Isolation and handling of actuator faults in nonlinear systems

This work considers the problem of control actuator fault detection and isolation and fault-tolerant control for a multi-input multi-output nonlinear system subject to constraints on the manipulated inputs and proposes a fault detection and isolation filter and controller reconfiguration design. The implementation of the fault detection and isolation filters and reconfiguration strategy are demonstrated via a chemical process example.     

Stochastic model predictive control for remanufacturing system management

Uncertainties in the quality, quantity, and operational time of used products pose a challenge to the management of remanufacturing systems. In addition, it becomes a necessity to optimize the operation of the remanufacturing system to balance the quality of products, remanufacturing efficiency, and service level. In this study, a stochastic discrete-time dynamical model is proposed to represent a remanufacturing system, where the relationship between the market satisfaction, inventory status, and operational actions is explicitly modeled. This includes production and inventory planning, resource allocation and acquisition. To handle uncertainties, a stochastic model predictive control approach is proposed to plan the actions that optimize the remanufacturing efficiency. Our results in the simulation examples show that: (a) without supplies, the remanufacturing system has better stability and robustness than a conventional manufacturing system with the same initial stocks; and (b) with insufficient initial stocks, the remanufacturing system demands fewer and more gradual supplies, thereby keeping the system stable. Finally, a sensitivity analysis is conducted for testing the performance of the remanufacturing system. By changing the operational action capacity, different state equilibria are discovered, which correspond to distinct system response characteristics. The study reveals notable managerial insights and effects of product commonality, demand patterns, and operational actions scheduling on the efficiency of the remanufacturing system.     

A stabilizing model predictive control for networked control system with data packet dropout

A constrained model predictive control （MPC） algorithm for networked control system with data packet dropout is proposed in this paper. A buffer is designed to store the predicted control sequence between controller and actuator. It is shown that if the control horizon of MPC is not less than the number of data packets lost continuously, feasibility of MPC at initial time implies asymptotical stability of the closed-loop system. A simulation example illustrates the effectiveness of the proposed approach.     

Sensor/actuator faults detection for networked control systems via predictive control

Quantized fault detection for sensor/actuator faults of networked control systems (NCSs) with time delays both in the sensor-to-controller channel and controller-to-actuator channel is concerned in this paper. A fault model is set up based on the possible cases of sensor/actuator faults. Then, the model predictive control is used to compensate the time delay. When the sensors and actuators are healthy, an H ∞ stability criterion of the state predictive observer is obtained in terms of linear matrix inequality. A new threshold computational method that conforms to the actual situation is proposed. Then, the thresholds of the false alarm rate (FAR) and miss detection rate (MDR) are presented by using our proposed method, which are also compared with the ones given in the existing literatures. Finally, some numerical simulations are shown to demonstrate the effectiveness of the proposed method.     

Distributed model predictive control of dynamically decoupled systems with coupled cost

This paper considers the distributed model predictive control (DMPC) of systems with interacting subsystems having decoupled dynamics and constraints but coupled costs. An easily-verifiable constraint is introduced to ensure asymptotic stability of the overall system in the absence of disturbance. The constraint introduced has a parameter which allows for the performance of the DMPC system to approach that controlled by a centralized model predictive controller. When the subsystems are linear and additive disturbance is present, the added constraint ensures the state of each subsystem converges to its respective minimal disturbance invariant set. The approach is demonstrated via several numerical examples.     

A stable block model predictive control with variable implementation horizon

In this paper, we present a stable model predictive control method for discrete-time nonlinear systems. The standard MPC scheme is modified to incorporate (1) a block implementation scheme where a sub-string of the optimized input sequence is applied instead of a single value; (2) an additional constraint which guarantees that a Lyapunov function will decrease over time; (3) a variable implementation window that facilitates the stability constraint enforcement. Stability of the closed-loop system with the proposed algorithm is established. Examples are given to illustrate the effectiveness of the control scheme. The impacts of several key design parameters on the overall performance are also analyzed and discussed.     

