Predictive control for mechanical system with backlash based on hybrid model

The mechanical system with backlash is distinguished between a＂backlash mode＂and a＂contact mode＂.The inherent switching between the two operating modes makes the system a prime example of hybrid system.For eliminating the bad effect of backlash, a piecewise affine（PWA） model of the mechanical servo system with backlash is built.The optimal control of constrained PWA system is obtained by taking advantage of model predictive control（MPC） method, and the explicit solution of MPC in a look-up table form is figured out by combining the dynamic programming and multi-parametric quadratic programming, thereby establishing an explicit hybrid model predictive controller.Furthermore, a piecewise quadratic（PWQ） function for guaranteeing the stability of closed-loop control is found by formulating the search of PWQ function as a semi-definite programming problem.In the tracking experiments, it is demonstrated that the explicit hybrid model predictive controller has a good traction control effect on the mechanical system with backlash.The error meets the demands of real system.Further, compared to the direct on-line computation, the computation burden is reduced by the explicit solution, thereby being suitable for real-time control of system with short sampling time.

A Hybrid Approach to Modeling and Control of Vehicle Height for Electronically Controlled Air Suspension

The control problems associated with vehicle height adjustment of electronically controlled air suspension （ECAS） still pose theoretical challenges for researchers, which manifest themselves in the publications on this subject over the last years. This paper deals with modeling and control of a vehicle height adjustment system for ECAS, which is an example of a hybrid dynamical system due to the coexistence and coupling of continuous variables and discrete events. A mixed logical dynamical （MLD） modeling approach is chosen for capturing enough details of the vehicle height adjustment process. The hybrid dynamic model is constructed on the basis of some assumptions and piecewise linear approximation for components nonlinearities. Then, the on-off statuses of solenoid valves and the piecewise approximation process are described by propositional logic, and the hybrid system is transformed into the set of linear mixed-integer equalities and inequalities, denoted as MLD model, automatically by HYSDEL. Using this model, a hybrid model predictive controller （HMPC） is tuned based on online mixed-integer quadratic optimization （MIQP）. Two different scenarios are considered in the simulation, whose results verify the height adjustment effectiveness of the proposed approach. Explicit solutions of the controller are computed to control the vehicle height adjustment system in realtime using an offline multi-parametric programming technology （MPT）, thus convert the controller into an equivalent explicit piecewise affine form. Finally, bench experiments for vehicle height lifting, holding and lowering procedures are conducted, which demonstrate that the HMPC can adjust the vehicle height by controlling the on-off statuses of solenoid valves directly. This research proposes a new modeling and control method for vehicle height adjustment of ECAS, which leads to a closed-loop system with favorable dynamical properties.

Hybrid Neural Network Model for RH Vacuum Refining Process Control

A hybrid neural network model,in which RH process(theoretical)model is combined organically with neural network(NN)and case-base reasoning(CBR),was established.The CBR method was used to select the operation mode and the RH operational guide parameters for different steel grades according to the initial conditions of molten steel,and a three-layer BP neural network was adopted to deal with nonlinear factors for improving and compensating the limitations of technological model for RH process control and end-point prediction.It was verified that the hybrid neural network is effective for improving the precision and calculation efficiency of the model.

Mathematical Model and Its Hybrid Dynamic Mechanism in Intelligent Control of Ironmaking

A hybrid dynamic model was proposed, which considered both the hydrokinetic and the chaotic properties of the blast furnace ironmaking process; and great emphasis was put on its mechanism. The new model took the high complexity of the blast furnace as well as the effects of main parameters of the model into account, and the predicted results were in very good agreement with actual data.

Dynamics Modeling and Robust Trajectory Tracking Control for a Class of Hybrid Humanoid Arm Based on Neural Network

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm（HHA）, which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network（NN）-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function（RBF） NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.

