastic systems with more than one solution, and a key theorem is given to provide the sufficient conditions for the stochastic stabilities in a weaker sense. Then, by introducing the suitable reduced order observer and appropriate control Lyapunov functions, and by using the method of adding a power integrator, a continuous (nonsmooth) output-feedback controller is successfully designed, which guarantees that the closed-loop system is globally asymptotically stable in probability.     

Leader-following consensus for uncertain second-order nonlinear multi-agent systems

This paper studies the leader-following consensus problem for a class of second-order nonlinear multi-agent systems subject to linearly parameterized uncertainty and disturbance. The problem is solved by integrating the adaptive control technique and the adaptive distributed observer method. The design procedure is illustrated by an example with a group of Van der Pol oscillators as the followers and a harmonic system as the leader.     

时变机器人系统的重复学习控制: 一种混合学习方案

Repetitive learning control is presented for finite-time-trajectory tracking of uncertain time-varying robotic systems. A hybrid learning scheme is given to cope with the constant and time-varying unknowns in system dynamics, where the time functions are learned in an iterative learning way, without the aid of Taylor expression, while the conventional differential learning method is suggested for estimating the constant ones. It is distinct that the presented repetitive learning control avoids the requirement for initial repositioning at the beginning of each cycle, and the time-varying unknowns are not necessary to be periodic. It is shown that with the adoption of hybrid learning, the boundedness of state variables of the closed-loop system is guaranteed and the tracking error is ensured to converge to zero as iteration increases. The effectiveness of the proposed scheme is demonstrated through numerical simulation.     

Stochastic optimal control of networked control systems with control packet dropouts

This paper considers the stochastic optimal control problem for networked control systems(NCSs)with control packet dropouts.The proportional plus up to the third-order derivative(PD3)compensation strategy is adopted to compensate for control packet dropouts at the actuator by using the past control packets stored in the buffer.Based on the strategy,a new NCS structure model with packet dropouts is provided,where the packet dropout is assumed to obey the Bernoulli random binary distribution.In terms of the given model,the stochastic optimal control law is proposed. Numerical examples illustrate the effectiveness of the results.     

Sliding Mode Control in Power Converters and Drives: A Review

Sliding mode control(SMC)has been studied since the 1950s and widely used in practical applications due to its insensitivity to matched disturbances.The aim of this paper is to present a review of SMC describing the key developments and examining the new trends and challenges for its application to power electronic systems.The fundamental theory of SMC is briefly reviewed and the key technical problems associated with the implementation of SMC to power converters and drives,such chattering phenomenon and variable switching frequency,are discussed and analyzed.The recent developments in SMC systems,future challenges and perspectives of SMC for power converters are discussed.     

Power Adjusting Algorithm: A New Cross-Layer Power Saving Mechanism for Mobile Ad-Hoc Networks

Power saving is one of the key issues in Mobile Ad-Hoc Networks(MANETs).It can be realized in Medium Access Control(MAC) layer and network layer.However,previous attentions were mainly paid to MAC layer or network layer with the aim of improving the channel utilization by adopting variable-range transmission power control.In this paper we focus on the power saving in both MAC layer and network layer,and propose a Power Adjusting Algorithm(PAA).In the presence of host’s mobility,PAA is designed to conserve energy by adjusting the transmission power to maintain the route’s connectivity and restarting the route discovery periodically to find a new route with better energy efficiency dynamically.After analyzing the operations of PAA,we find that the length of route discovery restarting period is a critical argument which will affect power saving,and an energy consumption model is abstracted to find the optimal value of the restarting period by analyzing the energy consumption of this algorithm.PAA can handle the mobility of MANET by adjusting the transmission power and in the meantime save energy by restarting route discovery periodically to balance the energy consumption on route discovery and packet delivering.Simulation results show that,PAA saves nearly 40% energy compared with Dynamic Source Routing protocol when the maximum speed of mobile hosts is larger than 8m/s.     

