Partial Actuator Faults Research Articles

Adaptive fault‐tolerant control of a nonlinear 2‐DOF helicopter system with prescribed performance

AbstractThis study investigates a novel tracking control with prescribed performance for an uncertain nonlinear 2‐DOF helicopter subject to actuator failure. The control design aims to address partial actuator faults, and to mitigate their effects, an adaptive auxiliary variable is introduced. Additionally, a neural network is employed to accurately approximate the system uncertainties. The prescribed performance is formulated as time‐varying constraints, and the barrier Lyapunov function (BLF) technique is applied to achieve the constrained control. Finally, the stability analysis of the closed‐loop system is provided, and the validity of the proposed control scheme is illustrated through a simulation study on the 2‐DOF helicopter system.

Robust predictive minmax fault‐tolerant control for multi‐phase batch processes under partial actuator faults and asynchronous switching

AbstractIn view of the uncertainties, partial actuator faults, asynchronous switching as well as unknown external interference in multi‐phase batch processes, a robust predictive minmax fault‐tolerant control approach is presented. First, considering that a controller and a system state cannot switch synchronously during switching, a match‐and‐mismatch switching model is constructed. Then, an output tracking error is introduced to establish an equivalent extended switching model. In this extended model, the robust control issue is transformed into the minmax optimization issue by establishing the minmax performance index. On this basis, the control laws with control input and interference input are designed. Second, sufficient conditions are given to assure the system separately has asymptotic and exponential stabilities in each phase and batch. By calculating the given sufficient conditions online, the gains of the control laws, and the minimum and maximum operating time under match and mismatch cases are obtained. With the maximum operating time, the controller receives the switching signal beforehand to eliminate the mismatch case of switching. Additionally, a robust criterion is supplied to confirm the system's robustness. Finally, a simulation study of an injection molding process shows that the approach is valid and feasible.

Finite time fault estimation and fault‐tolerant control for a class of uncertain nonlinear systems based on cascaded observers

AbstractIn this paper, a finite time fault estimation and fault‐tolerant control method is proposed for a class of uncertain nonlinear systems that have both actuator faults and sensor faults. First, the outputs of the original system pass through the low‐pass filters to obtain the augmented states, and then an augmented system is composed of the states of the low‐pass filters and the original system, so as to realize the conversion of the sensor faults of the original system into partial actuator faults of the augmented system. Next, design state observers to reconstruct the unknown states of the augmented system, and develop extended state observers to estimate the actuator faults and disturbance of the augmented system. Again, the fault‐tolerant controllers are completed based on the backstepping control and the finite time control, and the finite time convergences of the designed observers and controllers are proved by theoretical analyses. Finally, based on a permanent magnet synchronous motor (PMSM) speed system, the simulation research and dSPACE simulated experiment research are conducted to verify the effectiveness of the proposed control method and its feasibility in the actual application.

Iterative learning hybrid robust predictive fault-tolerant control for nonlinear batch processes with partial actuator faults

For nonlinear batch processes with uncertainties, disturbances and partial actuator faults, an iterative learning robust predictive fault-tolerant control approach is developed. Based on the conclusion of robustness analysis, the stability conditions combining the robust positive invariant set with the terminal constraint set can guarantee that the system is stable under faults. Furthermore, the real-time optimal gains of control law solved online can significantly reduce the learning cycles of the controller, which in turn can meet the demand for rapid production of batch processes. The injection process is employed as an example to ultimately confirm the viability and efficacy of the developed approach.

Nonlinear Fault-Tolerant Vibration Control for Partial Actuator Fault of a Flexible Arm

This paper presents a nonlinear fault-tolerant vibration control system for a flexible arm, considering partial actuator fault. A lightweight flexible arm with lower stiffness will inevitably cause vibration which will impair the performance of the high-precision control system. Therefore, an operator-based robust nonlinear vibration control system is integrated by a double-sided interactive controller actuated by the Shape Memory Alloy (SMA) actuators for the flexible arm. Furthermore, to improve the safety and reliability of the safety-critical application, fault-tolerant dynamics for partial actuator fault are considered as an essential part of the proposed control system. The experimental cases are set to the partial actuator as faulty conditions, and the proposed vibration control scheme has fault-tolerant dynamics which can still effectively stabilize the vibration displacement. The reconfigurable controller improves the fault-tolerant performance by shortening the vibration time and reducing the vibration displacement of the flexible arm. In addition, compared with a PD controller, the proposed nonlinear vibration control has better performance than the traditional controller. The experimental results show that the effectiveness of the proposed method is confirmed. That is, the safety and reliability of the proposed fault-tolerant vibration control are verified even if in the presence of an actuator fault.

Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems

Undetected partial actuator faults on multi-rotor UAVs can lead to system failures and uncontrolled crashes, necessitating the development of accurate and efficient fault detection and isolation (FDI) strategy. This paper proposes a hybrid FDI model for a quadrotor UAV that integrates an extreme learning neuro-fuzzy algorithm with a model-based extended Kalman filter (EKF). Three FDI models using Fuzzy-ELM, R-EL-ANFIS, and EL-ANFIS are compared based on training, validation performances, and sensitivity to weaker and shorter actuator faults. They are also tested online for linear and nonlinear incipient faults by measuring their isolation time delays and accuracies. The results show that the Fuzzy-ELM FDI model exhibits greater efficiency and sensitivity, while Fuzzy-ELM and R-EL-ANFIS FDI models demonstrate better performance than a conventional neuro-fuzzy algorithm, ANFIS.

Resilient anti-disturbance dynamic surface control of uncertain strict-feedback systems subject to partial loss of actuator effectiveness

This paper is concerned with the tracking control of a class of uncertain strict-feedback systems subject to partial loss of actuator effectiveness, in addition to uncertain model dynamics and unknown disturbances. A resilient anti-disturbance dynamic surface control method is proposed to achieve stable tracking regardless of partial actuator faults. First, data-driven adaptive extended state observers are designed based on memory-based identifiers, such that the uncertain model dynamics, external disturbances and the unknown input gains due to actuator faults can be estimated. Next, a resilient anti-disturbance dynamic surface controller is developed based on recovered information from the data-driven adaptive extended state observers. After that, it is proven that the cascade system formed by the observer and controller is input-to-state stable. Finally, comparative studies are performed to validate the efficacy of the resilient anti-disturbance dynamic surface control method for nonlinear strict-feedback systems subject to partial loss of actuator effectiveness.

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

The main aim of this paper is to propose a robust fault-tolerant control for a three degree of freedom (DOF) mechanical crane by using a convex quasi-Linear Parameter Varying (qLPV) approach for modeling the crane and a passive fault-tolerant scheme. The control objective is to minimize the load oscillations while the desired path is tracked. The convex qLPV model is obtained by considering the nonlinear sector approach, which can represent exactly the nonlinear system under the bounded nonlinear terms. To improve the system safety, tolerance to partial actuator faults is considered. Performance requirements of the tracking control system are specified in an H∞ criteria that guarantees robustness against measurement noise, and partial faults. As a result, a set of Linear Matrix Inequalities is derived to compute the controller gains. Numerical experiments on a realistic 3 DOF crane model confirm the applicability of the control scheme.

Distributed vibration control for flexible spacecraft with distributed disturbance and actuator fault

Distributed and boundary control for flexible spacecraft with distributed and boundary disturbances and actuator fault was investigated in this study. The flexible spacecraft analyzed here consists of a centrebody and two symmetric flexible appendages, which can be modeled by a set of partial differential equations (PDEs). Distributed disturbances are considered, distributed infinite disturbance observers are designed to estimate the disturbances in the system, which are proven exponentially stable. Distributed controllers are designed to compensate for the distributed disturbances. A boundary controller imported on the centrebody is designed to suppress the vibration of the flexible spacecraft. Adaptive control laws are established to ensure stable system operation in the presence of partial actuator fault. The Lyapunov function consists of the energy of the system and the stability is analyzed. Finally, numerical simulations are used to validate the system control effectiveness of the proposed method.

