一种非线性系统的自适应无模型预测控制方法

为了避免基于模型的控制方法在控制非线性系统时存在建模困难和模型失配的问题,提出一种非线性系统的自适应无模型预测控制方法。该方法首先将非线性系统转化为由一组伪偏导数描述的线性系统,然后利用一种改进的投影算法在线估计这组伪偏导数,得到被控系统的泛模型。根据得到的泛模型,推导出预测模型,在此基础上根据预测控制滚动的优化策略求解二次目标函数得出最优控制律。通过对CSTR过程进行仿真验证,结果表明该方法具有良好的跟踪性能和较强的鲁棒性。     

Nonlinear receding horizon guidance for spacecraft formation reconfiguration on libration point orbits using a symplectic numerical method

This paper studies a nonlinear receding horizon control guidance strategy for spacecraft formation reconfiguration on libration orbits in the Sun–Earth system. For comparison, a linear quadratic soft terminal control strategy is also considered for the same reconfiguration missions. A novel symplectic iterative numerical algorithm is proposed to obtain the optimal solution for the nonlinear receding horizon control strategy at each update instant. With the aid of the quasilinearization method, a high-efficiency structure-preserving symplectic method is introduced in the iterations, and the optimal control problem is replaced successfully by a series of sparse symmetrical linear equations. Several typical spacecraft formation reconfiguration missions including resizing, rotating and slewing reconfigurations and their combinations are numerically simulated to show the effectiveness of the nonlinear receding horizon guidance strategy based on the proposed symplectic algorithm. Through these simulations, the nonlinear receding horizon control strategy is shown to have obvious advantages in convergence and parameter sensitivity compared with a linear quadratic soft terminal control strategy. Monte Carlo stochastic simulations are used to test the robustness of the nonlinear receding horizon control guidance in dealing with measurement and execution errors.     

复合功率分流系统发动机起动模型预测控制

复合功率分流混合动力系统从纯电动模式至电子无级变速（Electronic-continuously variable transmission,E-CVT）混合动力模式的切换过程,伴随发动机的起动。由于发动机在整个起动过程始终与传动系相连,其低速往复脉动阻力矩特性对车辆模式切换过程的驾驶平顺性有直接影响,若控制不当,常引起较大的车辆纵向冲击。针对复合功率分流混合动力系统模式切换过程,基于Matlab/Simulink平台建立传动系动态模型和发动机阻力矩模型;提出一种发动机起动模型预测优化控制策略,在线计算电机转矩拖转发动机跟踪目标最优转速曲线并补偿输出端转矩波动。离线仿真及硬件在环台架试验结果表明,所开发的发动机模型预测转速跟踪控制策略能够快速起动发动机并使车辆平稳切换,将整车纵向冲击度限制在11.0 m/s³以内,且对整车参数摄动具有较好的鲁棒抑制效果。     

干式DCT双离合器联合起步最优协调控制

基于自主开发的6速干式双离合器自动变速器(Dual clutch transmission, DCT),提出基于极小值原理的双离合器联合起步过程发动机和双离合器转矩协调最优控制算法。在起步滑摩阶段,以滑摩功和冲击度为性能指标,在反映驾驶员意图的终端约束下,采用极小值原理和改进的发动机恒转速控制,研究并确定起步过程中双离合器,发动机转矩及其转速。根据起步控制目标,确定分离离合器分离条件和离合器的转矩分配关系。在需求转矩切换阶段,将发动机输出转矩平滑切换到驾驶员需求转矩。在Matlab/Simulink软件平台上,搭建DCT车辆双离合器联合起步控制仿真模型,对不同驾驶意图和起步档位下DCT车辆的起步过程进行仿真。结果表明,所提出的基于极小值原理的发动机和双离合器转矩协调控制策略,有效地保证双离合器联合起步品质,反映起步意图,延长离合器使用寿命。将所得的离合器最优转矩控制律转化为离合器位置控制律,在离合器伺服控制试验台架上进行离合器位置闭环控制,得到较好的跟踪效果。     

Event-triggered distributed receding horizon control for tracking and formation of homogeneous multi-agent systems

This paper proposes an event-triggered distributed receding horizon control (DRHC) approach for the formation and tracking problems of homogeneous multi-agent systems. For each agent, an event-triggering condition, based on assumed predictive information of the neighbours, is derived from stability analysis. Considering the uncertain deviation between the assumed and true predictive information, we design a time-varying compatibility constraint for the individual optimization problem. In the event-triggered DRHC algorithm, each agent solves the optimization problem and communicates with its neighbours only when the event-triggering condition is satisfied, so the communication and computation burden are reduced. Moreover, guarantees for the recursive feasibility and asymptotic stability of the overall system are proved. A simulation example is provided to illustrate effectiveness of the proposed approach.     

