Zhao Dongbin

Adaptive optimal control for the uncertain driving habit problem in adaptive cruise control system

In this paper, a novel adaptive optimal control approach based on Q-function is proposed to address the problem as the driving habits change among drivers and over time in the adaptive cruise control system. The proposed approach, adopting the special structure Q-function of the linear discrete-time system, uses policy iteration method to derive the optimal control policy online. It repeats between policy evaluation where the polynomial neural network is employed to approximate the cost function of the system and policy improvement where the control policy is updated based on the converged neural network, until the optimal controller is achieved. Simulation is conducted and results show the effectiveness for uncertain driving habit problem in the adaptive cruise control system.

Robot Planning with Ant Colony Optimization Algorithms

Ant colony optimization algorithms are investigated in this paper for robot planning in configuration space. The robot planning problem is to find a feasible path from a beginning to a goal while avoiding obstacles in a clustered environment. Lots of attentions have been paid on such problems, but little is with the ant colony optimization algorithms. Originated from the max-min ant system (MMAS) algorithm for traveling salesman problem, a modified ant colony optimization algorithm for robot planning is proposed. The algorithm has some distinguished features, such as a path pruning mechanism, etc. The optimal solution can be achieved effectively in different environments with a high probability.