Predictive Functional Control Based on Fuzzy Model: Comparison with Linear Predictive Functional Control and PID Control

The implementation of the fuzzy predictive functional control (FPFC) on the magnetic suspension system is presented in the paper. The magnetic suspension system was in our case the pilot plant for magnetic bearing and is an open-loop unstable process, therefore a lead compensator was used to stabilize it. The high quality control requirements were a-periodical step response and zero steady-state error. Adding the integrator to a feedback causes overshoot. The solution to the problem was cascade control with fuzzy predictive functional controller in the outer loop. To cope with the unknown model parameters and the nonlinear nature of the magnetic system, a fuzzy identification based on FNARX model was used. After successful validation the obtained fuzzy model was used for controller design. The FPFC is compared with a cascade linear predictive functional control (PFC) and PID control. The results we obtained with the FPFC are very promising and hardly comparable with conventional control techniques.     

Control of the common rail pressure in gasoline engines through an extended state observer based MPC

In this paper, a model predictive control (MPC) solution, assisted by extended state observer (ESO), is proposed for the common rail pressure control in gasoline engines. The rail pressure dynamic, nonlinear with large uncertainty, is modeled as a simple first order system. The discrepancy of the model from the real plant is lumped as ``total disturbance'', to be estimated in real-time by ESO and then mitigated in the nonlinear MPC, assuming the total disturbance does not change in the prediction horizon. The nonlinear MPC problem is solved using the Newton/generalized minimum residual (GMRES) algorithm. The proposed ESO-MPC solution, is compared with the conventional proportional-integral-differential (PID) controller, based on the high-fidelity model provided in the benchmark problem in IFAC-E-CoSM. Results show the following benefits from using ESO-MPC relative to PID (benchmark): 1) the disturbance rejection capability to fuel inject pulse step is improved by 12% in terms of recovery time; 2) the transient response of rail pressure is improved by 5% in terms of the integrated absolute tracking error; and 3) the robustness is improved without need for gain scheduling, which is required in PID. Additionally, increasing the bandwidth of ESO allows reducing the complexity of the model implemented in MPC, while maintaining the disturbance rejection performance at the cost of high noise-sensitivity. Therefore, the ESO-MPC combination offers a simpler and more practical solution for common rail pressure control, relative to the standard MPC, which is consistent with the findings in simulation.     

预测函数控制的内模控制理论

内模控制是一种基于过程数学模型进行控制器设计的新型控制策略,是研究预测控制重要的理论基础。预测函数控制是一种控制量计算方程简单,实时控制效果好的新型预测控制算法。本文用内模控制理论研究预测函数控制,分析了系统的鲁棒性和稳定性,最后进行了参数设计和仿真研究。     

基于精确线性化的MIMO双线性系统预测函数控制

针对典型多输入多输出双线性系统, 提出了基于非线性过程精确反馈解耦线性化的预测函数控制方法这是一种分层的控制策略, 首先设计一个静态的非线性状态反馈, 使得闭环系统是输入输出解耦和线性的;然后设计一组单输入单输出预测函数控制器, 下层为上层预测函数控制提供一组单输入单输出模型, 而上层预测函数控制以其固有的鲁棒性来补偿参数变化和解耦线性化的近似性, 并以纸机加压网前箱为例进行了仿真实验, 结果是令人满意的.     

神经网络解耦预测函数控制

结合解耦思想研究多变量系统预测函数控制。通过引入神经网络补偿环节，对多变量系统解耦，在此基础上，对解耦后各子系统进行单变量预测函数控制，以确定各个控制量。同时基于系统脉冲传递函数，得到单变量PFC控制器的显式表达式。仿真表明，该算法有较好的跟踪特性，对解决多变量系统的优化和控制具有一定的适用性。     

基于反馈线性化的预测函数控制及其在CSTR中的应用

针对典型化工非线性对象连续搅拌槽反应器(CSTR),研究了基于反馈线性化的预测函数控制方法.首先将非线性过程精确反馈线性化,然后在此基础上,设计相应的预测函数控制器,仿真结果是令人满意的.     

