Abstract: In this paper, a sampled-data model predictive tracking
control method is presented for mobile robots which is modeled as
constrained continuous-time linear parameter varying (LPV) systems.
The presented sampled-data predictive controller is designed by linear
matrix inequality approach. Based on the input delay approach, a
controller design condition is derived by constructing a new Lyapunov
function. Finally, a numerical example is given to demonstrate the
effectiveness of the presented method.
Abstract: This paper proposes improved delay-dependent stability conditions of the linear time-delay systems of neutral type. The proposed methods employ a suitable Lyapunov-Krasovskii’s functional and a new form of the augmented system. New delay-dependent stability criteria for the systems are established in terms of Linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Numerical examples showed that the proposed method is effective and can provide less conservative results.
Abstract: We present a system that finds road boundaries and
constructs the virtual lane based on fusion data from a laser and a
monocular sensor, and detects forward vehicle position even in no lane
markers or bad environmental conditions. When the road environment
is dark or a lot of vehicles are parked on the both sides of the road, it is
difficult to detect lane and road boundary. For this reason we use
fusion of laser and vision sensor to extract road boundary to acquire
three dimensional data. We use parabolic road model to calculate road
boundaries which is based on vehicle and sensors state parameters and
construct virtual lane. And then we distinguish vehicle position in each
lane.