Network selection policy based on efective capacity in heterogeneous wireless communication systems

A great deal of previous research was based on the concept of Shannon theory without considering the delay characteristic.However,in reality,diferent services have diferent delay constraints.In this paper,two new network selection policies are proposed for heterogeneous wireless communication systems using efective capacity,which incorporate delay in the transmission rate.Users can access the proper network after considering the delay demands of diferent services.Our proposed policies aim to maximize the entire throughput with diferent delay constraints.Both the mathematical analysis and simulations show that the proposed network selection policies can improve network throughput while providing quality-of-service guarantees.     

Decentralized robust control of multiple static var compensators via Hamiltonian function method

Using the energy-based Hamiltonian function method, this paper investigates the decentralized robust nonlinear control of multiple static var compensators (SVCs) in multimachine multiload power systems. First, the uncertain nonlinear differential algebraic equation model is constructed for the power system. Then, the dissipative Hamiltonian realization of the system is completed by means of variable transformation and prefeedback control. Finally, based on the obtained dissipative Hamiltonian realization, a decentralized robust nonlinear controller is put forward. The proposed controller can effectively utilize the internal structure and the energy balance property of the power system. Simulation results verify the effectiveness of the control scheme.     

Information theoretic conditions for tracking in leader-follower systems with communication constraints

In this paper, we introduce a general framework for tracking in leader-follower systems under communication constraints, in which the leader and follower systems as well as the corresponding controllers are spatially distributed and connected over communication links. We provide necessary conditions on the channel data rate of each communication link for tracking of the leader-follower systems. By considering the forward and feedback channels as one cascade channel, we also provide a lower bound for the data rate of the cascade channel for the system to track a reference signal such that the tracking error has finite second moment. Examples and simulations are provided to demonstrate some of the results.     

Robust nonlinear control of shunt active filters for harmonic current compensation

An advanced control strategy of shunt active filters (SAF) aiming to compensate for harmonic current in the electric supply grid is proposed. The SAF considered here is suitable for three-phase three-wire current harmonic compensation and is based on AC/DC three-phase boost converter topology. Robust control of the active-reactive current/power delivered by the SAF is designed exploiting the internal model principle. The stabilization of the DC-link voltage dynamics is addressed along with the fulfillment of the harmonics compensation objective. The two-time scale behavior of the SAF is exploited to apply the averaging theory in the control design. Experiments are provided to show the effectiveness of the proposed solution.     

并联型有源电力滤波器的无源性控制

将无源性控制应用于并联型有源电力滤波器,对有源电力滤波器电路进行简化等效．把负载电压和电源电流作为状态变量,把补偿电流作为控制变量,通过坐标变换建立有源电力滤波器的数学模型,通过无源性理论获得简单而容易实现的控制律．仿真结果表明系统具有良好的稳定性和鲁棒性。     

Experimental modeling and control design of shunt active power filters

One of the main issues when designing a control strategy for a power electronic system is the development of a reliable model of the real system. However, the evaluation of the actual plant parameters is difficult due to the mismatch between nameplate and actual values of components, and the presence of unmodeled dynamics and non-linearities. This paper presents a novel technique for both model parameters identification and optimized control design of a shunt active power filter system using genetic algorithms (GAs). Experimental results demonstrate that the proposed modeling and control design approach greatly improve the system performance.     

Cascade nonlinear control of shunt active power filters with average performance analysis

The problem of controlling single-phase shunt active power filters is addressed in presence of nonlinear loads. The control objective is twofold: (i) compensation of harmonic and reactive currents absorbed by the nonlinear load, this objective is referred to as power factor correction (PFC); (ii) regulation of the inverter output capacitor voltage. A two-loop cascade control strategy is developed that includes an inner-loop designed, using the Lyapunov design approach, to cope with the compensation issue and an outer-loop designed to regulate the capacitor voltage. The controller performances are formally analysed using system averaging theory. The theoretical results are illustrated by simulation.     

