Unmanned ground vehicles operating in complex environments must adaptively adjust to modeling uncertainties and external disturbances to perform tasks such as wall following and obstacle avoidance. This paper introduces an adaptive control approach based on spiking neural networks for wall fitting and tracking, which learns and adapts to unforeseen disturbances. We propose real-time wall-fitting algorithms to model unknown wall shapes and generate corresponding trajectories for the vehicle to follow. A discretized linear quadratic regulator is developed to provide a baseline control signal based on an ideal vehicle model. Point matching algorithms then identify the nearest matching point on the trajectory to generate feedforward control inputs. Finally, an adaptive spiking neural network controller, which adjusts its connection weights online based on error signals, is integrated with the aforementioned control algorithms. Numerical simulations demonstrate that this adaptive control framework outperforms the traditional linear quadratic regulator in tracking complex trajectories and following irregular walls, even in the presence of partial actuator failures and state estimation errors.

The existing solutions related to local differential privacy (LDP) in multi-layer networks for edge computing scenarios present several limitations in both key-value data heavy hitter identification and related frequency and mean estimation tasks. First, existing LDP approaches cannot effectively use edge nodes to improve their utility/performance. Secondly, there are many network transmission tasks in edge computing, which have relatively high requirements for communication and storage costs. Furthermore, the traditional privacy budget allocation cannot attain the best utilization. To solve the above problems, we propose MLPKV, a local differential multi-layer private key-value data collection scheme for edge computing, structured into three phases: dimensional reduction, padding-length estimation, and estimation. An improved EC-OLH algorithm is used to offload the computing efforts related to aggregation and estimation to edge nodes for achieving greater efficiency. In the dimensional reduction phase, a candidate set is generated to prune the domain of original data, which improves the estimation. In addition, our method groups users for completing the tasks in each phase to avoid additional errors caused by dividing the privacy budget, and proposes a new user division with an optimal grouping ratio. Finally, the proposed method was implemented in a proof-of-concept prototype system. We compare MLPKV with baseline methods such as PrivKV and PCKV. Experimental results on both synthetic and real-world datasets show that our method achieves better utility for heavy hitter identification, frequency, and mean estimations than other state-of-the-art mechanisms. For small data sets, our approach also provides high-accuracy estimation with a low privacy budget.