A general NMPC framework for a diesel engine air path

This paper presents the formulation of a parameterised nonlinear model predictive control (NMPC) scheme to be applied on a diesel engine air path. The most important feature of the proposed controller is that it uses no structural properties of the system model. Therefore, the proposed NMPC scheme can be applied to any nonlinear system, leading to a general framework for a diesel engine air path. Moreover, the computational burden is substantially reduced due to an optimisation problem of low dimension obtained by means of the parameterised approach. Simulation results and an experimental validation are presented in order to emphasise the controller's efficiency and the real-time implementability.     

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.     

Motion planning for experimental air path control of a variable-valve-timing spark ignition engine

Air path control of a spark ignition engine without an EGR loop, equipped with variable-valve-timing (VVT) actuators, is addressed in this paper. VVT devices are used to produce internal exhaust gas recirculation, providing beneficial effects in terms of fuel consumption and pollutant emissions reduction. However, VVT actuators affect the fresh air charge in the cylinders. This has an impact on the torque output (leading to driveability problems) and on the fuel/air ratio (FAR) (leading to pollution peaks). To compensate for these undesirable effects, a new approach is proposed. Supportive experimental results show the relevance of this approach.     

Fuzzy adaptive nonlinear sensor‐fault tolerant control for a quadrotor unmanned aerial vehicle

In this paper, a novel fuzzy adaptive nonlinear fault tolerant control design scheme is proposed for attitude dynamics of quadrotor UAV subjected to four sensor faults (bias, drift, loss of accuracy, loss of effectiveness). The sensor faults in Euler angle loop are transformed equivalently into a mismatched uncertainty vector, and other unknown items involving faults, uncertain parameters and external disturbances in angular velocity loop are lumped into an unknown nonlinear function vector. Fuzzy logic systems with adaptive parameters are used to approximate the mismatched uncertainty and lumped nonlinear function vectors. Dynamic surface control is applied to design the fault tolerant controller, and sliding mode control is introduced to improve the control accuracy. All signals of the closed‐loop control system are proved to be semi‐global uniformly ultimately bounded. Simulations demonstrate the effectiveness of the proposed approach for sensor faults.     

A performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control

This paper presents a performance optimization algorithm for controller reconfiguration in fault tolerant distributed model predictive control for large-scale systems. After the fault has been detected and diagnosed, several controller reconfigurations are proposed as candidate corrective actions for fault compensation. The solution of a set of constrained optimization problems with different actuator and setpoint reconfigurations is derived by means of an original approach, exploiting the information on the active constraints in the non-faulty subsystems. Thus, the global optimization problem is split into two optimization subproblems, which enable the online computational burden to be greatly reduced. Subsequently, the performances of different candidate controller reconfigurations are compared, and the better performing one is selected and then implemented to compensate the fault effects. Efficacy of the proposed approach has been shown by applying it to the benzene alkylation process, which is a benchmark process in distributed model predictive control.     

永磁容错游标轮缘推进电机改进模型预测转矩控制

为提高永磁容错游标轮缘推进电机(FTPMV-RDM)在正常状态和一相开路故障状态下的控制性能, 本文提出了一种基于电压矢量预选的改进模型预测转矩(MPTC)控制方法. 针对六相独立H桥逆变器提供的备选电压矢量数量多导致MPTC系统计算量大的问题, 首先, 采用直接转矩控制中利用转矩、磁链误差及定子磁链位置信息确定预选电压矢量, 减少MPTC系统中电压矢量的枚举次数. 然后, 利用价值函数进行二次筛选得到最优电压矢量. 为了实现开路故障下的容错控制, 提出了一种更换备选电压矢量表的开路故障容错控制策略. 实验结果表明, 基于电压矢量预选的FTPMV-RDM模型, 本文预测转矩控制算法能够在无故障和一相开路故障下抑制电流畸变, 进而有效降低转矩和磁链脉动.     

Sensorless fault tolerant control for induction motors

In this paper, a sensorless fault tolerant controller for induction motors is developed. In the proposed approach, a robust controller based on backstepping strategy is designed in order to compensate for both the load torque disturbance and the rotor resistance variation caused by the broken rotor bars faults. The proposed approach needs neither fault detection and isolation schemes nor controller re-design. Moreover, to avoid the use of speed and flux sensors, a second order sliding mode observer is introduced to estimate the flux and the speed. The observer converges in a finite time and leads to good estimates of the flux and the speed even in the presence of the rotor resistance variation and the load torque disturbance. Since the observer converges in the finite time, the stability of the closed-loop system (controller with observer) is shown in two steps. First, the boundedness of the closed-loop system trajectories before the convergence of the observer is proved. Second, the convergence of the closed-loop system trajectories is proved after the convergence of the observer. To highlight the efficiency and applicability of the proposed control scheme, simulation and experimental results are conducted for a 1.5 kW induction motor.     