一种基于滑模的自适应预测函数控制新算法

提出了一种应用带时变遗忘因子的基于滑模的自适应预测函数控制新算法。该算法采用带时变遗忘因子的递推最小二乘算法在线辨识模型参数,将滑模控制与预测函数控制(PFC)相结合对系统进行控制。与其他模型预测控制不同,预测函数控制可以克服其他模型预测控制可能出现规律不明的控制输入问题,具有良好的跟踪能力和较强的鲁棒性,离散滑模控制中的滑动模态对干扰具有不变性;最后分析了控制系统的闭环渐近稳定性。仿真结果验证了该方法的有效性。     

涡轮增压汽油机气路预测模型的建立与预测控制

针对涡轮增压汽油机气路系统中节气门与废气旁通阀动力学耦合、机理建模复杂的问题, 本文提出基于神经网 络模型的气路系统预测控制方法,实现了节气门与废气旁通阀的协调控制. 首先, 针对涡轮增压汽油机气路系统map与 机理混合描述的特性, 利用系统的输入输出数据,采用反向传播神经网络(back propagation neural network, BPNN)训 练得到一个非线性气路模型; 其次, 基于泰勒展开式对预测模型进行线性化, 并对模型的精度进行了验证,进而利用该 模型预测系统的未来动态; 然后, 在考虑系统存在输入约束的条件下, 设计了一个线性模型预测控制器对节气门与废气 旁通阀进行协调控制, 实现了进气歧管压力和升压的跟踪控制进而满足发动机的扭矩需求; 最后, 通过离线仿真和基 于dSPACE的快速原型实验(rapid control prototyping, RCP)验证了控制系统的有效性和实时性.     

灰色预测模糊自适应PID控制的城轨列车智能驾驶系统研究

为提高城轨列车驾驶系统的智能化程度，将灰色预测控制与模糊自适应PID控制技术相结合，应用到城轨列车驾驶系统中。在对城轨列车驾驶系统进行需求分析的基础上，针对其对各种性能指标的要求，提出了基于灰色预测模糊自适应PID控制的城轨列车智能驾驶系统的设计方案。将灰色预测控制与模糊自适应控制应用到经典PID控制中，设计出了系统的智能控制器，有效满足了城轨列车在运行时对跟踪误差、准点率、停车误差、舒适度和节能性等多个性能指标的要求。仿真结果表明：采用灰色预测模糊自适应PID算法的控制系统具有良好的控制精度且系统的智能化程度得到了相应的提升。     

缸内直喷汽油机的自抗扰轨压跟踪控制器设计

获得期望的共轨压力是保证缸内直喷发动机(GDI)稳定工作和喷油量精确控制的一个重要前提. 本文针对缸内直喷汽油发动机轨压控制问题, 首先通过动力学分析建立了共轨燃油喷射系统的数学模型; 由于系统中存在有较强的非线性和不确定性, 采用基于模型但对模型的精确形式依赖较小的自抗扰控制技术设计轨压跟踪控制器,其中线性扩张状态观测器(ESO) 对系统存在的总扰动和不确定性进行了估计, 非线性误差反馈控制(NLSEF) 则采用反馈补偿实现扰动的抑制. 最后, 通过给定不同的参考轨压对控制器的有效性进行验证, 仿真结果表明控制性能是满意的.     

基于远程预报辨识的非线性广义预测控制算法

推导了基于远程预报辨识的非线性广义预测控制算法.该算法在辨识基于Hammer-stein 模型的非线性过程时,采用了预测控制算法中远程预报的思想,并在广义预测控制器的设 计上采用了新的优化指标,这就使辨识机制与控制机制有机地结合起来,对系统的阶次不确定 有很好的鲁棒性.     

Energy Efficient Predictive Control for Vapor Compression Refrigeration Cycle Systems

Vapor compression refrigeration cycle (VCC) system is a high dimensional coupling thermodynamic system for which the controller design is a great challenge. In this paper, a model predictive control based energy efficient control strategy which aims at maximizing the system efficiency is proposed. Firstly, according to the mass and energy conservation law, an analysis on the nonlinear relationship between superheat and cooling load is carried out, which can produce the maximal effect on the system performance. Then a model predictive control (MPC) based controller is developed for tracking the calculated setting curve of superheat degree and pressure difference based on model identified from data which can be obtained from an experimental rig. The proposed control strategy maximizes the coefficient of performance (COP) which depends on operating conditions, in the meantime, it meets the changing demands of cooling capacity. The effectiveness of the control performance is validated on the experimental rig.      