Backstepping control integrated with Lyapunov-based model predictive control

In this study, backstepping control integrated with Lyapunov-based model predictive control (BS-MPC) is proposed for nonlinear systems in a strict-feedback form. The virtual input of the first step is designed by solving the finite-horizon optimal control problem (FHOCP), and the real input is designed by the backstepping method. BS-MPC guarantees (semiglobal) ultimate boundedness of the closed-loop system when the control is implemented in a zero-order hold manner. When the robustness of BS-MPC is analyzed for uniformly bounded disturbances, the ultimate boundedness of the solution of perturbed system is guaranteed. BS-MPC can provide a better desired value of the virtual input of the first step by solving the FHOCP, resulting in a faster stabilization of the system compared with the backstepping control. In addition, BS-MPC requires less computational load compared with MPC because the dimension of the states considered in the on-line optimization problem of BS-MPC is lower than that of MPC.     

More relaxed conditions for model predictive control with guaranteed stability

1Introduction In recent years the receding horizon control(RHC)ormodel predictive control(MPC)has received muchattention[1].At each sampling time,model predictivecontrol(MPC)yields an optimal control sequence byonline solving a finite horizon open_loop optimizationproblem.Onlythefirst control actionis appliedtothe plantandthe process is repeated at the next samplingtime.The closed_loop stability of model predictive control hasbeenthe focus of research for a long time.In the90’s,through in…     

State space model predictive fault-tolerant control for batch processes with partial actuator failure

This paper presents a state space model predictive fault-tolerant control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the model predictive fault-tolerant control, the batch process is first treated into a non-minimal representation using state space transformation. The relevant concepts of the corresponding model predictive fault-tolerant control is thus introduced through state space formulation, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults, because, unlike traditional model predictive fault-tolerant control, the proposed control method can directly regulate the process output/input changes in the design. For performance comparison, a traditional model predictive fault-tolerant control is also designed. Application to injection velocity control shows that the proposed scheme achieve the design objective well with performance improvement.     

Fault-tolerant control for a class of hybrid systems with uncontrollable switching

This article focuses on the fault-tolerant control (FTC) problem for a class of hybrid systems modelled by hybrid automata. An observer-based FTC framework is proposed for the hybrid system with uncontrollable state-dependent switching and without full continuous state measurements. Two kinds of faults are considered: continuous faults that affect each mode and discrete faults that affect the mode transition. Sufficient conditions are given such that the hybrid system can be stabilised in the sense of LaSalle invariance principle. Simulation results of example of CPU processing control show the efficiency of the proposed method.     

Implications of discretization on dissipativity and economic model predictive control

Economic model predictive control, where a generic cost is employed as the objective function to be minimized, has recently gained much attention in model predictive control literature. Stability proof of the resulting closed-loop system is often based on strict dissipativity of the system with respect to the objective function. In this paper, starting with a continuous-time setup, we consider the ‘discretize then optimize’ approach to solving continuous-time optimal control problems and investigate the effect of the discretization process on the closed-loop system. We show that while the continuous-time system may be strictly dissipative with respect to the objective function, it is possible that the resulting closed-loop system is unstable if the discrete-approximation of the continuous-time optimal control problem is not properly set up. We use a popular example from the economic MPC literature to illustrate our results.     

Nonlinear model predictive control of a magnetic levitation system

The paper presents a fast nonlinear model predictive control (MPC) scheme for a magnetic levitation system. A nonlinear dynamical model of the levitation system is derived that additionally captures the inductor current dynamics of the electromagnet in order to achieve a high MPC performance both for stabilization and fast setpoint changes of the levitating mass. The optimization algorithm underlying the MPC scheme accounts for control constraints and allows for a time and memory efficient computation of the single iteration. The overall control performance of the levitation system as well as the low computational costs of the MPC scheme is shown both in simulations and experiments with a sampling frequency of 700 Hz on a standard dSPACE hardware.     

Fault tolerant control for a class of nonlinear system with actuator faults

In this work, a fault tolerant control scheme is proposed for a class of nonlinear system with actuator faults. In this fault tolerant control strategy, an estimator is designed to estimate both the system states and the fault signal simultaneously. Based on these estimations, the control law is constructed to achieve the fault tolerant control for the nonlinear system considered. It is shown that the estimation error and the system state can be guaranteed to be bounded. The obtained theoretic results have been verified through the simulation examples on the three‐tank system.     

Production scheduling of parallel machines with model predictive control

This paper considers the problem of optimizing on-line the production scheduling of a multiple-line production plant composed of parallel equivalent machines which can operate at different speeds corresponding to different energy demands. The transportation lines may differ in length and the energy required to move the part to be processed along them is suitably considered in the computation of the overall energy consumption. The optimal control actions are recursively computed with Model Predictive Control aiming to limit the total energy consumption and maximize the overall production. Simulation results are reported to witness the potentialities of the approach in different scenarios.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.     