Distributed active fault tolerant control design against actuator faults for multiple mobile robots

This paper investigates the active fault tolerant cooperative control problem for a team of wheeled mobile robots whose actuators are subjected to partial or severe faults during the team mission. The cooperative robots network only requires the interaction between local neighbors over the undirected graph and does not assume the existence of leaders in the network. We assume that the communication exists all the time during the mission. To avoid the system’s deterioration in the event of a fault, a set of extended Kalman filters (EKFs) are employed to monitor the actuators’ behavior for each robot. Then, based on the online information given by the EKFs, a reconfigurable sliding mode control is proposed to take an appropriate action to accommodate that fault. In this research study, two types of faults are considered. The first type is a partial actuator fault in which the faulty actuator responds to a partial of its control input, but still has the capability to continue the mission when the control law is reconfigured. In addition, the controllers of the remaining healthy robots are reconfigured simultaneously to move within the same capability of the faulty one. The second type is a severe actuator fault in which the faulty actuator is subjected to a large loss of its control input, and that lead the exclusion of that faulty robot from the team formation. Consequently, the remaining healthy robots update their reference trajectories and form a new formation shape to achieve the rest of the team mission.

Design of the Linear Quadratic Structure Based Predictive Functional Control for Industrial Processes Against Partial Actuator Failures Using GA Optimization

This paper addresses the genetic algorithm (GA) optimization and the linear quadratic (LQ) structure based predictive functional control (PFC) for batch processes under non-repetitive unknown disturbances and partial actuator faults. First, by adopting the extended non-minimal state space (ENMSS) model in which the state variables and the tracking error are united, the new state vector with more degrees is provided for the controller design. In order to enhance the ensemble control performance under the PFC structure, GA is adopted for the optimization of the weighting matrix in the controller. The case study on the injection velocity control in an injection molding machine demonstrates the effectiveness of the proposed PFC scheme against various disadvantages.

Improved Robust Constrained Model Predictive Control Design for Industrial Processes Under Partial Actuator Faults

Focusing on industrial processes under uncertainties and partial actuator faults, a new robust constrained model predictive control (MPC) strategy is developed. To enhance the corresponding control performance, a new state-space model in which an extended state vector is constructed by combining the state variables and the tracking error is introduced for the proposed MPC algorithm. As a consequence, there are extra degrees of freedom for the subsequent controller design by adjusting the output tracking error and the state variables separately, and the enhanced control performance is anticipated. Note that the state variables cannot be tuned in the robust MPC design that utilizes the traditional state space model so that its control performance may be limited because of the restricted degrees of freedom. Finally, the validity of the proposed robust MPC strategy is evaluated on the injection velocity control under uncertainties and partial actuator failures.

Genetic-Algorithm-Optimization-Based Infinite Horizon Linear Quadratic Control for Injection Molding Batch Processes with Uncertainty

A new genetic-algorithm (GA)-based infinite horizon linear quadratic (IHLQ) control method is developed for injection molding batch processes under partial actuator faults and unknown disturbances....

Improved state space model predictive fault-tolerant control for injection molding batch processes with partial actuator faults using GA optimization

A novel model predictive fault-tolerant control (MPFTC) strategy adopting genetic algorithm (GA) is proposed for batch processes under the case of disturbances and partial actuator faults. Based on the extended state space model in which the tracking error is contained, there are more degrees of freedom provided for the controller design and better control performance is obtained. In order to enhance the control performance further, the GA is introduced to optimize the relevant weighting matrices in the cost function. The effectiveness of the proposed MPFTC approach is tested on the injection velocity regulation of the injection molding process.

New Minmax Linear Quadratic Fault-Tolerant Tracking Control for Batch Processes

A new design of linear quadratic control (LQ) is proposed for batch processes with partial actuator faults and unknown disturbance. The autoregressive model built through input-output process data is transformed into a new non-minimal state space (NMSS) model in which the process state variables and tracking error are combined, then a new LQ control scheme is derived based on minimax optimization. Unlike LQ based on traditional NMSS model (TNMSSLQ), the process state variables and tracking error can both be regulated in the proposed method. Monotonic situations of optimal performance cost are further derived to yield sufficient conditions for both nominal and robust system stability. Finally, an injection velocity control example is illustrated to show the effectiveness.

