Fault-tolerant Model Predictive Control Research Articles

Distributed fault tolerant model predictive control for multi‐unmanned aerial vehicle system

AbstractThis paper is devoted to the cooperative tracking control of multiple unmanned aerial vehicles with unknown faults. Considering the practical case that the unmanned aerial vehicles suffer from strong nonlinearities, external disturbances, actuator, and sensor faults, this brief investigates the distributed adaptive fault tolerant scheme for multi‐unmanned aerial vehicle system within model predictive control framework. Firstly, for preparation to the fault occurrence case, the baseline model predictive control scheme is designed under fault‐free case. Then considering the fault occurrence, a fault detection strategy is proposed by means of the moving horizon estimation and linearly parameterized approximation for actuator and sensor faults, respectively. Thereby with the characterized fault information, the fault tolerant model predictive control scheme is constructed by using an adaptive updating mechanism to compensate for actuator and sensor faults simultaneously. Finally, simulations well demonstrate the effectiveness of proposed control scheme.

Multi-vector model predictive fault-tolerant current control using reconstructed vector distribution for five-phase PM motor

<p indent=0mm>To address a single-phase open-circuit fault, this paper proposes multi-vector finite-control-set model predictive fault-tolerant current control. This method includes reconstruction of the post-fault vector distribution and introduces a vector selection method with a reduced computational burden. The reconstructed vector distribution is based on the principle of minimizing the copper loss, and is generated by combining several basic vectors. Standard PWM waveforms and an enlarged modulation area are considered simultaneously. The vector selection method adopts the basic principle of classic finite-control-set model predictive control twice, which avoids evaluation of all the candidate vectors. The computational burden can thus be reduced without a vector selection error. In addition, to further improve the operational performance, duty cycle modulation technology is adopted to optimize the amplitude of the candidate vectors. A thorough experimental evaluation is performed, which demonstrates the effectiveness and superiority of the proposed fault-tolerant method.

A Robust Fault-Tolerant Predictive Control for Discrete-Time Linear Systems Subject to Sensor and Actuator Faults.

In this paper, a robust fault-tolerant model predictive control (RFTPC) approach is proposed for discrete-time linear systems subject to sensor and actuator faults, disturbances, and input constraints. In this approach, a virtual observer is first considered to improve the observation accuracy as well as reduce fault effects on the system. Then, a real observer is established based on the proposed virtual observer, since the performance of virtual observers is limited due to the presence of unmeasurable information in the system. Based on the estimated information obtained by the observers, a robust fault-tolerant model predictive control is synthesized and used to control discrete-time systems subject to sensor and actuator faults, disturbances, and input constraints. Additionally, an optimized cost function is employed in the RFTPC design to guarantee robust stability as well as the rejection of bounded disturbances for the discrete-time system with sensor and actuator faults. Furthermore, a linear matrix inequality (LMI) approach is used to propose sufficient stability conditions that ensure and guarantee the robust stability of the whole closed-loop system composed of the states and the estimation error of the system dynamics. As a result, the entire control problem is formulated as an LMI problem, and the gains of both observer and robust fault-tolerant model predictive controller are obtained by solving the linear matrix inequalities (LMIs). Finally, the efficiency of the proposed RFTPC controller is tested by simulating a numerical example where the simulation results demonstrate the applicability of the proposed method in dealing with linear systems subject to faults in both actuators and sensors.

Open‐circuit fault‐tolerant operation of permanent magnet synchronous generator drives for wind turbine systems using a computationally efficient model predictive current control

Model predictive fault-tolerant current control (MPFTCC) of permanent magnet synchronous generator (PMSG) drives can make a valuable contribution to improving the reliability and availability levels of wind turbines, because back-to-back (BTB) converters are prone to failure. However, MPFTCC suffers from excessive computational burden, because the BTB converter is treated as one system where all feasible voltage vectors (VVs) are used for prediction and evaluation. Accordingly, a computationally efficient MPFTCC algorithm for a PMSG drive is developed and proposed with the ability to handle insulated-gate bipolar transistor open-circuit faults. The candidate VVs of both machine- and grid-side converters are separately predicted and evaluated, which significantly reduces calculation effort. The proposed reconfigurable converter is a five-leg power converter with a common leg that connects the generator first phase to the grid three-phase, ensuring proper postfault reconfiguration of the grid-side inverter. Moreover, a three-switch rectifier is adopted to achieve fault tolerance of the PMSG-side rectifier. Performance of the considered MPFTCC strategies is evaluated by experimental means.

