Robust predictive fault-tolerant control based on signal compensation for nonlinear industrial processes with partial actuator failures

The main contribution of the paper was to propose a robust predictive fault-tolerant control scheme for a class of non-linear discrete-time systems that can be described with the LPV models using neural networks. Indeed, the contribution can be divided into a few important points: extension of the efficient predictive control to the robust case with exogenous external disturbances acting on the system, development of robust fault estimation and compensation scheme, and an integration of the developed schemes within a unified robust predictive fault-tolerant control framework. The proposed approach was applied to the benchmark example of the multi-tank system. The achieved results show the performance of the high performance of the proposed approach. In spite of the incontestable appeal of the proposed approach there are still some points, which may further improve its effectiveness. Indeed, in the proposed approach it is assumed the the state is available and, hence a natural approach is to relax this assumption by the introduction of a suitable state estimation strategy.

To guarantee the control performance of the discrete system under unmeasurable state and partial actuator failure, a robust dynamic output feedback predictive fault-tolerant control method derived from switching strategy is developed. A new iterative error switching model is established, which both ensures tracking performance and lays the foundation for the development of the next switching controller. Based on this model, output feedback switching fault-tolerant controller is developed. Compared with conventional fault-tolerant control methods, the designed controller can be used under the case of unmeasurable states, and it is more fault tolerant and less conservative. According to the Lyapunov stability theory, the robust stability analysis is carried out under normal and fault conditions, respectively. Then, depending on the findings of the stability analysis, sufficient conditions to guarantee the stability of the closed-loop normal and faulty system are derived. Finally, the suggested method's efficacy is confirmed using the injection moulding process as an example.

A systematic and complete method to design robust predictive controllers for unconstrained linear systems is proposed. The synthesis procedure is based on rigorous theoretical foundations, without resorting to approximations or ad hoc design guidelines, yet it remains a viable tool for practical application. A significant feature is that the robust predictive controller retains the servo performance of a nominal predictive controller designed using conventional methods. In addition, the robust predictive controller can be designed to guarantee perfect steady‐state rejection of asymptotically constant disturbances. The robust design method is developed for systems affected by unmodeled dynamics and is based on solving a discrete‐time model‐matching problem. It is shown that the robust controller can be classified legitimately as a predictive controller because it minimizes the same performance functional as the nominal predictive controller. An illustrative design example is given.

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov–Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov–Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

Multiple model adaptive estimation (MMAE) with filter spawning is used to detect and estimate partial actuator failures on the Variable-Stability In-flight Simulator Test Aircraft (VISTA) F-16. The truth model is a full six-degree-of-freedom simulation provided by Calspan and General Dynamics. The design models are chosen as 13-state linearized models, including first order actuator models. Actuator failures are incorporated into the truth model and design model assuming a "failure to free stream". Filter spawning is used to include additional filters with partial actuator failure hypotheses into the MMAE bank. The spawned filters are based on varying degrees of partial failures (in terms of effectiveness) associated with the complete-actuator-failure hypothesis with the highest conditional probability of correctness at the current time. Thus, a blended estimate of the failure effectiveness is found using the filters' estimates based upon a no-failure hypothesis, a complete actuator failure hypothesis, and the spawned filters' partial-failure hypotheses. This yields substantial precision in effectiveness estimation, compared to what is possible without spawning additional filters, making partial failure adaptation a viable methodology in a manner heretofore unachieved.

Active Fault Tolerant Control for a Class of Networked Systems with Partial Actuator Failures

A torque ripple minimising and robust predictive torque control of a doubly fed induction generator (DFIG) is proposed. The proposed method is based on a novel cost function and a robust predictive model. The aim of the proposed cost function is torque ripple reduction beside the torque and flux control. This method predicts the torque and the flux those will be resulted by the feasible voltage vectors for the next step. The best option is chosen and applied based on the proposed cost function. Two expressions are added to the conventional cost function for the torque ripple reduction. These terms minimise the effective value of the torque ripple and the variation of the torque while the main control is not affected. Furthermore, a novel closed‐loop prediction model for DFIG is proposed in order to improve the robustness of the method. Simulation results for the conventional finite control set‐model predictive control and the proposed methods are presented and compared in order to confirm the effectiveness of this method.

For the tracking control problem of a class of complex nonlinear controlled processes with disturbances and unknown uncertainties, a compensation signal-driven nonlinear generalized predictive control method is proposed by combining the generalized predictive control and signal compensation techniques. The controlled object is represented by a low-order linear model and an unknown nonlinear term, which represents uncertainties such as system modeling errors and disturbances. A low-order linear model is used to design the generalized predictive controller. The tracking error affected by the unknown nonlinear term can be obtained from the generalized predictive control closed-loop system. A compensation signal is designed by introducing the one-step ahead optimal control to minimize the tracking error and the fluctuation of the control input, thereby eliminating the effect of the unknown nonlinear term on the controlled object and improving the dynamic performance of the system. The proposed method relaxes the previous condition that the unknown nonlinear term is globally bounded to the Lipschitz condition, which proves the stability and convergence of the closed-loop system. To further improve the dynamic performance of the system, an optimization method based on gradient descent for the weighting parameters of the controller is proposed. The effectiveness of the proposed method is verified by simulation comparison experiments.

