Intelligent state estimation for fault tolerant nonlinear predictive control

There is growing realization that on-line model maintenance is the key to realizing long term benefits of a predictive control scheme. In this work, a novel intelligent nonlinear state estimation strategy is proposed, which keeps diagnosing the root cause(s) of the plant model mismatch by isolating the subset of active faults (abrupt changes in parameters/disturbances, biases in sensors/actuators, actuator/sensor failures) and auto-corrects the model on-line so as to accommodate the isolated faults/failures. To carry out the task of fault diagnosis in multivariate nonlinear time varying systems, we propose a nonlinear version of the generalized likelihood ratio (GLR) based fault diagnosis and identification (FDI) scheme (NL-GLR). An active fault tolerant NMPC (FTNMPC) scheme is developed that makes use of the fault/failure location and magnitude estimates generated by NL-GLR to correct the state estimator and prediction model used in NMPC formulation. This facilitates application of the fault tolerant scheme to nonlinear and time varying processes including batch and semi-batch processes. The advantages of the proposed intelligent state estimation and FTNMPC schemes are demonstrated by conducting simulation studies on a benchmark CSTR system, which exhibits input multiplicity and change in the sign of steady state gain, and a fed batch bioreactor, which exhibits strongly nonlinear dynamics. By simulating a regulatory control problem associated with an unstable nonlinear system given by Chen and Allgower [H. Chen, F. Allgower, A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability, Automatica 34(10) (1998) 1205–1217], we also demonstrate that the proposed intelligent state estimation strategy can be used to maintain asymptotic closed loop stability in the face of abrupt changes in model parameters. Analysis of the simulation results reveals that the proposed approach provides a comprehensive method for treating both faults (biases/drifts in sensors/actuators/model parameters) and failures (sensor/ actuator failures) under the unified framework of fault tolerant nonlinear predictive control.     

Optimization-based tuning of nonlinear model predictive control with state estimation

Nonlinear model predictive controllers determine appropriate control actions by solving an on-line optimization problem. A nonlinear process model is utilized for on-line prediction, making such algorithms particularly appropriate for the control of chemical reactors. The algorithms presented in this paper incorporates an extended Kalman filter, which allows operations around unstable steady-state points. The paper proposes a formalization of the procedure for tuning the several parameters of the control algorithm. This is accomplished by specifying time-domain performance criteria and using an interactive multi-objective optimization package off-line to determine parameters values that satisfy these criteria. Three reactor examples are used to demonstrate the effectiveness of the proposed on-line algorithm and off-line tuning procedure.     

Stabilization of nonlinear systems with state and control constraints using Lyapunov-based predictive control

This work considers the problem of stabilization of nonlinear systems subject to state and control constraints, for cases where the state constraints need to be enforced at all times (hard constraints) and where they can be relaxed for some time (soft constraints). We propose a Lyapunov-based predictive control design that guarantees stabilization and state and input constraint satisfaction for all times from an explicitly characterized set of initial conditions. An auxiliary Lyapunov-based analytical bounded control design is used to characterize the stability region of the predictive controller and also provide a feasible initial guess to the optimization problem in the predictive controller formulation. For the case when the state constraints are soft, we propose a switched predictive control strategy that reduces the time during which state constraints are violated, driving the states into the state and input constraints feasibility region of the Lyapunov-based predictive controller. We demonstrate the application of the Lyapunov-based predictive controller designs through a chemical process example.     

Robust hybrid predictive control of nonlinear systems

In this work, we consider nonlinear systems with input constraints and uncertain variables, and develop a robust hybrid predictive control structure that provides a safety net for the implementation of any model predictive control (MPC) formulation, designed with or without taking uncertainty into account. The key idea is to use a Lyapunov-based bounded robust controller, for which an explicit characterization of the region of robust closed-loop stability can be obtained, to provide a stability region within which any available MPC formulation can be implemented. This is achieved by devising a set of switching laws that orchestrate switching between MPC and the bounded robust controller in a way that exploits the performance of MPC whenever possible, while using the bounded controller as a fall-back controller that can be switched in at any time to maintain robust closed-loop stability in the event that the predictive controller fails to yield a control move (due, e.g., to computational difficulties in the optimization or infeasibility) or leads to instability (due, e.g., to inappropriate penalties and/or horizon length in the objective function). The implementation and efficacy of the robust hybrid predictive control structure are demonstrated through simulations using a chemical process example.     