Multisensor fusion fault tolerant control

In this paper, a multisensor fusion fault tolerant control system with fault detection and identification via set separation is presented. The fault detection and identification unit verifies that for each sensor–estimator combination, the estimation tracking errors lie inside pre-computed sets and discards faulty sensors when their associated estimation tracking errors leave the sets. An active fault tolerant controller is obtained, where the remaining healthy estimates are combined using a technique based on the optimal fusion criterion in the linear minimum-variance sense. The fused estimates are then used to implement a state feedback tracking controller. We ensure closed-loop stability and performance under the occurrence of abrupt sensor faults. Experimental validation, illustrating the multisensor fusion fault tolerant control strategy is included.     

Fault tolerant control using a fuzzy predictive approach

This paper proposes the application of fault-tolerant control (FTC) using fuzzy predictive control. The FTC approach is based on two steps, fault detection and isolation (FDI) and fault accommodation. The fault detection is performed by a model-based approach using fuzzy modeling and fault isolation uses a fuzzy decision making approach. The information obtained on the FDI step is used to select the model to be used in fault accommodation, in a model predictive control (MPC) scheme. The fault accommodation is performed with one fuzzy model for each identified fault. The FTC scheme is used to accommodate the faults of two systems a container gantry crane and three tank benchmark system. The fuzzy FTC scheme proposed in this paper was able to detect, isolate and accommodate correctly the considered faults of both systems.     

运行控制系统集中式容错控制方法

本文针对运行控制系统,建立了运行优化控制过程的双层结构模型.在此基础上,通过建立相应的自适应故障诊断算法,提出了保证在系统有故障和干扰时仍能渐近优化指标的集中式容错控制方法,利用李雅普诺夫稳定性理论分析了自适应故障诊断算法的构建.已证明:该方法通过调整已优化的设定值来保证在回路控制层出现故障时整个运行控制仍可收敛到其原有的优化控制效果.该方法属于非完备容错控制,仿真结果验证了所提方法的有效性.     

可重构模块机器人分散容错控制

针对可重构模块机器人的执行器故障,提出一种基于自适应模糊系统的分散被动容错控制方法.该方法不需要机器人动力学模型与模块之间的信息交换,模块控制器分别采用间接和直接自适应方法设计,自适应参数的更新律基于Lyapunov稳定性理论设计,保证了系统的稳定性和H∞跟踪性能.数值仿真结果表明了所提出方法的有效性.     

Robust fault tolerant tracking control design for a linearized hypersonic vehicle with sensor fault

International Journal of Control, Automation and Systems - In this study, the robust fault tolerant tracking problem is investigated for a linearized hypersonic vehicle model with the bounded...     

基于积分滑模的航天器有限时间姿态容错控制

针对存在执行机构故障和外部干扰的刚体航天器姿态稳定系统,本文提出了基于积分滑模的容错控制策略,实现了姿态有限时间稳定.首先,利用齐次系统相关理论,设计了一类饱和有界的基础控制律,保证了不存在执行机构故障和干扰情况下的姿态有限时间稳定.在此基础上,利用积分滑模和自适应技术设计了一种有限时间姿态鲁棒容错控制方案,对执行机构故障和干扰进行有效的补偿;该方案能够快速地实现姿态高精度稳定,并抑制系统抖振现象.最后,将本文提出的姿态容错控制方案进行数值仿真与对比,验证了方案的有效性与优越性.     

Sensor adaptive fault tolerant control for non-linear processes

Adaptive fault tolerant control of non-linear processes is an open problem. In this paper, on the basis of a strong tracking filter (STF), an approach to sensor adaptive fault tolerant generic model control for non-linear processes is proposed. When the process runs normally, Adaptive Generic Model Control (AGMC) based on parameter estimation is used to control non-linear time-varying processes. A sensor fault model is set up by introducing a bias vector into the output equation of the process. The bias vector is estimated on-line based on the STF during every control period. With the estimated sensor bias vector and the time-varying parameters, a fault detection mechanism is developed to supervise sensors. When a sensor fault is detected, AGMC will be switched to a state estimation and soft-sensor-based GMC. This strategy constitutes a sensor-adaptive fault tolerant generic model control for non-linear processes. Experimental results on a three-tank system demonstrate the effectiveness of the proposed approach.     