A Systematic Optimization Design of New Constrained Robust Model Predictive Fault-Tolerant Control for Industrial Processes

A comprehensive optimization design method of new constrained robust model predictive fault-tolerant control is proposed for industrial processes with partial actuator failures and unknown disturbances. A new state space model which is composed of differential state variables, output tracking errors, and new state variables related to output tracking errors is first constructed according to the multiple single-input single-output (SISO) models. Considering the uncertainties existing in the system, the reproduced model is represented by a discrete switched model with polytopic uncertainties. Subsequently, the model predictive controller with min-max optimization is designed to ensure the performance improvement and solve the drawback that the controller gain is not adjustable. To further enhance the system performance, the designed controller is optimized in two steps based on the switching strategy. In addition, the corresponding switching strategy is designed to realize the mutual switching between different models of the system. Finally, taking continuous stirred tank and injection molding processes as examples, the effectiveness of the proposed method is verified by comparing with the traditional control method.

Reconstructed Current Model Predictive Control of NPC Three-Level Grid-Tied Converter With Current Sensor Fault

Neutral-point clamped (NPC) three-level grid-tied converter is the key power electronic equipment connecting renewable energy and power grid. High temperature and other factors can lead to current sensor failure, control strategy failure, output current distortion, and even lead to the disconnection of renewable energy. To maintain the normal operation of NPC three-level grid-tied converter with current sensor fault, a model predictive fault-tolerant control strategy based on space voltage vector set and fault phase current reconstruction is proposed. Firstly, the relationship between fault phase current and voltage vector is studied. After the current sensor fault, only 12 out of 27 voltage vectors were found to be able to reconstruct faulty phase currents with a DC current sensor and an AC current sensor. Model predictive current control using the set of these 12 voltage vectors, and fault phase current is directly calculated by DC current and normal phase current, which improves the fault tolerance and stability of the converter system. Finally, simulation and experimental results verified the effectiveness of the proposed method.

Event‐triggered estimation and model predictive control for linear systems with actuator fault

This study is concerned with the design of an event-triggered fault estimation and fault tolerant model predictive control scheme for linear systems subject to disturbances and actuator faults by using the method of delta operator. First, an observer is established to estimate the state and the fault simultaneously. An H 2 / H ∞ model predictive controller with fault compensation signals is then proposed through solving a constrained optimisation problem. An event-triggered scheme is employed to conserve the network bandwidth. The new design techniques are shown to be effective via simulation results.

Enhanced Fault-Tolerant Model Predictive Current Control for a Five-Phase PM Motor With Continued Modulation

To deal with the single-phase open-circuited fault, an enhanced fault-tolerant model predictive current control with continued modulation is proposed in this article. This method includes reconstructing the postfault vector distribution and adopting a multistep vector selection method. In the reconstruction procedure, the transition vectors are generating to realize standard pulsewidth modulation waveforms. Then, the unreachable area can be filled by creating new synthetic vectors. Finally, the vectors with oversized amplitudes are optimized by using null vectors. In order to cover the full modulation area, a multistep vector selection method is used to determine the phase angle and amplitude of the optimal vector. The phase angle can be determined by defining two cost functions and adopting duty cycle modulation technology. The amplitude of the optimal vector can be optimized by utilizing the duty cycle modulation technology again. The oscillation, due to the back electromotive force, is compensated in each step. A thorough experimental evaluation has been conducted to confirm the effectiveness and superiority of the proposed fault-tolerant method.

Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle.

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.

Fault diagnosis and model predictive fault-tolerant control for stochastic distribution collaborative systems based on the T–S fuzzy model

ABSTRACTThis paper presents a fault-tolerant control scheme for a class of nonlinear stochastic distribution collaborative control systems, which are composed of two nonlinear subsystems connected in series to complete the target. The Takagi–Sugeno (T–S) fuzzy model is applied to approximate the nonlinear dynamics of a subsystem. The output of the whole system is the output probability density function (PDF) of the second subsystem. The fuzzy logic systems (FLS) is used to approximate the output PDF. To diagnose the fault that occurred in the first subsystem, an adaptive diagnostic observer and linear matrix inequality (LMI) technique are used to obtain the adaptive tuning law to estimate the fault. When a fault occurs, the fault itself cannot be compensated in the first subsystem and a model predictive fault-tolerant controller is designed in the second subsystem to compensate the fault, making the post-fault output PDF still track the desired PDF as close as possible. A simulated example is given, and the desired results have been obtained.

