The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm.

Main steam temperature system has the characteristics of nonlinear, large inertia and time delay. Thus, traditional PID cascade control usually could not provide satisfied performance. As a most widely used model predictive control (MPC) algorithm, dynamic matrix control (DMC) is especially suitable for large inertia controlled object. However, DMC is vulnerable to be influenced by uncontrollable disturbances. In this paper, a new DMC control strategy based on the combination of cascade structure and feedforward compensation is proposed in main steam temperature control system. The disturbances of the load and the spray attemperator inlet temperature can be compensated by feedforward model. And the cascade structure can suppress the disturbances of spray attemperator water. Simulation results show that the novel feedforward DMC-PID cascade control strategy has remarkable dynamic performance and strong disturbance rejection ability.

Day-ahead dispatch of integrated energy systems (IESs) is commonly solved by mixed-integer linear programming when objectives, constraints, and forecasts are fully specified. In practice, however, operators often express temporary preferences in natural language, and day-ahead forecasts inevitably deviate from realized operation. These two conditions make a fixed optimization interface difficult to use without additional modeling effort. This paper proposes DARC (Decompose-Act-Repair-Critique), a constraint-diagnostic LLM agent framework for day-ahead dispatch of campus-level microgrid-type IES with under-specified operating requirements. DARC combines three language modules, namely a Decomposer for temporal structure, a Resolver for numerical schedule generation, and a Critic for root-cause diagnosis, with deterministic repair, constraint checking, and metric evaluation. In all main experiments, the Decomposer is instantiated by its deterministic rule-based variant for reproducibility, so the reported results reflect a loop with two LLM modules (Resolver and Critic). The Projector repairs candidate schedules where possible, while the Checker supplies mathematical facts that ground the Critic’s feedback. DARC does not model forecast uncertainty through sets or scenarios; robustness to forecast deviation is pursued operationally, through margin-aware repair and checker-grounded revision, and is assessed empirically. Experiments on a campus-level microgrid IES testbed show that DARC can incorporate natural-language preferences, improve realized feasibility under forecast noise relative to both a point-forecast MILP and an interval-robust MILP baseline in terms of strictly feasible configurations, at higher operating cost, and produce diagnostic feedback that is robustly more useful than an ungrounded LLM diagnoser under judge models from three families, and at least as useful as checker facts alone, with its advantage concentrated in actionable revision guidance. DARC is therefore positioned not as a replacement for MILP, robust formulations, or model predictive control on fully specified problems, but as a complementary interface for operational settings where preferences and forecasts are not completely formalized.