Learning to Stabilize High-dimensional Unknown Systems Using Lyapunov-guided Exploration

Designing stabilizing controllers is a fundamental challenge in autonomous systems, particularly for high-dimensional, nonlinear systems that cannot be accurately modeled using differential equations. Lyapunov theory offers a robust solution for stabilizing control systems. Still, current methods relying on Lyapunov functions require access to complete dynamics or samples of system executions throughout the entire state space. Consequently, they are impractical for high-dimensional systems. In this paper, we introduce a novel framework, LYGE, for learning stabilizing controllers specifically tailored to high-dimensional, unknown systems. LYGE employs Lyapunov theory to iteratively guide the search for samples during exploration while simultaneously learning the local system dynamics, control policy, and Lyapunov functions. We demonstrate its scalability on highly complex systems, including a high-fidelity F-16 jet model from the Air Force featuring a 16D state space and a 4D input space. Experimental results indicate that, compared to prior works in reinforcement learning, imitation learning, and neural certificates, LYGE reduces the distance to the goal by 50% while requiring only 5% to 32% of the samples. Furthermore, we demonstrate that our algorithm can be extended to learn controllers guided by alternative certificate functions for unknown systems.Comment: 32 pages, 7 figure

Experimental study on a new FCC spent catalyst distributor

In the regenerator of an industrial fluid catalytic cracker (FCC), uniform distribution of its solids reactant, i.e. spent catalyst, plays a crucial role in obtaining better regenerator performance. In traditional FCC unit designs, there was usually no spent catalyst distributor or some intuitive designs with simple structures, i.e. boat or pipe distributors in most China’s FCC units (1). In this study, we built a large cold experimental installation to evaluate the performances of various spent catalyst distributors. Distribution uniformity and solids flow resistance were the main target indices for distributor performance evaluation. The experimental results indicate that the boat distributor has the poorest performance, as solids flows preferentially through the few front openings. At high gas flowrates, the pipe distributor can obtain a relative uniform solids distribution, but its flow resistance is also higher. Good flowability of solids that is difficult to maintain throughout the distributor was found to be the root cause of their bad distribution performance. Referring to the idea of an air-slide solids transportation system (2-4), a new slot spent catalyst distributor was proposed. Its performance was systematically evaluated in a large cold model unit. It was found that the new slot distributor has a critical superficial gas velocity, beyond which good solids distribution uniformity and high solids transportation capacity can be both maintained. Compared with traditional boat and pipe spent catalyst distributors, the new slot distributor is much more advantageous comprehensively, e.g. in solids distribution uniformity, solids transportation capacity and operating flexibility. Please click Additional Files below to see the full abstract

State-Compute Replication: Parallelizing High-Speed Stateful Packet Processing

With the slowdown of Moore's law, CPU-oriented packet processing in software will be significantly outpaced by emerging line speeds of network interface cards (NICs). Single-core packet-processing throughput has saturated. We consider the problem of high-speed packet processing with multiple CPU cores. The key challenge is state--memory that multiple packets must read and update. The prevailing method to scale throughput with multiple cores involves state sharding, processing all packets that update the same state, i.e., flow, at the same core. However, given the heavy-tailed nature of realistic flow size distributions, this method will be untenable in the near future, since total throughput is severely limited by single core performance. This paper introduces state-compute replication, a principle to scale the throughput of a single stateful flow across multiple cores using replication. Our design leverages a packet history sequencer running on a NIC or top-of-the-rack switch to enable multiple cores to update state without explicit synchronization. Our experiments with realistic data center and wide-area Internet traces shows that state-compute replication can scale total packet-processing throughput linearly with cores, deterministically and independent of flow size distributions, across a range of realistic packet-processing programs

Integrated energy system optimization and scheduling method considering the source and load coordinated scheduling of thermal-storage electric boilers and electric vehicles

The northern regions of China face the challenges of the mismatch of the power supply and demand, as well as serious wind curtailment issues, caused mainly by the limitation of the “with heat to determine electricity” mode for combined heat and power generation during the winter season. To further absorb the surplus wind power and alleviate restrictions, a comprehensive energy system optimization method for parks based on coordinated scheduling between sources and loads is proposed in this paper. First, the implementation of a heat-storage electric boiler on the source side further achieves the decoupling of heat and power. Second, an optimized scheduling method for electric vehicles combining incentive scheduling and orderly scheduling is proposed on the load side, which helps flatten the load curve. Finally, a tiered carbon trading mechanism is introduced and a community integrated energy system (CIES) optimization scheduling model is established with the aim of minimizing the total cost of the CIES, and the problem is solved using the CPLEX commercial solver. The simulation results indicate that the overall system efficiency is significantly improved through the coordinated scheduling of power sources and loads. Specifically, the integration rate of wind power increases by 3.91% when compared to the sole consideration of the integrated demand response. Furthermore, the peak shaving and off-peak filling effect is considerably enhanced compared to the utilization of only thermal-storage electric boilers. Additionally, the implementation of coordinated scheduling leads to a reduction in the total system cost by 2764.32 yuan and a decrease in total carbon emissions by 3515.4 kg. These findings provide compelling evidence that the coordinated scheduling of power sources and loads surpasses the limitations of thermal power units, strengthens the demand response capability of electric vehicles, and enhances the economic benefits of the CIES

PRPD data analysis with Auto-Encoder Network

Gas Insulated Switchgear (GIS) is related to the stable operation of power equipment. The traditional partial discharge pattern recognition method relies on expert experience to carry out feature engineering design artificial features, which has strong subjectivity and large blindness. To address the problem, we introduce an encoding-decoding network to reconstruct the input data and then treat the encoded network output as a partial discharge signal feature. The adaptive feature mining ability of the Auto-Encoder Network is effectively utilized, and the traditional classifier is connected to realize the effective combination of the deep learning method and the traditional machine learning method. The results show that the features extracted based on this method have better recognition than artificial features, which can effectively improve the recognition accuracy of partial discharge

Influence of the Porosity of the TiO 2

The structure of mesoporous TiO2 (mp-TiO2) films is crucial to the performance of mesoporous perovskite solar cells (PSCs). In this study, we fabricated highly porous mp-TiO2 films by doping polystyrene (PS) spheres in TiO2 paste. The composition of the perovskite films was effectively improved by modifying the mass fraction of the PS spheres in the TiO2 paste. Due to the high porosity of the mp-TiO2 film, PbI2 and CH3NH3I could sufficiently infiltrate into the network of the mp-TiO2 film, which ensured a more complete transformation to CH3NH3PbI3. The surface morphology of the mp-TiO2 film and the photoelectric performance of the perovskite solar cells were investigated. The results showed that an increase in the porosity of the mp-TiO2 film resulted in an improvement in the performance of the PSCs. The best device with the optimized mass fraction of 1.0 wt% PS in TiO2 paste exhibited an efficiency of 12.69%, which is 25% higher than the efficiency of the PSCs without PS spheres