Fault‐tolerant predictive trajectory tracking of an air vehicle based on acceleration control

A novel fault-tolerant model predictive control (MPC)-based trajectory tracking approach for an aerial vehicle is presented in this study. A generalised online sequential extreme learning machine is introduced first to identify the corresponding coefficients of actuator faults. Subsequently, a robust trajectory tracking control is developed based on MPC, where the system constraints can be effectively considered in the designed control scheme. Trajectory tracking control is achieved by controlling only the acceleration of the aerial robot in the MPC structure. This leads to less computational burden and faster closed-loop dynamics. In addition, an effective disturbance observer is employed, which can satisfactorily capture the effects of unmodelled dynamics and external disturbances on the system dynamics. The stability of the proposed control algorithm is proved based on the Lyapunov theory under realistic assumptions. Finally, the proposed control strategy is applied to a quadrotor unmanned aerial vehicle. Both simulation and experimental results are presented to demonstrate the effectiveness of the introduced fault-tolerant control scheme. The obtained results suggest that the designed control approach is capable of stabilising the attitude and providing acceptable performance in tracking a predefined trajectory in a three-dimensional environment in the simultaneous presence of actuator faults and external disturbances.

Integrated model predictive fault-tolerant control, and fault detection based on the parity space approach for a reverse osmosis desalination unit

Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.

Dependable power extraction in wind turbines using model predictive fault tolerant control

In this paper, a fault tolerant model predictive control scheme is proposed for wind turbines in the partial-load region to meet the control objectives in the presence of disturbances, uncertainties, sensor and actuator faults. The aim of wind turbine control systems in the partial-load region is to capture maximum power by tracking the optimal generator speed. But any fault in the sensors and actuators can take away the closed-loop system from the main objectives and maybe make the system unstable in some cases. At first, an online model predictive controller (MPC) is designed as a nominal controller to track the maximum power and guarantee all constraints satisfaction without considering any fault. In the next step, an adaptive sliding mode observer (SMO) is designed to estimate the actual states and sensor faults, simultaneously. Finally, an additive control law is represented and shown that it is able to tolerate the actuator faults effectively. Using extensive simulation results it is shown that the proposed strategy is able to handle the uncertainties, sensor and actuator faults in the control system, simultaneously.

Improved H-Infinity Hybrid Model Predictive Fault-Tolerant Control for Time-Delayed Batch Processes Against Disturbances

An H-infinity model predictive fault-tolerant control strategy is proposed for multi-phase batch processes with interval delay and actuator failures. First, state variables, state errors and output tracking errors are introduced to establish an extended-state-space switched system model. Then, based on this model, a predictive fault-tolerant control law is designed for tracking the set point by the output that satisfies the requirements of the optimal performance index under input and output constraints. The feasibility conditions for the solvability of the control law are presented in the form of linear matrix inequalities. In addition, the designed switching law is constructed, and the gain of the control law is obtained via the optimization algorithm. This design has several advantages: the output tracking is faster, the tracking performance is superior, and the trace is smoother at the switching time. Finally, through a comparison with traditional methods, the effectiveness and feasibility of this method are demonstrated via injection molding simulation.

Fault-tolerant model predictive control of a de-manufacturing plant

This paper describes the efficient implementation of a model predictive control (MPC) algorithm for the management of the pallets loaded on the transportation line of a de-manufacturing plant. In order to reduce the computational burden required for the solution of the online optimization problem, and make it compatible with industrial applications, different control and prediction horizons are used. In this way, the complexity of the optimization problem is reduced without significantly affecting the performance of the plant. In addition, a detailed inspection of the transportation line configuration, and the parallelization of the optimization and implementation tasks, allows one to obtain computational times fully comparable to those of simple heuristic rules but with significant improvements in terms of the plant throughput. In the second part of the paper, a fault detection procedure is developed for the identification and isolation of sensors’ and actuators’ faults. Then, the basic MPC algorithm is modified to obtain a control scheme tolerant to instrumentation faults. Both simulation and experimental results are reported and discussed to show the control performance and the practical applicability of the proposed approach.