BDE-LSSVM在柴油机气门故障诊断中的应用

针对柴油机气门故障的诊断样本少和非线性数据特征等问题,最小二乘法的支持向量机（LSSVM）能够较好地进行诊断研究,但由于惩罚因子[C]和内核参数[σ]的选取对诊断结果影响较大,有必要对其进行参数优化,因此提出了基于二进制微分进化算法（BDE）的最小二乘法支持向量机算法。利用柴油机气门振动信号作为数据,经小波变换作为模型特征,建立了基于BDE-LSSVM故障诊断模型,并与基于遗传和基于粒子群算法的LSSVM模型进行柴油机气门故障诊断的性能对比。比较结果证明,基于BDE优化的LSSVM模型在故障特征选取前后具有更好的适应度值和稳定度,故障分类准确性高且运算速度更快。     

Supervisory fault tolerant control for a class of uncertain nonlinear systems

This paper focuses on the design of a unique scheme that simultaneously performs fault isolation and fault tolerant control for a class of uncertain nonlinear systems with faults ranging over a finite cover. The proposed framework relies on a supervisory switching among a family of pre-computed candidate controllers without any additional model or filter. The states are ensured to be bounded during the switching delay, which ends when the correct stabilizing controller has been selected. Simulation results about a flexible joint robotic example illustrate the efficiency of the proposed method.     

Diesel engine air path control based on neural approximation of nonlinear MPC

This paper deals with a control design problem for a diesel engine air path system that has strong nonlinearity and requires multi-input and multi-output control to satisfy requirements and constraints. We focus on a neural network based approximation of nonlinear model predictive control (NMPC) for high-speed computation. Most neural approximation methods are verified only through simulation; further, the influence of approximation on the closed-loop performance has been not sufficiently discussed. In this study, we discuss this influence, and propose a new method to improve stability against degradation due to an approximation error. The control system is assembled using a neural network based controller, obtained by the proposed method, and an unscented Kalman filter. This system is verified both numerically and experimentally; the results demonstrate the capability of the proposed method to track the boost pressure, EGR rate, and pumping loss according to the reference values, and satisfy the constraints of compressor surge and choke. The high computation speed that can be achieved using a standard on-board ECU is also demonstrated using the approximated controller.     

Robust adaptive fault tolerant control for a process with actuator faults

This paper studies design and implementation of an enhanced multivariable adaptive control scheme for an uncertain nonlinear process exposed to actuator faults. For adaptive fault compensation, a model reference adaptive control (MRAC) strategy is utilized as main controller. A new adaptation algorithm making possible to improve transient performance of adaptive control is integrated to the controller. With the help of further modifications, some restrictive conditions on multivariable adaptive design are relaxed so that the system requires less plant information. The resulting controller has a simpler structure than the other matrix factorization based controllers. At the final stage of design, a robust adaptive control scheme is obtained with consideration of practical implementation problems such as sensor noises, external disturbances and unmodeled​ system dynamics. It is proved that the controller guarantees closed-loop signal boundedness and asymptotic output tracking. Real-time experiment results acquired from quadruple tank benchmark system are presented in order to exhibit the effectiveness of the proposed scheme.     

Semi-physical mean-value NOx model for diesel engine control

A semi-physical model has been developed to predict nitrogen oxide (NO_x) emissions produced by diesel engines. This model is suitable for online NO_x estimation and for model-based engine control. It is derived from a zero-dimensional thermodynamic model which was simplified by only retaining main phenomena contributing to NO_x formation. The crank angle evolution of the burned gas temperature, which has a strong impact on NO_x formation rate, is described by a semi-empirical model whose key variable is the maximum burned gas temperature. This variable presents a good correlation with the molar fraction of NO_x at the end of combustion and can be expressed as a function of the intake burned gas ratio and the start of combustion. The maximum burned gas temperature sub-model is then coupled to an averaged NO_x formation kinetic model (based on the Zeldovich mechanism) to form a mean-value model for NO_x computation. This latter model was validated using data sets recorded in two diesel engines for steady-state operating conditions as well as for several driving cycles including parametric variations of the engine calibration.     

Fast neural networks for diesel engine control design

Advanced engine control systems require accurate dynamic models of the combustion process, which are substantially nonlinear. This contribution presents the application of fast neural net models for engine control design purposes. After briefly introducing a special local linear radial basis function network (LOLIMOT) the process of building adequate dynamic engine models is discussed in detail. These neuro-models are then integrated into an upper-level emission optimization tool which calculates a cost function for exhaust versus consumption/torque and determines optimal engine settings. A DSP-based process computer system allows a fast application of the optimization tool at the engine test stand.