Model predictive control (MPC) is subject to lim-itations, including stringent model accuracy requirements and time-consuming modeling, which impede its broader application. To address these challenges, this article proposes a predic-tive controller based on Data-Driven Recursive Subspace Least Squares (DD-RSPC), obviating the need for any prior model identification. Unlike traditional MPC, DD-RSPC dynamically updates the prediction model using real-time measured input-output data, thereby significantly enhancing system adaptability. Furthermore, this article introduces a rigorous stability theorem that employs the Lyapunov function to ensure the stability of the DD-RSPC approach. Finally, the effectiveness of DD-RSPC is validated through aeroengine simulation experiments, effectively substantiating its adaptability, stability, and practical applicability.

In this paper, we propose a switched model predictive control scheme based on the data-driven method without any terminal ingredients. Compared with conventional model-based model predictive control schemes, we utilize a series of input-output data to describe the system dynamics according to the behavioral systems theory. In order to improve system performance, multiple cost functions regulated by a suitable switching strategy are considered. For the nominal case with noise-free data, we prove that the proposed data-driven switched MPC schemes can ensure the exponential stability of the system provided that the switching signal satisfies the average dwell-time condition. Moreover, the robust stability is also analysed mathematically for the robust case considering bounded measurement noise. The effectiveness of the control algorithm is verified by the speed control of a commercial high bypass turbofan engine on a hardware-in-loop platform. The results of the experiment show the advantages of the proposed switched model predictive control scheme over conventional non-switched model predictive control schemes.Note to Practitioners—In this paper, we consider the problem of designing a control law to improve the performance of the system with multiple requirements that need a tradeoff. Since the performance criteria can easily be formulated in the cost function in model predictive control algorithm, it is a natural thought to design multiple cost functions which can be switched according to different performance requirements. However, the modelling process of the system is usually complicated and time-consuming for conventional model predictive control. In this paper, the data-driven method replaces the counterpart of the model in conventional model predictive control. The input-output measurements of the system can be directly used to forecast the dynamics of the system. The feasibility and stability of the control scheme are analyzed mathematically. We show that the proposed control law can ensure the above closed-loop performance of the controlled system, provided that the parameters of the controller are suitable and the switch of the cost function is sufficiently slow. The effectiveness of the proposed control scheme is demonstrated by the application of a high bypass ratio turbofan engine on a hardware-in-loop experiment platform. The results show that the turbofan engine can achieve a relatively rapid response with smaller overshoot.

Model predictive control (MPC) is popular in applications with slow dynamics because of its advantages in handling constraints and multivariable optimization. But for aeroengines, it is difficult to obtain an exact prediction model, which will lead to offsets in tracking. Besides, deploying MPC to embedded controllers for real-time control is a well-known challenge. Therefore, this paper presents a switched linear MPC, which incorporates the augmented prediction models with error integrator for offset-free tracking, the sparse-based quadratic programming formula for solving MPC, and a reset strategy for achieving bumpless transfer at the switching instant. Further, on the hardware board we developed, six hardware-related acceleration strategies are explored and evaluated for real-time performance. Then, eight cases of five objects are tested, whose results indicate a significant speedup of around 50 times. At last, the hardware-in-the-loop tests of the turbofan engine and the real bench tests of the micro-turbojet engine are performed, which verifies the superiority, real-time performance, and potential for practical applications.Note to Practitioners—This paper was motivated by the problem of applying the offset-free MPC to the embedded control system for aeroengines. The difficulty of accurate modelling and the requirement to compute the embedded MPC in a limited time are two major challenges. Accordingly, we explore six hardware acceleration strategies that are often ignored to ensure the real-time performance. Moreover, we investigate the switched MPC to obtain the desired dynamic response. However, the controller switching tends to induce fluctuations that are harmful to the engine. Hence, we present a reset strategy to ensure bumpless transfer performance. Preliminary physical experiments suggest that the proposed approaches are feasible to achieve the predetermined objectives. Further, the comparisons with the previous methods highlight our superiority. Overall, this paper contributes to optimize the computational performance of microcontrollers and enhance bumpless transfer performance for MPC. In our future work, the proposed approaches will be applied to flight experiments and more industrial devices.

Efficient fault-tolerant control (FTC) is necessary for the safe operation of aero-engine control system. In this paper, a high performance active FTC method based on zonotope for actuator fault in aero-engine control systems is proposed. Parameter uncertainties are considered to describe linearization error and identification error of system model for reducing the gap between theory and practice. Firstly, a zonotopic observer satisfying the peak-bounded index is proposed to reduce the influence of uncertainties and improve the accuracy of fault estimation. Moreover, with the aid of the zonotopic observer, the range of the sliding surface affected by the estimation errors and model uncertainties can be evaluated, and the dynamic quasi-sliding mode domain (QSMD) can be obtained. As a result, the dynamic QSMD can help design the parameters of the sliding mode fault-tolerant controller, ensure the stability and convergence of the entire closed-loop control system. Meanwhile, the conservative problem caused by manual parameters setting is avoided. Finally, the feasibility of the proposed method is verified by the aero-engine Hardware-in-the-loop (HIL) experiment platform.

Since the output signal of the controller is prone to jump suddenly when a switching occurs, the practical system usually suffers from undesired bumps. To address this “bump” phenomenon, a parameters-free bumpless transfer structure based on multiple differentiators and a cointegrator is proposed in this article. The proposed structure is applicable for switching between manual and automatic modes, as well as switching during steady state and transient state. Unlike the previous compensator-based BT methods, our structure exploits the continuous property of the integrator, which prompts optimal performance and preserves original performance. Furthermore, sufficient conditions based on the dynamic dwell time are given to guarantee the stability of switched linear systems. At last, the designed BT approach is applied to a bivariate turbofan engine and a micro-turbojet engine, wherein the hardware-in-the-loop tests and real bench tests are performed, respectively, so as to demonstrate the significant improvement and potential for practical engineering.