Payman Shakouri

Adaptive cruise control with stop&go function using the state-dependent nonlinear model predictive control approach.

In the design of adaptive cruise control (ACC) system two separate control loops - an outer loop to maintain the safe distance from the vehicle traveling in front and an inner loop to control the brake pedal and throttle opening position - are commonly used. In this paper a different approach is proposed in which a single control loop is utilized. The objective of the distance tracking is incorporated into the single nonlinear model predictive control (NMPC) by extending the original linear time invariant (LTI) models obtained by linearizing the nonlinear dynamic model of the vehicle. This is achieved by introducing the additional states corresponding to the relative distance between leading and following vehicles, and also the velocity of the leading vehicle. Control of the brake and throttle position is implemented by taking the state-dependent approach. The model demonstrates to be more effective in tracking the speed and distance by eliminating the necessity of switching between the two controllers. It also offers smooth variation in brake and throttle controlling signal which subsequently results in a more uniform acceleration of the vehicle. The results of proposed method are compared with other ACC systems using two separate control loops. Furthermore, an ACC simulation results using a stop&go scenario are shown, demonstrating a better fulfillment of the design requirements.

Adaptive Cruise Control System: Comparing Gain-Scheduling PI and LQ Controllers

Abstract Over the recent years, a considerable growth in the number of vehicles on the road has been observed. This increases importance of vehicle safety and minimization of fuel consumption, subsequently prompting manufacturers to equip cars, with more advanced features such as adaptive cruise control (ACC)or collision avoidance and collision warning system (CWS). This paper investigates two control applications design namely the gain scheduling proportional-integral (GSPI) control and gain scheduling Linear Quadratic (GSLQ)control for ACC, covering a high range speed. The control system consist of two loops in cascade, with the inner loop controlling the vehicle speed and the outer loop switching between the cruise control (CC) and the ACC mode and calculating the reference speed. A nonlinear dynamic model of the vehicle is constructed and then a set of operating points is determined and then a of linear models is extracted in operating point. For each operating point, PI and LQ controllers are obtained off-line. An integrated Simulink model including the nonlinear dynamic vehicle model and the ACC controller (either PI or LQ) was used to test the controllers in various traffic scenarios. Comparison results between the two controllers applications is provided to show the validity of the design.

Simulation Validation of Three Nonlinear Model-Based Controllers in the Adaptive Cruise Control System

In this paper, the simulation validation of the hierarchical two-loop Adaptive Cruise Control (ACC) system is considered as a candidate for the application in the Advanced Driver Assistance Systems (ADAS), which aims to ensure driving safety and comfort as well as to improve fuel efficiency. Three different nonlinear model-based approaches for the inner-loop controllers are investigated for this system: the conventional Proportional-Integral Gain Scheduling controller (PI + GS) and two other strategies based on the simplified modelling of the vehicle dynamics: Balance-Based Adaptive Controller (B-BAC) and Nonlinear Model Predictive Controller (NMPC). The performance of each considered ACC system is tested by simulation under the same realistic scenarios for distance tracking mode and switching mode. The comparative criteria include driving safety, comfort of the driver and passengers and the fuel-economy aspect defined as BSFC (Brake Specific Fuel Consumption) index. The simulation results demonstrate that all the considered control algorithms meet the safety and car-following requirements while they provide slightly different level of driving comfort and fuel consumption, depending on the traffic situation and operating mode.

Application of the state-dependent nonlinear model predictive control in adaptive cruise control system

In this paper the Nonlinear Model Predictive Control (NMPC) is applied in adaptive cruise control (ACC) system. State-dependent algorithm, as an approach to control the brake and throttle opening position is proposed. Two linear time invariant (LTI) discrete-time state space models, corresponding to modes of operation: accelerating — the throttle is active and braking — the brake is active, have been extracted from the full non-linear model of the vehicle and the power train. Those models are used to design the NMPC controller. From this prospective, a single but state-dependent MPC can be utilized in controlling the throttle and brake position which provides an easy approach to the control design process. The design is implemented in simulation environment to test its performance. Finally, a comparison between the application of two control methods including state-dependent NMPC and Linear Quadratic Control (LQC) are presented.

Fuel Efficiency by Coasting in the Vehicle

This paper investigates the possibility of improving the fuel efficiency by decreasing the engine speed during the coasting phase of the vehicle. The proposed approach is stimulated by the fact that the engine losses increase with the engine speed. If the engine speed is retained low, the engine losses will be reduced and subsequently the tractive torque will be increased, enabling the vehicle to remain moving for longer duration while coasting. By increasing the time period of the coasting the fuel efficiency can be increased, especially travelling downhill, since it can benefit from the kinematic energy stored in the vehicle to continue coasting for a longer duration. It is already industry standard practice to cut fuel during coasting and refuel at low engine speed. The substantial difference proposed in this paper is the controlled reduction of engine speed during this phase and thus reduction in the engine losses, resulting in improved fuel economy. The simulation model is tested and the results illustrating an improvement to the fuel efficiency through the proposed method are presented. Some results of the experimental tests with a real vehicle through the proposed strategy are also presented in the paper.