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.

Aiming at the trajectory tracking control problem caused by the coupling of strong nonlinearity, parameter uncertainty and unknown disturbances in rigid robotic arms, this paper proposes an adaptive robust model predictive control (APRMPC) scheme. This study aims to fill the gap in the existing literature by proposing a dedicated control framework capable of simultaneously and effectively handling parameter uncertainty, unmodeled dynamics, and external disturbances, while ensuring constraint satisfaction. Firstly, a dynamic model of a three-degree-of-freedom robotic arm was established based on the Lagrange equation; secondly, this paper designs a deep integration mechanism of adaptive law and robust predictive control: by designing a parameter adaptive algorithm to estimate the system uncertainty online and feedforward compensate it to the predictive model, the impact of model mismatch is significantly reduced; meanwhile, for the estimated residuals and unknown disturbances, feedback gain was introduced and the control input was designed based on the robust invariant set theory, achieving unified parameter identification, disturbance suppression and rolling optimization within a single framework. This paper strictly proves the feasibility and stability of the control scheme. Finally, the simulation experiments based on MATLAB show that, compared with the traditional MPC and PID methods, the APRMPC algorithm can achieve higher accuracy and stronger robustness in trajectory tracking under various working conditions, effectively resolving the inherent contradiction between the weak robustness of the traditional MPC and the large buffering of sliding mode control, and verifying the value of the proposed scheme in filling the gap in related literature.

This paper proposes a robust predictive current control with total disturbance observer for a synchronous motor drive. The traditional predictive current control is sensitive to system parameters, because its predictive model is based on the precise system description. However the parameter variations in electric motors are inevitable due to ambient temperature and magnetic saturation effect. To solve this problem, a total disturbance is adopted to cover the disturbances due to coupling term, parameter mismatch. Thus an extended linear disturbance observer is used to estimate the state currents and disturbances, which is then combined with the predictive current control to obtain a robust predictive current control. Some simulations and experimental tests have been conducted to verify the performance of the proposed method.

Decentralized adaptively weighted stacked autoencoder-based incipient fault detection for nonlinear industrial processes

In this paper we present a new state space model based linear quadratic fault-tolerant tracking control scheme for batch processes with unknown disturbances and partial actuator faults. To develop the linear quadratic fault-tolerant control, the batch process is first treated with a new state space representation that incorporates both the state and the output tracking error dynamics. Then relevant concepts of the subsequent linear quadratic control are formulated, where improved closed-loop control performance is achieved even with unknown disturbances and actuator faults compared with traditional linear quadratic control. Application to injection velocity control shows that the proposed scheme achieves the design objective well with performance improvement.

This article proposes some new improvements to the traditional constrained predictive fault-tolerant control (FTC) method by enlarging the maximal fault-tolerant admissible region and reducing the online computational complexity. In general, the generalized parameterization of predictive input and event-triggered predictive regulation are integrated into a framework of fault compensation-based dual-mode tube predictive FTC. This predictive FTC scheme has three notable characteristics: 1) The dual-mode FTC consisting of fixed-mode fault compensation and freely predictive adjustments allows most of the calculations to be predetermined offline, while only a simple optimization problem requires to be solved online; 2) the fault-tolerant ability is enhanced by utilizing generalized parameterization of predictive inputs to enlarge the region of attraction of a faulty system; 3) the real-time computational effort of predictive FTC is further reduced by constructing an event-triggered condition to avoid unnecessary online predictive optimization. A case study of a practical synchronous dc-dc Buck converter is given to validate the effectiveness of the proposed predictive FTC method.

In this paper, robust constrained model predictive control of uncertain fuzzy affine systems is considered. Based on piecewise quadratic Lyapunov functions, highly efficient robust constrained predictive control approaches are developed so that the closed-loop stability is guaranteed, and the transient control performance is improved even under input or state constraints. Moreover, by using approximate ellipsoid and S -procedure, the solution to the fuzzy affine model based predictive control can be cast as a convex optimization problem subject to some linear matrix inequalities, rather than a nonconvex problem via bilinear matrix inequalities as in conventional fuzzy affine model-based control. The proposed controllers are thus easier for real-time implementation in industry. Simulation results on the fluidized bed combustion plant have demonstrated the performance of the proposed approaches.