Looking into Year 2019 and Beyond, Develop Robust Model Predictive Control with Six Sigma
Dec 29, 2018 |
Looking into year 2019 and beyond, research in the field of autonomous vehicles has created promising results in the last years. Some researches groups have shown perception systems which are able to capture even complicated urban scenarios in great detail. However, what is often missing are general-purpose path- or trajectory planners which are not designed for a specific purpose. Based on Dr. John X. Wang’s most recent book “Industrial Design Engineering: Inventive Problem Solving,” researchers and safety engineering community can look at path- and trajectory planning from an architectural point of view and show how model predictive frameworks could contribute to generalized path and trajectory generation approaches for generating safe trajectories even in cases of system failures.
This CRC Press Featured Author News explores the use of Model Predictive Control (MPC) techniques to solve vehicle lateral motion control problem on highway scenarios. In particular, the problem of autonomously driving a vehicle along a desired path is formulated, where safety constraints and performance levels must be guaranteed for all possible road curvatures within a compact set. Safety constraints are translated into a maximum lateral deviation and orientation error with regard to a desired path, while performance requirements are formulated in terms of bounded lateral acceleration and velocity. Monte Carlo Simulation can help to show that the designed controller is capable of delivering acceptable performance at the cost of limited online computational costs.
Model Predictive Control (MPC) is a powerful technique to control nonlinear, multi-input multi-output systems with input and state constraints. It has previously been considered for trajectory tracking control of automated vehicles in many projects. However, MPC faces several challenges in practice, mainly regarding computation time and difficulty of implementation. Based on Monte Carlo Simulation, a new MPC algorithm can address both challenges, specifically for the problem of trajectory tracking. The algorithm comes with a code generator for open-source, library-free C-code with static memory allocation. Thus it can directly be deployed to a wide range of embedded control units (ECUs). The algorithm consists of a single controller block, which only requires adjustment of specific vehicle and tuning parameters.
Related to Functional Safety, highly automated vehicles have the potential to provide a variety of benefits, for example, decreasing traffic injuries and fatalities while offering people the freedom to choose how to spend their time in their vehicle without jeopardizing the safety of themselves or other traffic participants. For automated vehicles to be successfully commercialized, the safety and reliability of the technology must be guaranteed. A safe and robust trajectory planning algorithm is therefore a key enabling technology to realize an intelligent vehicle system for automated driving that can cope with both normal and high risk driving situations.
Here, we need to address the problem of real-time trajectory planning for smooth and safe automated driving maneuvers in traffic situations where the ego vehicle does not have right-of-way i.e., yielding maneuvers e.g., lane change, roundabout entry, and intersection crossing. The considered problem of generating an appropriate, safe, and smooth trajectory consisting of a sequence of longitudinal and lateral control signals is formulated as convex optimal control problems in the form of Quadratic Programs (QP) within the Model Predictive Control (MPC) framework in a manner that allows for reliable, predictable, and robust, real-time implementation on a standard passenger vehicle platform such autonomous bus.
Current research and development of automated driving technologies is a top edge topic. The benefits of such applications are well-documented and considered to enhance significantly attributes such as safety, fuel consumption and road throughput. The development of such functions require advanced control strategies to meet the aforementioned requirements. Towards this direction, the objective of this project is to analyze, design and implement a Model Predictive Control (MPC) strategy to enhance a Cooperative Advanced Cruise Control (CACC) automated system in line with ISO 26262 safety standards. A new architecture framework towards Functional Safety with Layers of Protection needs to be developed.
Looking into year 2019 and beyond, Engineering Robust Design with Six Sigma deals with the broad spectrum of problems including the evaluation of fault robustness of the software implemented Dynamic Matrix Control (DMC) Model Predictive Control (MPC) algorithms. To improve effectiveness, numerical and explicit implementations of the DMC algorithms should be considered to reveal that faults affecting the algorithms can provoke undesirable behavior or even destabilize the process. However, FITS would not be sufficient in case of the numerical DMC implementation. New software implemented fault injection approach would be based on the system emulator and delivers the same level of functionality as FITS while having capability to extend fault models.
SubjectsComputer Science & Engineering, Energy & Clean Technology, Engineering - Chemical, Engineering - Civil, Engineering - Electrical, Engineering - Environmental, Engineering - General, Engineering - Industrial & Manufacturing, Engineering - Mechanical, Environmental Science, Ergonomics & Human Factors, Mathematics, Statistics