Bumpless switching control for switched systems with partial actuator failures

ABSTRACTThis study is concerned with the bumpless transfer problem for switched systems with partial actuator failures, in order to obtain smooth system performance output transition. Taking into account that the system requires a controller switching from current sub-controller to a fault-tolerant sub-controller after actuator fault. And bumpless transfer for control input cannot be traditionally designed when the actuator fault occurs, while performance smoothing can be considered and it is actually the ultimate goal of bumpless transfer. Specifically, the actuator fault model is firstly established and partial actuator fault is considered. Then, the system performance output signal is deemed as the main design variable of bumpless transfer, and closed-loop control systems both previous and after controller switching are constructed. Moreover, by using model matching thought and the adaptive sliding mode control technique, a bumpless transfer compensator design strategy is given to drive the performance output variable (after controller switching) to track the one of reference model. At last, simulation results of numeric and application examples demonstrate the effectiveness of the proposed bumpless transfer strategy.

Design of passive fault-tolerant controllers of a quadrotor based on sliding mode theory

Abstract In this paper, sliding mode control is used to develop two passive fault tolerant controllers for an AscTec Pelican UAV quadrotor. In the first approach, a regular sliding mode controller (SMC) augmented with an integrator uses the robustness property of variable structure control to tolerate partial actuator faults. The second approach is a cascaded sliding mode controller with an inner and outer SMC loops. In this configuration, faults are tolerated in the fast inner loop controlling the velocity system. Tuning the controllers to find the optimal values of the sliding mode controller gains is made using the ecological systems algorithm (ESA), a biologically inspired stochastic search algorithm based on the natural equilibrium of animal species. The controllers are tested using SIMULINK in the presence of two different types of actuator faults, partial loss of motor power affecting all the motors at once, and partial loss of motor speed. Results of the quadrotor following a continuous path demonstrated the effectiveness of the controllers, which are able to tolerate a significant number of actuator faults despite the lack of hardware redundancy in the quadrotor system. Tuning the controller using a faulty system improves further its ability to afford more severe faults. Simulation results show that passive schemes reserve their important role in fault tolerant control and are complementary to active techniques

A linear quadratic structure based predictive functional control design for industrial processes against partial actuator failures

An improved predictive functional control (PFC) scheme is proposed for linear processes to compensate for partial actuator failures. First, a new state space model based on the process state and output error variables is derived to provide more information of the system. The PFC is then designed through a linear quadratic form that makes the controller optimal. Thus, the control performance can be improved under partial actuator faults and unknown disturbances. Finally, an injection molding process is adopted to illustrate the feasibility and effectiveness of the proposed scheme. Simulation results show that satisfactory control performance can be obtained by comparing with traditional PFC.

Design of state space linear quadratic tracking control using GA optimization for batch processes with partial actuator failure

This paper presents a new state space model design of linear quadratic (LQ) tracking control scheme using genetic algorithm (GA) optimization for batch processes under partial actuator faults. To develop the LQ control, the process model is first transformed into a new state space formulation that includes both the process state and the output tracking error dynamics. Then the subsequent LQ control is formulated. As analytical tuning rules of the elements in the weighting matrices are not known, a GA is introduced to optimize these elements to achieve the desired closed-loop process responses. Simulation on several processes demonstrates that the proposed LQ scheme obtains the design objective well with improved control performance.

GA based predictive functional control for batch processes under actuator faults

In this article, an improved predictive functional control (PFC) based on genetic algorithm (GA) optimization is proposed for batch processes with partial actuator faults and unknown disturbances. The design consists of two steps. Firstly, based on an expanded state space process model, a new PFC is designed that can regulate both the process state and output tracking error dynamics. Secondly, as knowledge of analytical weighting factors on the process state and output tracking error is not known, a GA is introduced to optimize the weighting factors to achieve the desired closed-loop responses. The performance of the proposed PFC is illustrated through an injection molding process, where injection velocity control is tested and results show that the proposed PFC improves closed-loop performance compared with existing methods.