A hybrid model predictive control strategy for nonlinear plant-wide control

A plant-wide control strategy based on integrating linear model predictive control (LMPC) and nonlinear model predictive control (NMPC) is proposed. The hybrid method is applicable to plants that can be decomposed into approximately linear subsystems and highly nonlinear subsystems that interact via mass and energy flows. LMPC is applied to the linear subsystems and NMPC is applied to the nonlinear subsystems. A simple controller coordination strategy that counteracts interaction effects is proposed for the case of one linear subsystem and one nonlinear subsystem. A reactor/separator process with recycle is used to compare the hybrid method to conventional LMPC and NMPC techniques.     

Optimized treatment of fibromyalgia using system identification and hybrid model predictive control

The term adaptive intervention is used in behavioral health to describe individually tailored strategies for preventing and treating chronic, relapsing disorders. This paper describes a system identification approach for developing dynamical models from clinical data, and subsequently, a hybrid model predictive control scheme for assigning dosages of naltrexone as treatment for fibromyalgia, a chronic pain condition. A simulation study that includes conditions of significant plant-model mismatch demonstrates the benefits of hybrid predictive control as a decision framework for optimized adaptive interventions. This work provides insights on the design of novel personalized interventions for chronic pain and related conditions in behavioral health.     

Laser-scanner-based object state estimation and tracking control algorithms of autonomous truck using single-wheel driving module

Microsystem Technologies - This paper describes state estimation and tracking control algorithms for use in an autonomous truck using a single-wheel driving module and simulation-based performance...     

Flood regulation using nonlinear model predictive control

In this paper the flood problem of the river Demer, a river located in Belgium, is discussed. First a simplified model of the Demer basin is derived based on the conceptual reservoir modeling concept. This model was calibrated to simulations results with a more detailed full hydrodynamic model. Afterwards, the focus is shifted to a nonlinear model predictive controller (NMPC) which is based on a new semi-condensed optimization procedure combined with a line search approach. Finally, simulations are performed based on historical data in which the NMPC is compared with the current control strategy used by the local water administration. Uncertainties are added to the rainfall predictions in order to assess the robustness of the NMPC.     

稳定的非线性系统广义预测控制

针对广义预测控制(GPC)算法稳定性分析困难,对参数未知非线性系统提出一种稳定广义预测控制(DGPC)方法。该方法首先将非线性系统转换为时变线性系统,然后利用三次样条基函数逼近时变系统中的系数,通过带时变遗忘因子的递推最小二乘算法辨识系数获得对象模型。基于模型通过性能指标中的前馈增益设计来保证控制系统稳定,仿真结果验证了该方法的有效性。     

State estimation and predictive control of fed-batch cultures of hybridoma cells

Fed-batch cultures of hybridoma cells are commonly used for the production of monoclonal antibodies (MAb). In this study, a simple macroscopic model of the cell culture is used, which is based on the overflow metabolism paradigm. This allows to specify optimal culture conditions, and the natural formulation of a nonlinear model predictive control strategy (NMPC). As not all the component concentrations are available for measurement, system observability is analyzed, and an unscented Kalman filter (UKF) is designed, which provides satisfactory estimates of glucose and glutamine concentrations. Robustness of the NMPC scheme is investigated, as well as the combined UKF+NMPC scheme, through a minimax robust version and the closed-loop system.     

