Trajectory tracking control of tractor semi-trailer vehicles poses significant challenges due to inherent off-tracking behavior and roll instability risks. While existing approaches have demonstrated effectiveness, they often rely on computationally intensive numerical solvers and require time-consuming manual tuning of cost function weights. This paper presents an approximate bi-level policy optimization (ABPO) framework that simultaneously optimizes the cost function and synthesizes an explicit control policy to minimize off-tracking while reducing computational complexity. The proposed framework employs a hierarchical structure: the upper level updates cost weights based on the trailer’s stability trajectory, while the lower level derives an approximate optimal policy by solving the tractor’s control problem. By leveraging Pontryagin’s Maximum Principle (PMP), we have developed a novel method to analytically compute cost weight gradients through differentiation of the PMP conditions. This enables the formulation of a related optimal control problem (OCP) whose solutions directly yield gradients for cost parameter updates. The ABPO framework achieves automatic weight coefficient adjustment, enhances trajectory tracking accuracy for both tractor and trailer units, and significantly reduces computational burden. Simulation and experimental validation across 4 classical scenarios demonstrates that the learned policy reduces rearward amplification by 17.82%, lateral tracking errors by 84.15%, and rollover by 64.19%, respectively. Notably, the control policy computation requires less than 10 ms, making it suitable for real-time applications. The source code for the algorithms described in this paper is publicly available at https://github.com/TroyResearch/ABPO.git.

Off-road environments with varied terrain and obstacle types present substantial challenges to the safe maneuvering of unmanned ground vehicles (UGVs). This study addresses the need for personalized path planning by introducing a multi-source off-road potential field (MOPF) method that quantifies risk and impediments in off-road settings based on internal and external characteristics. Specifically, Vehicle capability boundaries are defined by longitudinal dynamics analysis of the ego-vehicle to prevent instability due to insufficient driving force and limited adhesion conditions. A novel Non-Uniform Safety Margin Expression (NSME) is proposed to adjust the MOPF, allowing it to consider the vehicle’s state to enhance travel efficiency and minimize detours. The MOPF can be adapted according to the characteristics of the ego vehicle, drivers, and cargo. To incorporate driving styles, the Driving Style Probabilistic Roadmap (DSPRM) algorithm is developed, leading to smoother and more personalized paths. Comparative tests demonstrate that our method enables personalized path planning, achieving an average reduction of 10.29% in path length and 30.83% in path slope compared to traditional planning methods, while maintaining a safe distance from obstacles.

Inverse optimal control (IOC) seeks to infer a control cost function that captures the underlying goals and preferences of expert demonstrations. While significant progress has been made in finite-horizon IOC, which focuses on learning control cost functions based on rollout trajectories rather than actual trajectories, the application of IOC to receding horizon control, also known as model predictive control (MPC), has been overlooked. MPC is more prevalent in practical settings and poses additional challenges for IOC learning since it is complicated to calculate the gradient of actual trajectories with respect to cost parameters. In light of this, we propose the inverse MPC (IMPC) method to identify the optimal cost function that effectively minimizes the discrepancy between the actual trajectory and its associated demonstration. To compute the gradient of actual trajectories with respect to cost parameters, we first establish two differential Pontryagin's maximum principle (PMP) conditions by differentiating the traditional PMP conditions with respect to cost parameters and initial states, respectively. We then formulate two auxiliary optimal control problems based on the derived differentiated PMP conditions, whose solutions can be directly used to determine the gradient for updating cost parameters. We validate the efficacy of the proposed method through experiments involving five simulation tasks and two real-world mobile robot control tasks. The results consistently demonstrate that IMPC outperforms existing finite-horizon IOC methods across all experiments.

While research has shown that the agile chip design methodology is promising to sustain the scaling of computing performance in a more efficient way, it is still of limited usage in actual applications due to two major obstacles: 1) Lack of tool-chain and developing framework supporting agile chip design, especially for large-scale modern processors. 2) The conventional verification methods are less agile and become a major bottleneck of the entire process. To tackle both issues, we propose MINJIE, an open-source platform supporting agile processor development flow. MINJIE integrates a broad set of tools for logic design, functional verification, performance modelling, pre-silicon validation and debugging for better development efficiency of state-of-the-art processor designs. We demonstrate the usage and effectiveness of MINJIE by building two generations of an open-source superscalar out-of-order RISC-V processor code-named XIANGSHAN using agile methodologies. We quantify the performance of XIANGSHAN using SPEC CPU2006 benchmarks and demonstrate that XIANGSHAN achieves industry-competitive performance.