Most satellite attitude control models are built analytically, which requires a clear understanding of the kinematic equations of the satellites and the various disturbance models to deal with the interferences and uncertainties in the space environment. This paper studies a system identification method to build a system model for satellite attitude control. The satellite model is based on a neural network model, i.e., the RBF-ARX model, which requires only observation data. Unlike the existing off-line identification methods for the RBF-ARX model, this paper proposes an on-line identification method that can constantly correct the model according to the newest data. To reduce the computational complexity of identifying the satellite model, a decomposition scheme is also developed. The computational efficiency of the proposed method is analyzed. Finally, the efficacy of the proposed identification method is demonstrated by applying it to two satellite attitude control systems.

With its natural ability in handling constraints, model predictive control (MPC) has become an established control technology in the petrochemical industry, and its use is currently being pioneered in an increasingly wide range of process industries. It is also being proposed for a range of higher bandwidth applications, such as ships, aerospace and road vehicles. To extend its applications to miniaturized devices and/or embedded systems, this paper explores the implementation of the MPC technology into reconfigurable hardware such as a FPGA chip. A rapid prototyping environment suitable for exploring the various implementation issues to bring MPC onto a chip is described. Tests were conducted to verify the applicability of the "MPC on a chip" idea. It is shown that a modest FPGA chip could be used to implement a reasonably sized constrained MPC controller

To further the penetration level of renewable energy sources (RESs) in power systems, the paper integrates wind turbines into conventional load frequency control (LFC). A resilient model predictive control (MPC) framework is constructed in the context of potential false data injection (FDI) attacks on vulnerable communication networks of multi-area power systems. To reduce the power generation cost, an economic cost function for MPC is firstly formulated. Then, a decentralized-model-based $\chi^{2}$ detection unit is presented to distinguish the attacked measurements sent from neighbors. Moreover, to reduce the computation burden of executing the distributed MPC strategy, an intensified event-triggered scheme that can handle incomplete and inaccurate modeling issues is proposed. Validation results illustrate the efficacy of the detection unit and the intensified event-triggered scheme, and conclude the relationships between alarming thresholds and key performance indicators. Note to Practitioners —This paper explores the applicability of LFC with the integration of wind turbines under economic MPC framework. Motivated by the underlying FDI attacks on vulnerable communication networks among different control areas, an intrusion detection unit is proposed to install at each controller side to realize resiliency enhancement. Different from the existing works, this paper meticulously investigates the relationships between alarming thresholds and key performance indicators (KPIs), aiming at providing some valuable references for power operators and managers. Besides, this paper proposes an intensified event-triggered scheme to relieve the computation burden of MPC algorithm. This intensified event-triggered scheme has two advantages. One is that it considers the historic released signals in event-triggered conditions, which makes sure the critical signals at crests or troughs of frequency dynamic curves can be triggered. The other advantage is that the supplementary event-trigged condition can well tolerant the incomplete and inaccurate modeling problems existing in conventional event-triggered conditions. Simulations verify the efficacy and feasibility of the resilient event-triggered MPC strategy for frequency regulation under FDI attacks.

Model predictive control (MPC) is an optimisation-based scheme that imposes a real-time constraint on computing the solution of a quadratic programming (QP) problem. The implementation of MPC in fast embedded systems presents new technological challenges. In this paper we present a parameterised field-programmable gate array implementation of a customised QP solver for optimal control of linear processes with constraints, which can achieve substantial acceleration over a general purpose microprocessor, especially as the size of the optimisation problem grows. The focus is on exploiting the structure and accelerating the computational bottleneck in a primal-dual interior-point method. We then introduce a new MPC formulation that can take advantage of the novel computational opportunities, in the form of parallel computational channels, offered by the proposed pipelined architecture to improve performance even further. This highlights the importance of the interaction between the control theory and digital system design communities for the success of MPC in fast embedded systems.