xk 1 Gaug xk Hauguk
yk Caug xk Dauguk
finder。 The ultrasonic sensors are not so accurate in the distance reading, because the measurement can be affected by reflectivity of the different surfaces and the environmental
Aaug and Baug are the state and input transition matrices, Caug and Daug are the output and direct (feedthrough) transition matrices, respectively。 The subscript “aug” stands for augmented equation。 the system input (uk) is the velocity setpoint (vsp)。The distance obtained by the ultrasonic sensor
(d) is used as an output measurement of the system to estimate the unmeasured states of the system (af and vl) utilising the Kalman filter。 The Kalman filter gain matrix is calculated through a recursive algorithm and using the state- space model of the overall system given by (6)-(8)。 The calculated gain matrix by a recursive algorithm is Kk+1=[0。0630 -0。068 0。3452 0。2559]T。 Consequently, the acceleration (af) and relative velocity (vr) can be obtained having the estimated state ( xˆk 1|k 1 ) as:
factors and the gain depends on the location of the obstacle with respect to the sensor。 In order to mitigate the sudden variation of the distance which in turn degrades the controlling process, a Median Filter was used within the program algorithm。 Fig。 7 shows the results obtained by using two robots。 The longitudinal motion of the leading robot was defined arbitrary in the tests。 The results of the real-implementation indicate fairly good performance of the ACC system。 Despite the disturbances imposed by the distance measurement as well as the estimation error of the unmeasured parameters, the distance tracking was accomplished at an acceptable level (RMSE from the distance tracking was 0。0325 (m))。 The parameters estimated by a Kalman filter including the inter-distance, the velocity of the leading and following robots and the acceleration are
a 1 0 0
0xˆ
k 1|k 1
(9)
depicted in Fig。 7。 Here, the acceleration indicates a smooth performance of the robot during tracking。 The comparison
v 0 1 0
1xˆ
k 1|k 1
(10)
between the parameters measured by sensors, i。e。 the distance and velocity of the following robot, with the same parameters
where
xˆk 1|k is the estimated state by the Kalman filter at
estimated by a Kalman filter (Fig。7 a-b) demonstrates that
time k+1 based on the information given at time k。
they have been estimated by small errors, which are
acceptable。 The RMSEs of the estimation for the distance and
velocity of the following robot are 0。0085 (m) and 0。0163 (m), respectively。
Fig。 6。 Distance tracking (ACC) using Fuzzy PID controller:
(a)inter-distance between the robots using test robot, (b) inter-distance between the robots obtained from simulation, and (c) velocities obtained during distance tracking。
6。IMPLEMENTATION INTO CIRRICULUM AND RESUTLS
The material described in this paper was introduced to the MSc level students in embedded system course。 The costs of the implementation into our courses include the purchase of ten DANI robotic vehicles。
Students were given a set of formal lectures and tutorials, covering real-time embedded system environment and programming Compact RIO using LabVIEW。 Most of the students have participated in the control systems module or have a control background。 The course was assessed 100% by coursework。 The design and implementation of automated speed-dependant ECU function for safe distance-keeping of an electric vehicle accounted for 50% of the total marks。 Here we assessed the following learning objectives: 机器人运动模糊逻辑控制英文文献和中文翻译(5):http://www.youerw.com/fanyi/lunwen_100015.html