Tae Hyun Fang

Remote-controlled platoon merging via coder-estimator sequence algorithm for a communication network

In this paper, a platoon-merging control system is considered as a remotely located system with a state represented by a stochastic process. In this system, it is common to encounter situations where a single decision maker controls a large number of subsystems, and observation and control signals are sent over a communication channel with finite capacity and significant transmission delays. Unlike a classical estimation problem where the observation is a continuous process corrupted by additive noise, there is a constraint that the observation must be coded and transmitted over a digital communication channel with finite capacity. A recursive coder-estimator sequence is a state estimation scheme based on observations transmitted with finite communication capacity constraint. In this paper, we introduce a stochastic model for the lead vehicle in a platoon of vehicles in a lane considering the angle between the road surface and a horizontal plane as a stochastic process. In order to merge two platoons, the lead vehicle of the following platoon is controlled by a remote control station. Using the coder-estimator sequence, the remote control station designs the feedback controller. The simulation results show that the inter-vehicle distance and the deviation from the desired inter-vehicle distance are well regulated.

Optimal channel scheduling for remote control of lead vehicle in a platoon

This paper proposes a remote control strategy for vehicles in Intelligent Vehicle Highway System (IVHS). An optimal scheduling of a limited communication channel is proposed for control of each lead vehicle in a set of platoons. The optimal scheduling problem is to find the optimal communication sequence that minimizes the cost which can be obtained inherently by the optimal control without the communication constraint. In this paper, a PID control law which guarantees the string stability is used for the lead vehicle control. It is shown that the PID control law is equivalent to the approximately optimal linear quadratic tracker. Simulations are conducted with five maneuvering platoons to evaluate the optimality of the obtained sequence.

Manoeuvring target tracking using error monitoring and recovery technique of perception net

This work is concerned with the problem for tracking a manoeuvring target. In this paper, an error monitoring and recovery method of perception net is utilized to improve tracking performance for a highly manoeuvring target. Perception net based error monitoring and recovery technique, which is a kind of geometric data fusion, makes it possible to monitor errors and to calibrate possible biases involved in sensed data and extracted features. Both detecting a manoeuvring target and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique. The IMM filter which employing the error monitoring and recovery technique shows good tracking performance for a highly manoeuvring target and it reduces maximum values of estimation errors when manoeuvring starts and finishes.

Detection and diagnosis of sensor and actuator failures using perception net

In a modern engineering system, failures of its actuator or sensor need to be detected and isolated as soon and as accurately as possible. Many researches have been performed for fault detection and diagnosis (FDD) of dynamic systems. In recent researches, it is shown that the interacting multiple model (IMM) algorithm is successfully employed to cope with it. The algorithm proposed in this paper is another approach to deal with FDD using the IMM algorithm, and it is accomplished by using the error monitoring method provided by perception net which was proposed for the effective data fusion in multisensor environment. Simulation results for total actuator failures are demonstrated by Monte-Carlo simulations.

Sensor data fusion using perception net for a precise assembly task

In this paper, work on sensor fusion technology is presented for a peg-in-hole insertion task. Three kinds of sensors are fused for the task execution. They are a vision sensor, a proximity sensor, and a force/torque sensor. The fusion technique is based on the perception net. The perception net, as a structural representation of the sensing capabilities of a system, connects features of various levels of abstraction, referred to here as logical sensors, with their functional relationships such as feature transformations, data fusions, and constraints to be satisfied. The vision and proximity sensors are mainly used for the gross motion control and the force/torque sensor is additionally used for the fine motion control of the robot. The perception net is introduced to exploit the merit of each sensor with putting weight on the sensor. The effectiveness of the proposed sensor data fusion method is validated through simulations.

State estimation for vehicle maneuvering control with stochastic model in communication network

In feedback control systems, if the controller which includes the decision maker is apart remotely from the plant, it is required that the measurement and the control input are transmitted over the communication channel. We investigate a platoon maneuver in the state estimation viewpoint, which is remotely operated by a remote control station with a finite communication capacity channel constraint.

Estimation characteristics of tracking filters according to maneuvering patterns

Three kinds of maneuvering patterns (waver, pop-up, and high-diver maneuvers) are basically considered for a target motion to evaluate the estimation characteristics of the employed tracking filters according to the maneuvering patterns. The employed tracking filters are the interacting multiple model (IMM) filter, which is known as the most cost-effective estimation scheme, and the other filters stem from it. The simulation results show well the estimation characteristics of the employed filters according to the given maneuvering patterns of a target.

Perception-net-based maneuvering target tracking

The IMM estimator is known as a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The value of hybrid models for tracking algorithm is that the occurrence of target maneuvers can be explicitly included in the kinematic equations through regime jumps. However, the model probabilities of IMM filter trend to be slowly adapted from the non-maneuver mode to maneuver mode or from maneuver to non-maneuver, although sudden maneuver can take place in the true system. This is why the model probability is dependent on the past model probabilities. In order to track a suddenly and highly maneuvering target, the technique which combines IMM filter with error monitoring and recovery technique of perception nit is proposed in this paper. The perception net, as a structural representation of the sensing capabilities of system, is formed by the interconnection of logical senor with three types of modules: feature transformation module, data fusion module (DFM), and constraint satisfaction module. Observing the output and input of DFM, we can detect an error of input data. Error monitoring and recovery technique based on this function makes it possible to detect and identity errors, and to calibrate possible biases involved in sensed data and extracted features. Both detecting maneuver and compensating the estimated state can be achieved by employing the properly implemented error monitoring and recovery technique reduces the maximum values of estimation errors when maneuvering starts and finishes, and shows higher tracking performance especially for a highly maneuvering target. Its effectiveness is demonstrated through Monte-Carlo simulations.