Stabilization of linear systems over networks with bounded packet loss and its use in model predictive control

This paper considers stabilization of discrete-time linear systems, where network exists for transmitting the sensor and controller information, and arbitrary bounded packet loss occurs in the sensor–controller link and the controller–actuator link. The stabilization of this system is transformed into the robust stabilization of a set of systems. The stability result for this system is specially applied on model predictive control (MPC) that explicitly considers the satisfaction of input and state constraints. Two synthesis approaches of MPC are presented, one parameterizing the infinite horizon control moves into a single state feedback law, the other into a free control move followed by the single state feedback law. Two simulation examples are given to illustrate the effectiveness of the proposed techniques.     

Fault‐tolerant reference generation for model predictive control with active diagnosis of elevator jamming faults

This paper focuses on the longitudinal control of an Airbus passenger aircraft in the presence of elevator jamming faults. In particular, in this paper, we address permanent and temporary actuator jamming faults using a novel reconfigurable fault‐tolerant predictive control design. Due to their different consequences on the available control authority and fault duration, the above 2 actuator jamming faults need to be distinguished so that appropriate control reconfigurations can be adopted accordingly. Their similarity in symptoms, however, prevents an effective discrimination of the root cause of the jamming when using only a passive fault‐diagnosis approach. Hence, we propose the use of model predictive control (MPC) as a fault‐tolerant controller to actively help the fault‐detection (FD) unit discriminate between a permanent and a temporary jamming fault, while ensuring the performance of the aircraft. The MPC controller and the FD unit closely interact during the detection and diagnosis phases. In particular, every time a fault is detected, the FD module commands the MPC controller to perform a predefined sequence of reconfigurations to diagnose the root cause of the fault. An artificial reference signal that accounts for changes in the actuator operative ranges is used to guide the system through this sequence of reconfigurations. Our strategy is demonstrated on an Airbus passenger aircraft simulator.     

具有执行器饱和特性的不确定系统预测控制器设计

针对具有执行器饱和特征的不确定系统,提出了一种带有状态观测器的新型预测控制器设计方法.该方法在滚动优化的每一步,采用带有饱和特性的反馈控制结构得到一个最优控制律.使无穷时域性能指标最小.考虑在状态不完全已知的情况下,设计了带有状态观测器的预测控制器,并通过观测器参数调整使闭环系统渐近稳定.通过仿真实验验证了所设计控制器的有效性.     

Simultaneous fault detection and control for switched systems with actuator faults

This paper is concerned with the fault detection and control problem for discrete-time switched systems. The actuator faults, especially ‘outage cases’, are considered. The detector/controller is designed simultaneously such that the closed-loop system switches under an average dwell time, and when a fault is detected, an alarm is generated and then the controller is switched to allow the norm of the states of the subsystem to increase within the acceptable limits. Thus, a switching strategy which combines average dwell time switching with event-driven switching is proposed. Under this switching strategy, the attention is focused on designing the detector/controller such that estimation errors between residual signals and faults are minimised for the fulfillment of fault detection objectives; simultaneously, the closed-loop system becomes asymptotically stable for the fulfillment of control objectives. A two-step procedure is adopted to obtain the solutions through satisfying a set of linear matrix inequalities. An example comprising of three cases is considered. Through these cases, it is demonstrated that the fault detection and control for switched systems using a two-stage switching strategy and asynchronous switching are feasible.     

Stability analysis of a multi-model predictive control algorithm with application to control of chemical reactors

We study a stabilizing multi-model predictive control strategy for controlling nonlinear process at different operating conditions. The control algorithm is a receding horizon scheme with a quasi-infinite horizon objective function that has finite and infinite horizon cost components. The finite horizon cost consists of free input variables that direct the system towards a terminal region which contains the desired operating point. The infinite horizon cost has an upper bound and steers the system to the desired operating point. The system is represented by a sequence of piecewise linear models. Based on the condition of the system states, the sequence of piecewise linear models is updated and the controller’s objective function switches form quasi-infinite to infinite horizon objective function. This results in a hybrid control structure. A recent approach in the analysis of hybrid systems that uses multiple Lyapunov functions is employed in the stability analysis of the closed-loop system. The stabilizing hybrid control strategy is illustrated on two examples and their closed-loop stability properties are studied.