With the increase of users in the Internet of Vehicles (IoV), various heterogeneous user demands are also increasing. The current contradiction in the development of Vehicle Edge Computing (VEC) is how to satisfy all kinds of heterogeneous task requirements in the dynamically changing channel environment. This paper proposes an efficient collaborative scheme for demand-aware terminals, based on spectrum-sharing techniques and Deep Reinforcement Learning (DRL) algorithms, to dynamically satisfy the demands of heterogeneous tasks. Specifically, the delay and energy consumption of two types of tasks are modeled and a multi-objective optimization problem is constructed. Thereafter, we propose a heuristic algorithm to determine the suboptimal solution for optimization variables. Furthermore, we use the DRL algorithm to realize the purpose of dynamically allocating resources according to the channel state. Finally, the scheme's effectiveness is verified through extensive simulation experiments.

Vehicle Edge Computing (VEC) assists vehicles in performing latency-sensitive tasks by deploying resources near the vehicle. Designing an incentive mechanism for vehicles and VEC is crucial for realizing an intelligent transmission system. Considering the rationality of resource allocation, we model the utility functions of the VEC and the vehicle, which are used as optimization objectives. Specifically, the VEC allocates resources through pricing to maximize revenue under resource-constrained conditions, and the vehicle weighs payments against energy consumption to determine offloading and resource allocation. Given the vehicle movement and the variable channel state, we use the Deep Reinforcement Learning (DRL) algorithm to solve these optimization problems. To reduce the learning difficulty of the DRL algorithm in complex VEC scenarios with multiple optimization variables, we propose a Pricing-Driven Resource Allocation (PDRA) algorithm that performs mobility-aware task offloading and calculates the optimal values of the optimization variables in the utility function of the vehicle to reduce the decision dimension. Furthermore, we also propose a DRL-based Pricing-Driven Dynamic Resource Allocation (DPDDRA) algorithm to achieve efficient resource allocation. Extensive experimental results show that the proposed algorithms can reduce the learning difficulty while maximizing VEC and vehicle revenue in complex VEC scenarios.

A lattice-filter structure is used for real-time parameter estimation of a flexible one-arm robot. The estimated parameters are employed in the control calculations based on predictive adaptive output feedback control. The lattice filters are particularly suitable for identifying the parameters of mechanical systems with distributed flexibility, since the order of the filter may be adjusted online as the task conditions require. Hence, the required dynamic model order for an accurate control does not have to be decided upon beforehand. A predictive adaptive control is studied using tip-position measurements. Although there are potential instability problems due to the nonminimum-phase nature of the open-loop system as a result of sensor-actuator noncolocation, the discrete-time predictive adaptive control implemented with the lattice filter (forming a special self-tuning regulator) demonstrated precision, high-bandwidth tip-position control of a flexible one-arm robot.>