Weight-Free Multi-Objective Predictive Cruise Control of Autonomous Vehicles in Integrated Perturbation Analysis and Sequential Quadratic Programming Optimization Framework

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Defeng He ◽  
Yujie Shi ◽  
Xiulan Song
Keyword(s):  
Quadratic Programming ◽  
Perturbation Analysis ◽  
Sequential Quadratic Programming ◽  
Autonomous Vehicles ◽  
Optimization Problem ◽  
Ride Comfort ◽  
Cruise Control ◽  
Multi Objective ◽  
Optimization Framework ◽  
Utopia Point

Adaptive cruise control of autonomous vehicles can be posed as a multi-objective optimization problem where several conflicting criteria, e.g., fuel economy, tracking capability, ride comfort, and safety, need to be satisfied simultaneously. In order to reconcile these conflicting criteria, this paper presents a novel multi-objective predictive cruise control (MOPCC) approach in the feasible perturbation-based real-time iterative optimization framework. The longitudinal dynamics of vehicles are described as nonlinear car-tracking models. The new cost function for MOPCC is defined as the distance of the criteria vector to the vector of separately minimized criteria (i.e., a utopia point of the criteria). The weight-free MOPCC is then obtained by solving a constrained nonlinear optimal control problem in receding horizon fashion. Due to the difficulty in solving the optimization problem, the integrated perturbation analysis and sequential quadratic programming (InPA-SQP) is employed to compute the cruise controller. The merit of the proposed MOPCC is that it can systematically handle different cruise scenarios regardless of the weights of the predictive cruise control (PCC) criteria. Several driving cases are used to demonstrate the effectiveness and benefits of the proposed approach via comparing to weighted PCC approaches.

scholarly journals Braille Block Detection via Multi-Objective Optimization from an Egocentric Viewpoint

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2775
Author(s):  
Tsubasa Takano ◽  
Takumi Nakane ◽  
Takuya Akashi ◽  
Chao Zhang
Keyword(s):  
Optimization Problem ◽  
Line Pair ◽  
Objective Functions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimization Framework ◽  
Visually Impaired People ◽  
Comprehensive Comparison ◽  
Basic Characteristics ◽  
Support Devices

In this paper, we propose a method to detect Braille blocks from an egocentric viewpoint, which is a key part of many walking support devices for visually impaired people. Our main contribution is to cast this task as a multi-objective optimization problem and exploits both the geometric and the appearance features for detection. Specifically, two objective functions were designed under an evolutionary optimization framework with a line pair modeled as an individual (i.e., solution). Both of the objectives follow the basic characteristics of the Braille blocks, which aim to clarify the boundaries and estimate the likelihood of the Braille block surface. Our proposed method was assessed by an originally collected and annotated dataset under real scenarios. Both quantitative and qualitative experimental results show that the proposed method can detect Braille blocks under various environments. We also provide a comprehensive comparison of the detection performance with respect to different multi-objective optimization algorithms.

A Sequential-Quadratic-Programming-Filter Algorithm with a Modified Stochastic Gradient for Robust Life-Cycle Optimization Problems with Nonlinear State Constraints

SPE Journal ◽  
2020 ◽  
Vol 25 (04) ◽  
pp. 1938-1963 ◽  
Author(s):  
Zhe Liu ◽  
Albert C. Reynolds
Keyword(s):  
Life Cycle ◽  
Quadratic Programming ◽  
Optimization Algorithm ◽  
Sequential Quadratic Programming ◽  
Optimization Problem ◽  
State Constraints ◽  
Optimization Problems ◽  
Filter Method ◽  
Reservoir Model ◽  
Nonlinear State