基于PSO优化的燃油供给压力动态跟踪控制

燃烧用油料供给压力的精确控制是一个具有非线性特性的流体输送压力控制问题,针对现有PID控制存在的不足,提出了基于PSO优化的预测函数控制(PSO-PFC)方法,通过与PFC,PID控制方法的仿真比较,以及对阳极焙烧炉重油燃烧供给压力的动态跟踪控制表明,该方法优于原有燃油燃烧系统的PID控制,以及常规和其他改进的PFC控制方法,实现了燃油供给压力的动态跟踪精确控制。     

一种基于衰减集结的鲁棒预测控制器

高效鲁棒预测控制 (ERPC) 是一种在线计算量较小, 且控制性能较好的鲁棒预测控制算法. 但采用单一椭圆不变集的设计方法存在保守性. 本文采用衰减集结策略, 通过离线设计在系统状态空间中投影彼此正交的两个椭圆不变集, 在线进行凸组合的方法设计 ERPC 控制器, 使系统初始可行域进一步扩大, 并在一定程度上改善了控制性能.     

六自由度机械臂约束预测控制系统的设计

本文提出了一种基于约束预测控制的机械臂实时运动控制方法.该控制方法分为两层,分别设计了约束预测控制器和跟踪控制器.其中,约束预测控制器在考虑系统物理约束的条件下,在线为跟踪控制器生成参考轨迹;跟踪控制器采用最优反馈控制律,使机械臂沿参考轨迹运动.为了简化控制器的设计和在线求解,本文采用输入输出线性化的方式简化机械臂动力学模型.同时,为了克服扰动,在约束预测控制器中引入前馈策略,提出了带前馈一反馈控制结构的预测控制设计.因此,本文设计的控制器可以使机械臂在满足物理约束的条件下快速稳定地跟踪到目标位置.通过在PUMA560机理模型上进行仿真实验,验证了预测控制算法的可行性和有效性.     

网路化双线性系统的预测控制优化算法研究

本文对一类离散时间双线性系统进行网络化预测控制研究.针对控制系统网络信道传输引起的前向通道和反馈通道时延问题,基于双线性系统结构特性提出2种逐步优化算法对非凸优化问题进行求解,进而得到未来时刻的预测控制序列.仿真实例说明所求预测控制序列可以主动补偿网络引起的时延问题,从而说明所提出预测控制算法的有效性.     

Terminal sliding mode control of rail pressure for gasoline direct injection engines

The desired fuel rail pressure is a crucial factor for guaranteeing the gasoline direct injection (GDI) engine to work stably. In order to solve the rail pressure control problem, the detailed nonlinear model of GDI is derived and reasonable simplification of this model is carried out for the following controller design. Terminal sliding mode control strategy is proposed to design the rail pressure controller with Lyapunov stability. The designed approach with the fast terminal sliding mode surface makes the system have the capacity of global fast convergence and achieves precise tracking control. To demonstrate the validity of the designed control method, simulations are conducted by tracking the different reference rail pressures. Results show that the designed controller tracks the given reference accurately and has strong robustness.     

多值逻辑混合动态系统的Matlab仿真研究

模拟人的思维逻辑,分析人的思维在控制中的特点,同时融合基于泛布尔代数的逻辑控制器和基于模型的预测控制器的特点,提出一种多值逻辑混合动态系统,仿人逻辑预测控制器(HLPC).Matlab仿真表明,该控制器在模型匹配时性能良好,在模型失配时依然能满意运行,表现出响应快、鲁棒性强、超调量小的特点.该控制器结构简单、数学概念和物理背景清晰,可应用于工业控制领域.     

Robust output feedback model predictive control of constrained linear systems

This paper provides a solution to the problem of robust output feedback model predictive control of constrained, linear, discrete-time systems in the presence of bounded state and output disturbances. The proposed output feedback controller consists of a simple, stable Luenberger state estimator and a recently developed, robustly stabilizing, tube-based, model predictive controller. The state estimation error is bounded by an invariant set. The tube-based controller ensures that all possible realizations of the state trajectory lie in a simple uncertainty tube the ‘center’ of which is the solution of a nominal (disturbance-free) system and the ‘cross-section’ of which is also invariant. Satisfaction of the state and input constraints for the original system is guaranteed by employing tighter constraint sets for the nominal system. The complexity of the resultant controller is similar to that required for nominal model predictive control.     

参数时变系统的广义预测控制研究

针对参数时变系统,研究了自适应预测控制器的设计问题.采用隐式广义预测控制算法(IGPC),无需辨识对象模型参数,而是利用输入/输出数据直接辩识控制器参数,以求解最优控制增量,具有计算量小、实时性高的特点.仿真结果表明,在不需要关于被控对象先验知识的情况下,隐式广义预测控制器可以很好地跟踪设定值的变化,同时对于系统外部干扰和模型参数的变化具有很好的适应能力,在参数时变系统控制器设计中具有良好的应用前景.