Max-plus-linear model-based predictive control for constrained hybrid systems: linear programming solution

In this paper, a linear programming method is proposed to solve model predictive control for a class of hybrid systems. Firstly, using the （max, ＋） algebra, a typical subclass of hybrid systems called max-plus-linear （MPL） systems is obtained. And then, model predictive control （MPC） framework is extended to MPL systems. In general, the nonlinear optimization approach or extended linear complementarity problem （ELCP） were applied to solve the MPL-MPC optimization problem. A new optimization method based on canonical forms for max-min-plus-scaling （MMPS） functions （using the operations maximization, minimization, addition and scalar multiplication） with linear constraints on the inputs is presented. The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization. The validity of the algorithm is illustrated by an example.

Hybrid force control of astronaut rehabilitative training robot under active loading mode

In order to mitigate the effects of space adaptation syndrome(SAS) and improve the training efficiency of the astronauts, a novel astronaut rehabilitative training robot(ART) was proposed. ART can help the astronauts to carry out the bench press training in the microgravity environment. Firstly, a dynamic model of cable driven unit(CDU) was established whose accuracy was verified through the model identification. Secondly, to improve the accuracy and the speed of the active loading, an active loading hybrid force controller was proposed on the basis of the dynamic model of the CDU. Finally, the actual effect of the hybrid force controller was tested by simulations and experiments. The results suggest that the hybrid force controller can significantly improve the precision and the dynamic performance of the active loading with the maximum phase lag of the active loading being 9° and the maximum amplitude error being 2% at the frequency range of 10 Hz. The controller can meet the design requirements.

Test and numerical simulation of a new type of hybrid control technique

In this paper, a new hybrid control technique, based on a combination of base-isolation and semi-active variable stiffness/damping in a superstructure, is presented. To illustrate the efficiency of the proposed control system, model tests on a mini-electromagnetic shaking table and a numerical simulation were performed. The test and numerical calculation results indicate that this new hybrid control mode with additional damping and smaller additional stiffness can achieve a better control efficiency.

Hybrid Power Systems Energy Controller Based on Neural Network and Fuzzy Logic

This paper presents a novel adaptive scheme for energy management in stand-alone hybrid power systems. The proposed management system is designed to manage the power flow between the hybrid power system and energy storage elements in order to satisfy the load requirements based on artificial neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic controller is developed to distribute the power among the hybrid system and to manage the charge and discharge current flow for performance optimization. The developed management system performance was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton exchange membrane fuel cell (PEMFC). To improve the generating performance of the PEMFC and prolong its life, stack temperature is controlled by a fuzzy logic controller. The dynamic behavior of the proposed model is examined under different operating conditions. Real-time measured parameters are used as inputs for the developed system. The proposed model and its control strategy offer a proper tool for optimizing hybrid power system performance, such as that used in smart-house applications.

Earth-observation satellite attitude control using passive and active hybrid magnetically suspended flywheels

The control strategy is presented using passive and active hybrid magnetically suspended flywheels（P＆A MSFWs）,which can help meet the requirements of high precision and high stability for earth-observation satellites.Compared with the conventional flywheel,P＆A MSFW has more rotation degrees of freedom（DOFs）since the rotor is suspended by magnetic bearings,and thus requires more efficient controllers.A modified sliding mode control law（SMC）to our novel nonlinear and coupled system is presented,which is interrupted by inertia matrix uncertainties and external disturbances.SMC law via Lyapunov method is improved,and a fuzzy control scheme is used to attenuate the chatting and control attitude accuracy and maintain the robustness of SMC.Simulation results are provided to illustrate the efficiency of our model by using our control law.