混合驱动水下自航行器控制系统设计

将32位ARM微处理器应用于混合驱动水下自航行器的控制系统。首先介绍了混合驱动AUV控制系统的总体体系结构,将CAN总线应用于混合驱动AUV,开发了基于CAN总线的分布式控制系统;简单介绍了以ARM7微处理器LPC2129为主控制芯片的控制系统硬件设计;制定了适用于混合驱动AUV的CAN应用层协议和相应的软件,遵照分层递阶的体系结构设计了控制系统的软件,并采取软硬件相结合的方法解决了控制系统的可靠性问题。联调实验证明该系统性能稳定、工作可靠     

A multiple model approach for predictive control of nonlinear hybrid systems

This paper presents modeling and control of nonlinear hybrid systems using multiple linearized models. Each linearized model is a local representation of all locations of the hybrid system. These models are then combined using Bayes theorem to describe the nonlinear hybrid system. The multiple models, which consist of continuous as well as discrete variables, are used for synthesis of a model predictive control (MPC) law. The discrete-time equivalent of the model predicts the hybrid system behavior over the prediction horizon. The MPC formulation takes on a similar form as that used for control of a continuous variable system. Although implementation of the control law requires solution of an online mixed integer nonlinear program, the optimization problem has a fixed structure with certain computational advantages. We demonstrate performance and computational efficiency of the modeling and control scheme using simulations on a benchmark three-spherical tank system and a hydraulic process plant.     

Modeling,state estimation and nonlinear model predictive control of cathode exhaust gas mass flow for PEM fuel cells

Polymer electrolyte membrane fuel cells are efficient energy converters and provide electrical energy, water and oxygen depleted air with a low oxygen content as exhaust gas if fed with air. Due to their low emission of greenhouse gases and noise they are investigated as replacement for auxiliary power units currently used for electrical power supply on aircraft. Oxygen depleted air, called ODA-gas, with an oxygen concentration of 10–11% and a low humidity can be used for tank-inerting on aircraft. A challenging task is controlling the fuel cell system for generation of dehumidified ODA-gas mass flow while simultaneously keeping bounds and gradients on control inputs. This task is attacked by a nonlinear model predictive control. Not all system states can be measured and some states measured exhibit a significant time delay. A nonlinear state estimation strategy builds the entire system state and compensates for the delay. The nonlinear model predictive control and the state estimation are derived from the system model, which is presented. Simulation and experimental results are shown.     

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.     

非线性系统智能状态估计研究进展与展望

综述了近年来基于智能方法的非线性系统滤波器和观测器的设计方法.关于滤波器,一方面介绍了基于智能方法辨识系统模型而设计的间接滤波器,分析了模型偏差修正的重要性;另一方面探讨了基于智能方法设计的直接滤波器的研究进展.关于观测器,重点介绍了基于神经网络的广义Luenberger观测器的设计方法,总结了该观测器稳定性和鲁棒性的理论分析结果,并进一步介绍了适用范围更广的智能自适应鲁棒观测器的设计方法.最后,对非线性估计问题的进一步研究提出了几点展望.     

Output-feedback predictive control of constrained linear systems via set-membership state estimation

This paper combines model predictive control (MPC) and set-membership (SM) state estimation techniques for controlling systems subject to hard input and state constraints. Linear systems with unknown but bounded disturbances and partial state information are considered. The adopted approach guarantees that the constraints are satisfied for all the states which are compatible with the available information and for all the disturbances within given bounds. Properties of the proposed MPC-SM algorithm and simulation studies are reported.     

基于多模型的非线性系统广义预测控制

对于复杂的离散时间非线性系统，提出一种基于多模型的广义预测控制方法．通过在平衡点附近建立线性模型，并用径向基函数神经网络来补偿匹配误差，形成了非线性系统的多模型表示，然后采用模糊识别方法作为切换法则，并结合广义预测控制构成了多模型广义预测控制器．通过对连续发酵过程的计算机仿真，表明了该方法的有效性．     

非线性系统神经网络预测控制研究进展

摘 要：神经网络由于其在非线性系统建模与优化求解方面的优势,被广泛应用于预测控制中,形成了各种各样的神经网络预测控制算法。本文系统地评述了非线性系统神经网络预测控制系统中的模型选取、控制器优化、控制系统结构设计方法以及收敛性理论等研究现状,分析了非线性系统神经网络预测控制算法存在的问题和今后的研究方向。