Model Predictive Control for Neuromimetic Quantized Systems
Based on our recent research on neural heuristic quantization systems, we propose an emulation problem consistent with the neuromimetic paradigm. This optimal quantization problem can be solved with model predictive control (MPC) by deriving the conditions under which the quantized system can guarantee (asymptotic) stability during emulation by optimizing a Lyapunov-like objective function. The neuromimetic model features large numbers of discrete inputs, and the optimization involves integer variables. The approach in the paper begins by solving an optimization using model predictive control (MPC) and then using a neural network to train the data generated in this process and applying Fincke and Pohst's sphere decoding algorithm to narrow down the search for the optimal solution.
Code (0)
등록된 구현이 없습니다.
Tasks
modelModel Predictive ControlQuantizationSimilar Papers 제목 키워드 기반
Emulation Learning for Neuromimetic Systems
Building on our recent research on neural heuristic quantization systems, results on learning quantized motions and resilience to channel dropouts are reported. We propose a general emulation problem consistent with the …
Model Predictive ControlQuantizationTransfer LearningNeuromimetic Linear Systems -- Resilience and Learning
Building on our recent work on {\em neuromimetic control theory}, new results on resilience and neuro-inspired quantization are reported. The term neuromimetic refers to the models having features that are characteristic…
Combinatorial OptimizationQ-LearningQuantizationNeuromimetic Dynamic Networks with Hebbian Learning
Leveraging recent advances in neuroscience and control theory, this paper presents a neuromimetic network model with dynamic symmetric connections governed by Hebbian learning rules. Formal analysis grounded in graph the…
Brain in the Dark: Design Principles for Neuromimetic Inference under the Free Energy Principle
Deep learning has revolutionised artificial intelligence (AI) by enabling automatic feature extraction and function approximation from raw data. However, it faces challenges such as a lack of out-of-distribution generali…
QSID-MPC: Model Predictive Control with System Identification from Quantized Data
Least-square system identification is widely used for data-driven model-predictive control (MPC) of unknown or partially known systems. This letter investigates how the system identification and subsequent MPC is affecte…
Model Predictive ControlQuantization