变频电网三相有源电力滤波器重复控制策略

为了在变频环境下,提高三相并联型有源电力滤波器的电流跟踪性能和补偿效果,提出了一种基于并联通用内模,具有频率自适应性的复合重复控制策略.提出的并联通用内模能依据指令电流谐波成份的变化动态调节内模的补偿范围和延迟时间,并进一步将延迟时间缩短至同等补偿范围内模的一半,显著提高了重复控制器动态性能和适应能力.内模中由电网频率变化引起的分数阶延时环节,采用线性插值法近似,使基于并联通用内模的重复控制器具有频率自适应性.采用插入式结构设计了复合重复控制系统.详细分析了复合控制系统的稳定条件和收敛性.以飞机变频电网并联型有源电力滤波器系统为应用环境,将提出的复合重复控制策略与其他控制策略进行了对比.仿真和实验结果验证了提出的复合重复控制策略的有效性和优越性.     

无源性控制在有源电力滤波器中的应用

线路或负载参数未知可能会导致有源滤波器系统失稳. 为解决系统的稳定性问题, 将无源性控制应用于有源电力滤波器来保证系统的稳定性. 将有源滤波器和非线性负载视为一个整体. 对负载进行诺顿等效, 把负载电压和电源电流作为状态变量, 把补偿电流作为控制变量, 建立系统在dq轴上的模型. 获得系统的控制律也就获得了补偿电流的给定值. 验证系统满足无源性条件之后, 再利用无源性控制方法获得系统的控制律. 虽然算法在理论上比较复杂, 但是实现上相当简单. 在仿真中, 对比了容性负载, 感性负载以及变动负载情况下的控制     

无源性控制在有源电力滤波器中的应用

线路或负载参数未知可能会导致有源滤波器系统失稳，为解决系统的稳定性问题，将无源性控制应用于有源电力滤波器来保证系统的稳定性。将有源滤波器和非线性负载视为一个整体，对负载进行诺顿等效，把负载电压和电源电流作为状态变量，把补偿电流作为控制变量，建立系统在dp轴上的模型。获得系统的控制律也就获得了补偿电流的给定值，验证系统满足无源性条件之后，再利用无源性控制方法获得系统的控制律。虽然算法在理论上比较复杂，但是实现上相当简单。在仿真中，对比了容性负载，感性负载以及变动负载情况下的控制效果，仿真结果表明算法具有很好的鲁棒性，并能很好的保证系统的稳定性。     

Current control for a shunt hybrid active power filter using recursive integral PI

This paper presents a current control method for a shunt hybrid active power filter （HAPF） using recursive integral PI algorithm. The method improves the performance of the HAPF system by reducing the influence of detection accuracy, time delay of instruction current calculation and phase displacement of output filter. Fuzzy logic based set-point weighing algorithm is combined in the control scheme to enhance its robustness and anti-interference ability. The proposed algorithm is easy to implement for engineering applications and easy to compute. Experiment results have verified the validity of the proposed controller. Furthermore, the proposed recursive integral PI algorithm can also be applied in the control of periodic current as in AC drivers.     

非线性主动悬架系统自适应最优控制

针对非线性主动悬架系统多性能指标综合优化问题,提出一类自适应最优控制方法.首先,通过引入一阶低通滤波操作,利用系统输入输出构建结构简单且调节参数少的一类未知非线性动态估计器,在线估计系统未知非线性动态;其次,构建包含乘驾舒适度、悬架行程空间及输入能耗的性能指标函数,采用单层神经网络对最优性能指标函数进行在线逼近,并得到新的哈密尔顿函数;为实现在线求解,构建一类新的基于参数估计误差信息的自适应律,在线更新神经网络权值并计算最优控制律;最后,理论分析闭环系统稳定性和收敛性,并通过专业软件Carsim与Matlab/Simulink搭建的联合仿真平台给出的对比仿真结果,验证所提出方法可有效解决主动悬架系统多目标性能优化控制问题,提升主动悬架系统综合性能.     

一种新型有源电力滤波器电流控制策略研究

针对现有有源电力滤波器结构较为复杂、谐波电流跟踪困难等问题,提出了一种新型有源电力滤波器电流控制策略。该控制策略不需要进行负载电流测量与谐波检测,而是直接在电网侧提取电网电流,并将比例积分与矢量比例积分控制器结合使用以对电网电流进行控制,从而实现谐波补偿。仿真结果证明了该策略的正确性与有效性。