Constant Speed Control of Four-stroke Micro Internal Combustion Swing Engine

The increasing demands on safety, emission and fuel consumption require more accurate control models of micro internal combustion swing engine （MICSE）. The objective of this paper is to investigate the constant speed control models of four-stroke MICSE The operation principle of the four-stroke MICSE is presented based on the description of MICSE prototype. A two-level Petri net based hybrid mode/ is proposed to mode/ the four-stroke MICSE engine cycle. The Petri net subsystem at the upper level controls and synchronizes the four Petri net subsystems at the lower level. The continuous sub-models, including breathing dynamics of intake manifold, thermodynamics of the chamber and dynamics of the torque generation, are investigated and integrated with the discrete model in MATLAB Simulink. Through the comparison of experimental data and simulated DC voltage output, it is demonstrated that the hybrid model is valid for the four-stroke MICSE system. A nonlinear model is obtained from the cycle average data via the regression method, and it is linearized around a given nominal equilibrium point for the controller design. The feedback controller of the spark timing and valve duration timing is designed with a sequential loop closing design approach. The simulation of the sequential loop closure control design applied to the hybrid model is implemented in MATLAB. The simulation results show that the system is able to reach its desired operating point within 0.2 s, and the designed controller shows good MICSE engine performance with a constant speed. This paper presents the constant speed control models of four-stroke MICSE and carries out the simulation tests, the models and the simulation results can be used for further study on the precision control of four-stroke MICSE.

基于Modelica的混动车型控制策略分析

文章首先介绍了一种基于行星齿轮的混动车型控制策略,随后在MWorks软件中针对此结构策略进行模型搭建,并对所建模型进行经济性、动力性仿真计算。最后将仿真数据与某款竞品混动车型进行对比分析,以验证此控制策略的可行性。

计及SOC自恢复的混合储能平抑风电功率波动控制

混合储能系统能够较好地应对复杂的风电波动,有效地提高电网的稳定性和安全性。在混合储能平抑风电功率波动的典型应用场景下,该文首先提出一种计及荷电状态(SOC)自恢复的混合储能平抑风电功率波动控制方法,在满足风电平抑需求的情况下,通过模型预测控制快速调节储能在平抑功率过程中的荷电状态,提高储能持续稳定运行能力;然后,为提高混合储能系统协调运行能力,设计了加权滑动平均(WMA)-模糊控制策略对超级电容和蓄电池功率进行动态分配;最后,结合实际风电功率数据,通过仿真验证了所提策略能有效平衡储能寿命和平抑风电波动的矛盾,能充分考虑两种储能设备的特性差异并提高功率分配的合理性。

运行工况自适应的风储系统双层功率平滑策略

针对风电并网带来的功率波动问题,提出一种运行工况自适应的风电-混合储能系统双层功率平滑策略。在上层,考虑风储系统并网功率波动和储能运行状态,利用无模型自适应控制动态跟踪经滑动平均算法得到的并网参考功率,生成混合储能总功率任务;在下层,考虑储能介质的技术特性,利用模糊控制动态调节经变分模态分解确定的混合储能总功率任务的频率分界点,完成功率的自适应分配。算例分析表明:所提策略可以自适应平抑多工况下的风电功率波动,实现混合储能各介质间功率的合理分配。

基于模型预测控制的高频隔离型混合配电变压器

配电台区新型源/荷占比逐年提升,使得高/低电压、谐波放大、三相不平衡等配电终端电能质量问题凸显。提出一种基于模型预测控制的高频隔离型混合配电变压器,具备电能质量综合治理、交/直流混合配电等功能,可以满足配电终端用户高品质供用电以及新型源/荷友好接入需求。介绍了高频隔离型混合配电变压器的拓扑结构并推导了数学模型。结合拓扑结构特点,设计了连续控制集模型预测控制(CCS-MPC)策略和延时模型预测控制策略,CCS-MPC通过计算最优化占空比,实现前端变换器输出具有固定的开关频率和开关序列,减小寻优计算量。利用MATLAB/Simulink仿真软件搭建了一套10 kV/0.4 kV电压等级的高频隔离型混合配电变压器系统,验证了所提拓扑结构电能质量综合治理能力的有效性。

基于模型预测框架的燃料电池汽车混合动力系统能量管理

混合动力系统作为燃料电池汽车的关键组成部分,系统的能耗与寿命优化是推动其进一步商业化发展的关键,而能量管理策略(EMS)在提高燃料电池汽车混合动力系统的效率和燃料经济性方面发挥着重要作用。为有效降低混合动力系统的内部损耗,进一步提高系统经济性,提出了一种基于模型预测框架的混合动力系统能耗优化能量管理控制方法。首先构建包含动力系统内部损耗与氢气消耗的目标函数,然后利用模型预测架构求解目标函数来控制燃料电池的输出电流,以实现合理的能量分配,最后在Matlab中进行仿真,验证所制定方法的合理性和有效性。与应用较为广泛的等效氢耗最小策略(ECMS)相比,结果表明所提方法可以显著降低动力系统的内部损耗和氢气消耗。此外,该策略还可以有效保持蓄电池的荷电状态(SOC),延长动力系统的使用寿命。