Summary Solving a large-scale optimization problem with nonlinear state constraints is challenging when adjoint gradients are not available for computing the derivatives needed in the basic optimization algorithm used. Here, we present a methodology for the solution of an optimization problem with nonlinear and linear constraints, where the true gradients that cannot be computed analytically are approximated by ensemble-based stochastic gradients using an improved stochastic simplex approximate gradient (StoSAG). Our discussion is focused on the application of our procedure to waterflooding optimization where the optimization variables are the well controls and the cost function is the life-cycle net present value (NPV) of production. The optimization algorithm used for solving the constrained-optimization problem is sequential quadratic programming (SQP) with constraints enforced using the filter method. We introduce modifications to StoSAG that improve its fidelity [i.e., the improvements give a more accurate approximation to the true gradient (assumed here to equal the gradient computed with the adjoint method) than the approximation obtained using the original StoSAG algorithm]. The modifications to StoSAG vastly improve the performance of the optimization algorithm; in fact, we show that if the basic StoSAG is applied without the improvements, then the SQP might yield a highly suboptimal result for optimization problems with nonlinear state constraints. For robust optimization, each constraint should be satisfied for every reservoir model, which is highly computationally intensive. However, the computationally viable alternative of letting the reservoir simulation enforce the nonlinear state constraints using its internal heuristics yields significantly inferior results. Thus, we develop an alternative procedure for handling nonlinear state constraints, which avoids explicit enforcement of nonlinear constraints for each reservoir model yet yields results where any constraint violation for any model is extremely small.

Multi-Objective Optimization of a Segmented Lunar Wheel Concept

2011 ◽  
Author(s):  
Michele Faragalli ◽  
Damiano Pasini ◽  
Peter Radzizsewski
Keyword(s):  
Optimization Problem ◽  
Element Model ◽  
Multi Objective Optimization ◽  
Stress Concentrations ◽  
Multi Objective ◽  
Optimization Framework ◽  
Multi Objective Genetic Algorithm ◽  
Design Variables ◽  
Rider Comfort ◽  
Original Concept

The goal of this work is to develop a systematic method for optimizing the structural design of a segmented wheel concept to improve its operating performance. In this study, a wheel concept is parameterized into a set of size and shape design variables, and a finite element model of the wheel component is created. A multi-objective optimization problem is formulated to optimize its directional compliance and reduce stress concentrations, which has a direct affect on the efficiency, traction, rider comfort, maneuverability, and reliability of the wheel. To solve the optimization problem, a Matlab-FE simulation loop is built and a multi-objective genetic algorithm is used to find the Pareto front of optimal solutions. A trade-off design is selected which demonstrates an improvement from the original concept. Finally, recommendations will be made to apply the structural optimization framework to alternative wheel conceptual designs.

scholarly journals Efficient predictive cruise control of autonomous vehicles with improving ride comfort and safety

2020 ◽  
Vol 53 (1-2) ◽  
pp. 18-28
Author(s):  
Defeng He ◽  
Wentao He ◽  
Xiulan Song
Keyword(s):  
Control Problem ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Ride Comfort ◽  
Main Idea ◽  
Driving Safety ◽  
Decision Variable ◽  
Cruise Control ◽  
Constrained Optimal Control ◽  
Control Approach

In this paper, the adaptive cruise control problem of autonomous vehicles is considered and we propose a novel predictive cruise control approach to improve driving safety and comfort of the host vehicle. The main idea of the approach is that the predicted acceleration commands of the host vehicle are stair-likely pre-planned to satisfy their changes along the same direction within the prediction horizon. The predictive cruise controller is then computed by online solving a finite horizon constrained optimal control problem with a decision variable. Besides explicitly handling safety constraints of vehicles, the obtained controller has abilities to efficiently attenuate peaks of the cruise commands while reducing computational load of online solving the optimization problem. Hence, the ride comfort and safety performances of vehicles are improved in terms of softening acceleration response and constraint satisfaction. Moreover, the ride comfort, following and safety performances of vehicles are summed with varying weights to cope with various traffic scenarios. Some classical cases are adopted to evaluate the proposed adaptive cruise control algorithm in terms of ride comfort, car-following ability and computational demand.

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