Teaching control using NI Starter Kit Robot

Teaching engineering concepts using demonstrations and experiments on real hardware is always engaging and well received by students. This paper provides reference materials (both theoretical and test results), to be used in Control teaching and assessment using a laboratory experiment, with a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). This robotic vehicle is programmed using a graphical programming environment. The Adaptive Cruise Control (ACC) algorithm based on Proportional-Integral (PI) and Proportional - Integral - Derivative (PID) Control are deployed on a field programmable gate array (FPGA), included in the robots architecture. The robot model (based on a given second order transfer function) is controlled using the same method. The results obtained are compared for the simulation model and a real robot. The performance comparison demonstrates a good correlation between theory and implementation, whilst demonstrating problems and discrepancies introduced by a real system.

Adaptive Cruise Control System using Balance-Based Adaptive Control technique

In this paper, a nonlinear Balance-Based Adaptive Control (B-BAC) technique is proposed for the design of an Adaptive Cruise Control (ACC) System. The architecture for ACC system represents the cascade control and it includes two control loops. The B-BAC technique accounts for system nonlinearities and it allows for split range control of both the brake and the throttle so it can be utilized in the design of the inner-loop control, while the outer-loop control is based on a simple proportional controller. The results of the simulation prove the effectiveness of the proposed algorithm in distance and speed tracking as well as providing the smooth variation of the vehicle acceleration.

Robotic Implementation of the Adaptive Cruise Control-Comparison of Three Control Methods

This paper explores the practical implementation of the Adaptive Cruise Control (ACC) system on a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). The ACC algorithm based on three control methodologies, the fuzzy PID control, model predictive control (MPC) and conventional PID control, is deployed on a field programmable gate array (FPGA), included in the robot’s architecture. The results are compared both in the simulation and using the real robot. The comparison of the performance demonstrates a good correlation between theory and real implementation, whilst highlighting problems introduced by a real system.

Teaching Fuzzy Logic Control Based on a Robotic Implementation

Abstract Advanced control concepts present a teaching challenge - even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modeling the plant, applying control strategy and tuning. This paper provides reference materials (both theoretical and test results), to be used in control teaching and assessment using a laboratory experiment, with a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). This paper explores the practical implementation of the ACC system through use of a real-time single board computer based robotic vehicle (National Instruments Robotics Starter Kit). The ACC algorithm based on fuzzy PID control is deployed on a field programmable gate array (FPGA), included in the robots architecture. This robotic vehicle is programmed using a graphical programming language (LabVIEW). A Kalman filter is used to estimate the unmeasured parameters while implementing the control algorithm in the hardware (the real robot). The results obtained are compared for the simulation model and the real robot, respectively. The experiment demonstrates clear correlation between theoretical expectations and real-life system performance and at the same time offers a novel idea how to deliver this advanced control concept in an applied and visual manner.

Teaching Model Predictive Control Algorithm Using Starter Kit Robot

Abstract Advanced control concepts present a teaching challenge, where even at master level students benefit from these concepts being implemented and demonstrated on real hardware, rather than simply modelling the plant, applying control strategy and tuning. This paper describes one of a series of three experiments demonstrating the implementation of different control strategies using adaptive cruise control (ACC) on robot models and real robots. The experiment described here utilises the model predictive control (MPC) strategy implemented in ACC. The algorithm is realised using the graphical programming language (LabVIEW) as the design environment and National Instruments Robotics Starter Kit robot as the target hardware, with the code being deployed on a field programmable gate array (FPGA), included in the robot’s architecture. Two robotic vehicles, ‘the leader’ and ‘the follower’ are programmed to execute ACC: the velocity of the leader robot and the distance between the robots are augmented into the robot’s state-space equation, to design the controller (MPC), which was then tuned for both velocity and distance tracking modes. The experiment offers a novel idea on how to deliver this advanced control strategy in an applied and visual manner with laboratory experimentation supporting the theoretical aspects of learning. It brings to life some often stated theoretical qualities of an MPC controller, including quick rise time, minor fluctuation and a small distance tracking error, in line with current scientific papers. Thus, it demonstrates to students a clear correlation between theoretical expectations and real-life system performance whilst challenging their ability to work with real hardware.