Yaobin Chen

Implementation of evolutionary fuzzy systems

Evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.

A rule-based energy management strategy for Plug-in Hybrid Electric Vehicle (PHEV)

Hybrid Electric Vehicles (HEV) combine the power from an electric motor with that from an internal combustion engine to propel the vehicle. The HEV electric motor is typically powered by a battery pack through power electronics. The HEV battery is recharged either by the engine or from regenerative braking. The electric drive mode is very limited for an HEV due to the limited battery power. A more powerful battery will increase the electric drive range of the vehicle, thus improving fuel economy. However, the battery will need to be recharged using an electric outlet since the regenerative braking and limited engine usage will not be sufficient to fully recharge the larger battery pack. In this paper, a rule-based energy management strategy for a Plug-in Hybrid Electric Vehicle (PHEV) is presented. Since large amount of electric energy is stored in the battery from the electric power grid, the fuel consumption is reduced significantly as compared with HEV counterpart. The proposed energy management strategy is implemented on a PHEV model in ADVISOR and the model is then simulated for a number of predefined drive cycles. The proposed PHEV algorithm results are compared with those for HEV with similar battery capacity as PHEV.

Structure of minimum-time control law for robotic manipulators with constrained paths

The authors address the problem of the structure of minimum-time control (MTC) of robotic manipulators along a specified geometric path subject to hard control constraints. By using the extended Pontryagin minimum principle (EPMP) and a set of parameterized robot dynamic equations, it is shown that the structure of the minimum-time control law requires that one and only one control torque is always in saturation on every finite time interval along its time-optimal trajectory, while the rest of the torques are adjusted so that the path constraint on the motion is not violated. This is in contrast to the point-to-point minimum-time control law, which requires that at least one of the control torques is always in saturation. Simulation results are presented to verify the structure of the MTC law.<<ETX>>

A proof of the structure of the minimum-time control law of robotic manipulators using a Hamiltonian formulation

A Hamiltonian canonical formulation that yields a new and straightforward proof of the structure of the minimum-time control (MTC) law for m-link robotic manipulators is used. It is shown that the structure of the MTC law requires that at least one of the actuators always be in saturation. A numerical algorithm is presented. The algorithm converts the original problem, possibly a partially singular one, into a totally nonsingular optimal control problem by introducing a perturbed energy term in the performance index. It is shown that the solution to the perturbed problem converges to that of the MTC problem in the sense of the performance index as the perturbation parameter approaches zero. The control algorithm is used in a simulation to verify the MTC law structure. >

Control of hybrid dynamical systems for electric vehicles

This paper presents a system controller for hybrid electric vehicles using hybrid dynamical system theory. The hierarchical control structure of parallel hybrid electric vehicle systems is first introduced and the drive control problem of the vehicle is formulated using the hybrid dynamical system theory. The controller design problem and the optimal control of the vehicle system is investigated and an optimal coordination strategy is developed based on the minimization of the system cost subject to system constraints, where the optimal solution is obtained by solving a sequential quadratic programming problem. The simulation is conducted with a comparison study between the optimal control strategy and a rule-based control strategy.

An analysis of Bare Bones Particle Swarm

The bare bones particle swarm (BBPS) is evolved from the canonical particle swarm optimizer (PSO). The velocity term of the canonical PSO is removed in BBPS and replaced by Gaussian sampling strategy. There is no parameter tuning and it is much easier to implement. In the paper, it is proven that the BBPS can be mathematically deduced from the canonical PSO and a more general formula of BBPS is also presented. The results presented in the paper represent initial results of an ongoing research project effort.

An Extreme Learning Machine-based pedestrian detection method

Pedestrian detection is a challenging task due to the high variance of pedestrians and fast changing background, especially for a single in-car camera system. Traditional HOG+SVM methods have two challenges: (1) false positives and (2) processing speed. In this paper, a new pedestrian detection method using multimodal HOG for pedestrian feature extraction and kernel based Extreme Learning Machine (ELM) for classification is presented. The experimental results using our naturalistic driving dataset show that the proposed method outperforms the traditional HOG+SVM method in both recognition accuracy and processing speed.

Studying the Effects of Driver Distraction and Traffic Density on the Probability of Crash and Near-Crash Events in Naturalistic Driving Environment

Driver distraction detection and intervention are important for designing modern driver-assistance systems and for improving safety. The main research question of this paper is to investigate how the cumulative driver off-road glance duration can be controlled to reduce the probability of occurrences of crash and near-crash events. Based on the available data sets from the Virginia Tech Transportation Institute (VTTI) 100-car study, the conditional probability is calculated to study the chance of crash and near-crash events when the given cumulative off-road glance duration in 6 s has been reached. Different off-road eye-glance locations and traffic density levels are also evaluated. The results show that one linear relationship can be obtained between the cumulative off-road eye-glance duration in 6 s and the risk of occurrences of crash and near-crash events, which varies for different off-road eye-glance locations. In addition, the traffic density level is found to be one significant moderator to this linear relationship. Detailed comparisons are made for different traffic density levels, and one nonlinear equation is obtained to predict the probability of occurrences of crash and near-crash events by considering both cumulative off-road glance duration and traffic density levels.

A new computational approach to solving a class of optimal control problems

A class of optimal control problems in which the control signals are bounded and appear linearly in the hamiltonian function occur in many applications. In the solutions to these problems, the fact that optimal control trajectories may contain discontinuities and possibly singular arcs/sub-arcs makes the problems extremely difficult to solve, if not impossible. This paper presents a novel and efficient computational method for solving such problems. The method consists of two steps. In the first step, the original optimal control problem with possible discontinuities and singular arcs in control is converted into one with continuous and non-singular control trajectories by adding to the performance indexes a perturbed (or weighted) energy term. The resultant boundary value problem can easily be solved for an appropriately large value of the perturbation parameter. In the second step, a continuation method (imbedding method, or homotopy method) is developed to obtain the solution to the original problem by...

General Structure of Time-Optimal Control of Robotic Manipulators Moving Along Prescribed Paths

This paper addresses the structure of time-optimal control of robotic manipulators along a specified geometric path subject to constraints on control torques Both regular and singular (where one or more effective inertia components are zero on any finite time interval) cases are studied by using the Extended Pontryagins Minimum Principle (EPMP) and a parameterization method. It is shown that the structure of the time-optimal control law requires either (a) one and only one control torque be always in saturation in every finite time interval along its optimal trajectory, while the rest of them adjust thier values so that the motion of the robot is guaranteed along the constrained path, or (b) at least one of the actuators takes on its extremal values. The first form of the control law dominates the robot motion along the optimal trajectory though the second form may exist. The theoretical results are verified by various existing numerical examples.