基于非线性模型预测的可变截面涡轮增压器控制

针对可变截面涡轮增压器的开度与增压柴油机参数呈非线性关系的问题,提出一种基于反向传播神经网络和量子粒子群算法的非线性模型预测控制算法,通过调节涡轮增压器的开度,控制过量空气系数,从而实现柴油机的进气量与燃油消耗量的快速匹配,使转矩快速达到期望值.仿真分析表明:该方法相比于PID控制,可使增压柴油机更加平稳地完成转矩阶跃...     

Auto-tuning of PID controller parameters with supervised receding horizon optimization

In this paper, a novel two-layer online auto-tuning algorithm is presented for a nonlinear time-varying system. The lower layer consists of a conventional proportional-integral-derivative (PID) controller and a plant process, while the upper layer is composed of identification and tuning modules. The purpose of the upper layer is to find a set of optimal PID parameters for the lower layer via an online receding horizon optimization approach, which result in a time-varying PID controller. Through mathematical analysis, the proposed system performance is equivalent to that of a standard generalized predictive control. Simulation and experiment demonstrate that the new method has a better control system performance compared with conventional PID controllers.     

Receding horizon tracking control for wheeled mobile robots with time-delay

In this paper, a receding horizon (RH) controller is developed for tracking control of wheeled mobile robots (WMRs) subject to nonholonomic constraint in the environments without obstacles. The problem is simplified by neglecting the vehicle dynamics and considering only the steering system. First, the tracking-error kinematic model is linearized at the equilibrium point. And then, it is transferred to an exact discrete form considering the time-delay. The control policy is derived from the optimization of a quadratic cost function, which penalizes the tracking error and control variables in each sampling time. The minimizing problem is solved by using the QP (quadratic programming) method taking the current error state as the initial value and including the velocity constraints. The performance of the control algorithm is verified via the computer simulations with several different predefined trajectories showing that the strategy is feasible. This paper was recommended for publication in revised form by Associate Editor Doo Yong Lee Kil To Chong (M’96) received the Ph.D. degree in mechanical engineering from Texas A&M University, College Station, in 1995. Currently, he is a Professor at the School of Electronics and Information Engineering, Chonbuk National University, Jeonju, Korea, and Head of the Mechatronics Research Center granted from the Korea Science Foundation. His research interests are in the areas of motor fault detection, network system control, time-delay systems, and neural networks. Chang Goo Lee was born in Chonju, South Korea on Dec., 1958. He received the B.S. and M.S., and Dr.Eng. degrees in Electrical Engineering from Chonbuk National University, South Korea, 1981, 1983 and 1990 respectively. He had been with ETRI as a senior researcher from 1983 to 1991. Since 1992, He has been with the School of Electronic and Information Engineering, Chonbuk National University where he is presently a Professor. His research interests include intelligent control, nonlinear control, and home network control. Yu Gao received the master’s degree in Electronics and Information from Chonbuk National University, Korea, in 2008. He got his bachelor’s degree in Physics from Soochow University, China, in 2005. Currently, he is a Ph.D. candidate in the School of Electronics and Information, Chonbuk National University, Korea. His research interests are in the area of the receding horizon control.     

Nonlinear robust dual-loop control for electro-hydraulic load simulator

This paper investigates on the high performance torque control of electro-hydraulic load simulator (EHLS). In order to suppress actuator׳s motion disturbance, a nonlinear robust dual-loop control scheme is developed, which consists of a open-loop nonlinear velocity feed-forward compensator and a closed-loop nonlinear deterministic robust torque controller. The main function of the open-loop compensator is to decouple actuator׳s active motion disturbance, whereas the torque loop controller aims at guaranteeing the dynamics performance of tracking torque reference. Besides actuator׳s motion disturbance, both the nonlinearity characteristics and friction problem of the EHLS system are taken into consideration in this paper. The effectiveness of the developed method are verified through comparative co-simulations and experiments.     