复杂地形下轮腿复合机动平台动态运动控制

复杂地形环境下质心参考轨迹的动态精确跟踪是保障轮腿复合机动平台稳定执行各项任务的关键。为提升平台的地形适应能力与位姿跟踪能力,提出一种动态运动控制策略。综合地形因素,建立包含车轮动力学的单刚体动力学模型,并通过近似简化将系统动力学模型转化为状态空间方程的标准形式。考虑车轮与腿部耦合运动,提出一种基于前馈力矩与反馈力矩的混合运动控制方法。通过二次规划算法求解最优地面反作用力,利用雅克比矩阵将作用力映射至关节以获取前馈力矩。为避免由环境引起的外部扰动造成系统无法在较短时间内完成优化计算,引入关节力矩反馈控制及时修正位姿跟踪误差,使系统能够快速准确地响应外部扰动,有效提高系统的鲁棒性和稳定性。仿真结果表明,新方法可有效提升平台在复杂地形下位姿动态跟踪精度,保障平台平稳运行,为复杂地形下轮腿复合机动平台的工程应用提供有力支撑。

基于FCS-MPC的分布式HESS功率协调控制

针对孤岛直流微电网中分布式混合储能系统(HESS)协调运行问题,提出一种基于有限控制集模型预测控制(FCSMPC)的功率协调控制策略。该策略采用小波包变换来分解分布式HESS的功率需求,由于分解后信号的特点和分布式HESS中蓄电池初始状态的不同,于是对分解后最低频信号基于蓄电池荷电状态(SOC)设计自适应因数来进一步再分解。由于DC/DC变换器开关状态组合数量较少,使得多步FCS-MPC的滚动优化计算量较少,于是采用FCS-MPC取代双闭环PI控制,提高系统动态响应。搭建含有分布式HESS的孤岛直流微电网模型,并做了3种方案的仿真对比,验证该策略有效平缓了直流母线波动并实现蓄电池SOC一致。该策略有利于提高电网运行的稳定性,并延长蓄电池使用寿命。

考虑车网互动的园区电网动态双层能量管理策略

为解决大量接入的清洁能源出力波动和充电场内汽车数量的随机变化给电网能量管理带来的困难,提出一种园区电网双层优化控制策略.上层基于模型预测控制技术建立风光储集成动态优化模型,综合应用园区电池储能系统和电动汽车(EV)电池储能系统参与电网能量平衡.下层则考虑同时满足EV车主对EV的充电需求和上层能量调度的车网互动管理需求,制定园区电网内停车场的EV有序充放电策略.仿真算例验证表明:所提策略能将分散园区各处的EV储能集成为统一虚拟储能,扩展园区电网储能系统,增加可再生分布式电源的消纳与园区电网的经济效益.

基于模型预测控制的双机组混合动力船舶能量管理研究

[目的]船舶柴电混合动力系统合理分配柴油机和电机输出功率,可大幅降低油耗和排放。针对传统混合动力能量管理策略的性能最优与运算实时的矛盾,提出采用模型预测控制(MPC)进行能量管理瞬时优化。[方法]首先,利用反向建模法搭建由双柴油发电机组、储能系统、岸电组成的客渡船混合动力系统能量流模型;然后,提出以油耗和电能消耗的总温室气体(GHG)排放为目标函数,在系统约束条件下可在线滚动优化求解的MPC能量管理算法,最终进一步进行了不同预测时域长度的灵敏度分析。[结果]仿真结果表明,MPC较传统规则控制方法可分别降低4.85%的燃油消耗量和3.54%的温室气体总排放。[结论]相比于传统的规则控制策略,MPC油耗低,温室气体排放少,且计算负荷较小,具有良好的实船应用潜力。