Fault Diagnosis and Model Predictive Fault Tolerant Control of Networked Control Systems with Random Packet Loss

In this paper, a fault diagnosis and fault tolerant control algorithm is performed for the networked control system with packet loss problem. Firstly, the random packet loss phenomenon of the system is viewed as a binary switching sequence which obeys a Bernoulli distributed white sequence taking on values of zero and one with certain probability. When a failure occurs in the system, fault diagnosis is performed by designing a residual signal and an adaptive adjustment rate for the faulty system, and then based on the fault diagnosis information, model predictive control is used for fault tolerant control for the system. By optimizing the solution of the model predictive control system, a suitable fault-tolerant controller is designed to complete the active fault-tolerant control of the system. Then the closed-loop stability of the system is analyzed. Simulation results are provided to show the effectiveness of the proposed method.

Fault-Tolerant Predictive Control of Six-Phase PMSM Drives Based on Pulsewidth Modulation

Multiphase machines are famous for their fault-tolerant capability. Conventional fault-tolerant model predictive current control (FT-CMPC) of multiphase machines suffers from high computational burden, low switching frequency, and relatively high current harmonics. This paper proposes a fault-tolerant PWM predictive control (FT-PWMPC) to enhance the performance of fault-tolerant predictive control strategy without using complex enumeration method. The reference phase voltages are generated by a postfault mathematical model in stationary reference frame and transformed to duty cycles with a properly set common-mode voltage. Then, a comparative study of fault-tolerant predictive control strategies is presented, including FT-CMPC, FT-two-vector-based-predictive control, and the proposed FT-PWMPC. The control strategies are implemented with the same sampling frequency or switching frequency on a six-phase permanent magnet synchronous machine test bench. Experimental results of dynamic response, steady-state currents, vibration, and parameter sensitivities are given to identify the advantages and limitations of each control strategy.

Distributed fault‐tolerant model predictive control for intermittent faults

Intermittent fault is a special kind of fault form, which often occurs in industry processes but has been less studied. This study focuses on the distributed fault-tolerant model predictive control (DFTMPC) problem for an intermittent fault in actuators or sensors. A novel state observer and DFTMPC controller integral framework are presented. The proposed distributed system state observer can both deal with the unknown inputs and compensate for the interconnected variables' estimation errors, achieving a more accurate estimation. A fault mode-switched model is constructed to describe the system with multi-intermittent faults in a hidden way. This allows us to design a distributed fault-tolerant controller without reconfiguration. For the proposed distributed model predictive controller, asymptotically stable conditions are given, meanwhile, the whole algorithm for multi-intermittent fault-tolerant control is concluded. A numerical example is simulated to verify the proposed method.

Shared control of highly automated vehicles using steer-by-wire systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control (MPC) controller driving the vehicle along an optimal safe path. The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.

Robust constrained model predictive fault-tolerant control for industrial processes with partial actuator failures and interval time-varying delays

This paper studies a robust constrained model predictive fault-tolerant control (RCMPFTC) problem for a class of industrial processes with uncertainties, interval time-varying delay, unknown disturbances and partial actuator failures, in which the main idea is in the relevant theory of RCMPFTC based on a novel extended state space model description of these industrial processes. This extended model includes the state variables and output tracking error variable of process, which actually regulates the dynamic response of the process state and output tracking error separately. Based on this model, the proposed control law is designed, which not only guarantees the convergence and tracking performance of system but also offers more degrees of freedom for designed controller. To ensure robust stability and reject any unknown bounded disturbances for the uncertain system with admissible failures, the optimized cost function and H∞ performance index are thus introduced in the RCMPFTC controller design. By using a differential inequality to construct a differential Lyapunov-Krasovskii function candidate without introducing some redundant free-weighting matrices, the novel, less conservative and more simplified delay-range-dependent stable conditions of the RCMPFTC design are further presented in terms of linear matrix inequality (LMI) constraints. By solving these LMIs, the RCMPFTC law is explicitly formulated, possessing the optimized cost and H∞ performance. Furthermore, the stable condition can also be easily extended from delay-range-dependent to common delay- dependent stability. The comparison results on the liquid level of tank system and multi-input and multi-output glasshouse process show that the proposed control method is effective and feasible for the cases of the constant and random-varying actuator faults.