One-step receding horizon H(∞) control for networked control systems with random delay and packet disordering

The receding horizon H(∞) control (RHHC) problem is investigated in this paper for a class of networked control systems (NCSs) with random delay and packet disordering. A new model is proposed to describe the NCS with random delay which may be larger than one sampling period. The random delay is modeled as a Markov chain while the closed-loop system is described as a Markovian jump system. Sufficient conditions for the closed-loop NCS to be stochastically stable and the performance index to be upper bounded are derived by using the receding optimization principle. Furthermore, by solving a semi-definite programming (SDP) with linear matrix inequalities (LMIs) constraint, a piecewise-constant receding horizon H(∞) controller is obtained, and the designed piecewise-constant controller ensures that the closed-loop NCS achieves a prescribed H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed method.     

双离合器变速箱换档动力学和控制

依据包含一双离分器变速箱的汽车动力传动系统的详细动力模型开发了一个集成的动力传动系的换档控制.基于换高档和换低档以及发生于变速箱相同一部分的换档控制的仿真结果证实了该控制器的工作.采用带有再现单向离合离工作目标为均匀传递发动机转矩离合器滑动的闭环控制制订了控制法则,进一步的要素是通过发动机控制和离合器压力操纵的组合对发动机速度的闭环控制.此外,它证实了变速箱换档时输出转矩的控制可增加控制的持久性以及提供了直接操纵换档品质的方法.最后讨论了通过方便的液力传动锥型同步器在总换档质量方面予选换档的动力影响.     

Constrained incremental predictive controller design for a flexible joint robot

In this paper, an improved predictive control algorithm for controlling a typical nonlinear flexible-joint robot (FJR) with input constraint is proposed. The receding horizon algorithm, called generalized incremental predictive control (GIPC), utilizes both present and previous states rather than present states only. The GIPC algorithm includes the weighted difference of the current and the previous states and the summation of the control action increments. In order to illustrate the effectiveness of the proposed control strategy, it is implemented to the FJR and the results are compared with those of generalized predictive control (GPC). It is demonstrated that the proposed GIPC algorithm is more robust than the standard GPC method. Furthermore, the constrained GIPC algorithm using the quadratic programming removes instabilities caused by actuator saturation.     

干式DCT变速HEV换档过程鲁棒优化控制

针对双离合器自动变速器(Dual clutch transmission,DCT)变速弱混合动力轿车,考虑到起动发电一体化电机(Integrated starter and generator,ISG)转矩响应特性较快、转速/转矩控制精度高等特点,对其介入到换档过程时不同动力源输出转矩和离合器传递转矩协调鲁棒控制问题进行研究。建立体现DCT换档切换过程阶段差异性的动力学模型;考虑转矩相的离合器执行机构的响应能力和惯性相的模型不确定性和外界干扰(转速量测噪声和发动机转矩响应滞后),优化决策了动力源合成转矩;在需求转矩切换阶段,切换发动机转矩至驾驶员需求水平并退出ISG电机;基于系统效率最优对动力源合成转矩进行分配。基于Simulink的仿真试验表明所提出的换档控制策略能有效协调控制动力源转矩,并对模型不确定性和干扰有较强的抑制能力。为进一步验证策略进行的动态台架试验表明,所设计的转矩协调控制策略有效地解决了DCT换档过程中ISG电机、发动机以及双离合器之间的实时转矩协调控制问题,使其具有较好的换档品质。     

Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms

In this paper, a fuzzy model predictive control (FMPC) approach is introduced to design a control system for nonlinear processes. The proposed control strategy has been successfully employed for representative, benchmark chemical processes. Each nonlinear process system is described by fuzzy convolution models, which comprise a number of quasi-linear fuzzy implications (FIs). Each FI is employed to describe a fuzzy-set based relation between control input and model output. A quadratic optimization problem is then formulated, which minimizes the difference between the model predictions and the desired trajectory over a predefined predictive horizon and the requirement of control energy over a shorter control horizon. The present work proposes to solve this optimization problem by employing a contemporary population-based evolutionary optimization strategy, called the Bacterial Foraging Optimization (BFO) algorithm. The solution of this optimization problem is utilized to determine optimal controller parameters. The utility of the proposed controller is demonstrated by applying it to two non-linear chemical processes, where this controller could achieve better performances than those achieved by similar competing controller, under various operating conditions and design considerations. Further comparisons between various stochastic optimization algorithms have been reported and the efficacy of the proposed approach over similar optimization based algorithms has been concluded employing suitable performance indices.     

