Md. Abdus Samad Kamal

Ecological Vehicle Control on Roads With Up-Down Slopes

This paper presents a novel development of an ecological (eco) driving system for running a vehicle on roads with up-down slopes. Fuel consumed in a vehicle is greatly influenced by road gradients, aside from its velocity and acceleration characteristics. Therefore, optimum control inputs can only be computed through anticipated rigorous reasoning using information concerning road terrain, model of the vehicle dynamics, and fuel consumption characteristics. In this development, a nonlinear model predictive control method with a fast optimization algorithm is implemented to derive the vehicle control inputs based on road gradient conditions obtained from digital road maps. The fuel consumption model of a typical vehicle is formulated using engine efficiency characteristics and used in the objective function to ensure fuel economy driving. The proposed eco-driving system is simulated on a typical road with various shapes of up-down slopes. Simulation results reveal the ability of the eco-driving system in significantly reducing fuel consumption of a vehicle. The fuel saving behavior is graphically illustrated, compared, and analyzed to focus on the significance of this development.

Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy

Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.

On board eco-driving system for varying road-traffic environments using model predictive control

This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.

Optimal PID controller tuning of automatic gantry crane using PSO algorithm

In this paper, a novel method for tuning PID controller of automatic gantry crane control using particle swarm optimization (PSO) is proposed. PSO is one of the most recent optimization techniques based on evolutionary algorithm. PSO is also known as computationally efficient method. This work presents in detail how to apply PSO method in finding the optimal PID gains of gantry crane system in the fashion of min-max optimization. The simulation results show that with proper tuning a satisfactory PID control performance can be achieved to drive nonlinear plant. The controller is able to effectively move the trolley of the crane in short time while canceling the swing angle of the payload hanging on the trolley at the end position. The robustness of the controller is also tested.

Objective function selection of GA-based PID control optimization for automatic gantry crane

Gantry cranes are widely used in various applications to transfer a payload from one position to desired position. Gantry crane system is an underactuated system where the number of inputs is less than the number of outputs. When the input signal is given to the actuator, the trolley starts to accelerate whilst causing a swing of payload hanging on a flexible cable. Many researchers have proposed anti-swing controls of gantry crane using PID+PD structure where PID controller is used for trolley positioning control and PD controller is used to dampen the swing oscillation. This is because of the simplicity of PID control structure. Some have combined intelligent methods such as fuzzy and neural networks to improve the performance of the proposed PID control structure. This paper discusses GA-based PID+PD controller tuning for automatic gantry crane system. The discussion is emphasized on the selection of the objective function since the objective function is the key to use the GA (genetic algorithm). The optimized PID gains would be mainly due to the appropriate objective/fitness function. The simulation results show that a good anti-swing control performance can be obtained from the proposed objective function.

Coordination of automated vehicles at a traffic-lightless intersection

This paper presents a coordination scheme of automated vehicles at an intersection without using any traffic lights under a connected vehicles environment. Vehicles approaching the intersection from all sections are globally coordinated, by considering their states all together in a model predictive control framework, for their safe and rapid crossing. The optimal trajectories of the vehicles are computed based on avoidance of the cross-collision risks around the intersection. Compared with the other methods, the scheme enhances safety by generating the trajectories of vehicles far from each other with respect to the cross-collision points and enables turning movements under constrained velocity without using any auxiliary lanes. The proposed coordination scheme is evaluated through numerical simulation for a simple test intersection consisting of four single-lane approaches. Observations under different traffic flow conditions reveal that the proposed scheme significantly improves performance compared with the traditional traffic-light-based intersection control.

Ecological driving based on preceding vehicle prediction using MPC

Abstract This paper presents an ecological (eco) driving system based on prediction of the preceding vehicle using model predictive control. At any measured road-traffic states it computes the optimal vehicle control input using the models of the vehicle dynamics and fuel consumption. The prediction model the preceding vehicle is formulated based on experimentally obtained driving data. The proposed system is evaluated for driving on urban roads containing traffic control signals at the intersections using the microscopic transport simulator AIMSUN. Significant improvement in fuel efficiency by introducing the model of the preceding vehicle has been confirmed from the simulation results.

Reinforcement learning for problems with symmetrical restricted states

A reinforcement learning method is proposed that can utilize parts of states and their partial symmetries to solve a problem efficiently. In most cases the action selection does not need considering all the states but only needs looking at parts of states or restricted state of corresponding action. Moreover, restricted states of different actions are symmetrical, and thus the action value function based on restricted states can be shared which further reduces the reinforcement learning problem size. The method is compared, in terms of simulation results and other aspects, with other standard reinforcement learning methods.

Driver-Adaptive Assist System for Avoiding Abnormality in Driving

Sometimes a driver deviates from his natural or normal driving style due to inadequate attention or faces abnormal situation caused by a number of psychological and physical factors. Such abnormalities often lead a driver to a mistake that may cause an accident. This paper presents a novel approach called driver-adaptive assist system to avoid such abnormalities in driving scenario as a preventive measure against occurrence of vehicle collisions, assuming that natural driving style of individual drivers is the safest style. Adaptive fuzzy system with statistics of recent fluctuations records are used to determine the driving behavior from noisy data. Another fuzzy reasoning section determines the level of abnormality in driving to notify or warn the driver so that he can pay back his full concentration in driving. Different simulated drivers with pseudo realistic styles in starting, stopping and car following are used to investigate performance of the proposed system. Empirical results show the ability of the system to recognize abnormality of drivers having different driving styles.

Traffic Signal Control of a Road Network Using MILP in the MPC Framework

This paper investigates the significance of a traffic signal control scheme that simultaneously adjusts all signal parameters, i.e., cycle time, split time and offset, in a road network. A novel framework of model predictive control (MPC) is designed that overcomes the limitations of other MPC based traffic signal control strategies, which are mostly restricted to control only split or green time in a fixed cycle ignoring signal offset. A simple macroscopic model of traffic tailored to MPC is formulated that describes traffic dynamics in the network at a short sampling interval. The proposed framework is demonstrated using a small road network with dynamically changing traffic flows. The parameters of the proposed model are calibrated by using data obtained from detailed microscopic simulation that yields realistic statistics. The model is transformed into a mixed logical dynamical system that is suitable to a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The framework makes the signals flexibly turn to red and green by adapting quickly to any changes in traffic conditions. Results are also verified by microscopic traffic simulation and compared with other signal control schemes.