过失速大迎角条件下推力矢量飞机机动仿真

推力矢量飞机是我国下一代飞机发展的必然趋势。在过失速大迎角飞行条件下,常规舵面失去了操纵能力,需要开发新型控制律算法来研究推力矢量和推力对飞行性能的影响。本文推导和综合了推力矢量飞机运动方程,推力矢量喷管模型和传感器、作动器模型,并且提出采用后退区间优化控制算法作为研究飞机在过失速大迎角飞行条件下的鲁棒跟踪控制律。仿真验证了在飞行扩展包线中,只有推力矢量和推力配合使用才能保证飞机准确跟随驾驶指令飞行,对下一代飞机设计打下一定的基础。     

无人驾驶越野车辆纵向速度跟踪控制试验

以无人驾驶轻型战术轮式越野车辆为平台,开展模型预测纵向速度跟踪控制实车试验研究。针对平台控制特性设计合适的下位控制器,使用Matlab/Simulink与包含气压制动系统的TruckSim车辆联合仿真初步测试系统可行性,并在沥青路和土路分别进行实车试验。试验结果表明：模型预测速度跟踪控制系统能够克服气压制动延时长、整车质量重、越野路况行驶阻力波动大等模型误差和不确定干扰,自适应调节期望加速度大小,实现不同行驶工况高精度速度跟踪。试验过程驱动/制动切换平稳、无振荡,且能够像熟练驾驶员一样充分利用发动机辅助制动,必要时既不施加电控制动,也不请求发动机输出转矩。系统使用现代车辆易于获得的车辆状态参数,便于向其他车辆移植,可作为无人车辆车体控制得力技术加以推广。     

Cascade control of superheated steam temperature with neuro-PID controller

In this paper, an improved cascade control methodology for superheated processes is developed, in which the primary PID controller is implemented by neural networks trained by minimizing error entropy criterion. The entropy of the tracking error can be estimated recursively by utilizing receding horizon window technique. The measurable disturbances in superheated processes are input to the neuro-PID controller besides the sequences of tracking error in outer loop control system, hence, feedback control is combined with feedforward control in the proposed neuro-PID controller. The convergent condition of the neural networks is analyzed. The implementation procedures of the proposed cascade control approach are summarized. Compared with the neuro-PID controller using minimizing squared error criterion, the proposed neuro-PID controller using minimizing error entropy criterion may decrease fluctuations of the superheated steam temperature. A simulation example shows the advantages of the proposed method.     

自动变速器升挡过程惯性相发动机协调控制

发动机协调控制是提高换挡品质的必要技术手段。分析换挡过程输出轴转矩波动与换挡品质的关系,基于液力变矩器模型设计出输出轴转矩观测器,制定出基于转矩的升挡过程惯性相发动机协调控制策略,在DEUTZ BF4M1013单体泵柴油机和Allison S2000离合器—离合器换挡式液力自动变速器组成的动力传动系统台架上,进行基于转矩的1-2升挡过程惯性相发动机协调控制试验研究。试验结果表明,单体泵柴油机的转矩响应能够满足换挡过程协调控制的要求;基于转矩的换挡过程发动机协调控制能够有效地减小输出轴转矩波动,提高换挡品质。     

小型航空汽油发动机模糊增益恒转速控制

汽油发动机具有非线性、时变时滞等特点,传统的增益可调PID方法很难保证发动机整个工作范围内转速控制的稳定性及性能要求.笔者采用模糊PID控制,来实现小型汽油发动机的恒转速控制.在不同转速/负载点,采用时域阶跃响应方法,建立了一组线性模型来表示发动机的非线性特性.基于建立的发动机模型,给出了模糊规则和推理来在线调整PID...     

Modeling and output tracking of transverse flux permanent magnet machines using high gain observer and RBF neural network

This paper deals with modeling and adaptive output tracking of a transverse flux permanent magnet machine as a nonlinear system with unknown nonlinearities by utilizing high gain observer and radial basis function networks. The proposed model is developed based on computing the permeance between rotor and stator using quasiflux tubes. Based on this model, the techniques of feedback linearization and H_∞ control are used to design an adaptive control law for compensating the unknown nonlinear parts, such as the effect of cogging torque, as a disturbance is decreased onto the rotor angle and angular velocity tracking performances. Finally, the capability of the proposed method in tracking both the angle and the angular velocity is shown in the simulation results.