Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation

Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints

Autonomous Driving and Stabilisation for Collision Avoidance System in Structured Environment

In the last decades, autonomous vehicles have been an active area of research in the academia as well as in industry. In particular, trajectory planning/control under emergency scenarios pose major challenges from a control point of view. In this thesis, we are addressing these challenges by developing controllers for trajectory planning/control for the application of autonomous collision avoidance system for the next generation of self-driving vehicles. The first part of this work is dedicated to design a trajectory planning/control under extreme driving conditions such ashigh-speed driving and low surface friction conditions. It is imperative that the autonomous vehicles can ensure the safety considerations while performing a feasible and smooth manoeuvre. Thus we propose a framework including trajectory planing/control with integration of torque vectoring controller to design a feasible yet collision free trajectory subject to the external disturbances such as low surface friction and crosswind. Next, with the same control structure we propose a trajectory planing strategy using the idea of linear affine collision avoidance constraints that can be generalized for both straight and curve driving scenarios. A comparison between three controllers entitled as nominal model predictive control (MPC), offset-free MPC and robust MPC for planning a safe trajectory is then provided in order to justify the proposed trajectory planning design. Then the simulation results are validated in a high-fidelity co-simulation environment (IPG-Carmaker) to investigate the performance of the trajectory planning algorithms. In the second part of the thesis, a novel tuning technique is designed to improve the proposed control framework performance. The suggested tuning design is capable of providing optimal weights by matching the performance of a pre-design controller such as LQR controller to the trajectory planning/control framework. Finally, an intelligent controller entitled as Reinforcement Learning (RL) has been used as an on line tuning technique to improve the performance of yaw moment controller. This architecture is implemented on various operating conditions such as different friction surfaces and vehicle velocities and its performance has been validated using a four wheels vehicle model with non-linear tyre characteristic

Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance

This paper proposes an integrated trajectory planning based on Model Predictive Control (MPC) for designing collision-free evasive trajectory and a torque vectoring controller based on optimal control to ensure lateral-yaw stabilization in autonomous emergency collision avoidance under low friction and crosswinds on highways. The trajectory for performing the evasive manoeuvre is designed to minimise the deviation of the vehicle from the lane center while ensuring the vehicle remains within the road boundaries. The steering input computed from the MPC is used to steer the vehicle along the reference trajectory while the torque vectoring controller provides additional lateral-yaw stability. The integrated control framework was implemented on IPG Carmaker-MATLAB co-simulation platform and its efficacy was evaluated under different scenarios. Simulations performed for emergency collision avoidance at high speeds with low road friction and heavy crosswinds confirm the ability of the proposed closed-loop framework at successfully avoiding collisions with moving obstacles while ensuring that the controlled vehicle remains within its limits of stability. Furthermore, the robustness of the proposed control framework to variations in road friction changes is demonstrated by simulating an evasive manoeuvre at high-speeds for wide range of road friction conditions. Comparing the performance of the proposed control framework to a vehicle without the corrective actions available via torque vectoring highlight the additional benefits provided by the proposed closed-loop scheme at ensuring lateral-yaw stability under emergency scenarios

Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance

This paper proposes an integrated trajectory planning based on Model Predictive Control (MPC) for designing collision-free evasive trajectory and a torque vectoring controller based on optimal control to ensure lateral-yaw stabilization in autonomous emergency collision avoidance under low friction and crosswinds on highways. The trajectory for performing the evasive manoeuvre is designed to minimise the deviation of the vehicle from the lane center while ensuring the vehicle remains within the road boundaries. The steering input computed from the MPC is used to steer the vehicle along the reference trajectory while the torque vectoring controller provides additional lateral-yaw stability. The integrated control framework was implemented on IPG Carmaker-MATLAB co-simulation platform and its efficacy was evaluated under different scenarios. Simulations performed for emergency collision avoidance at high speeds with low road friction and heavy crosswinds confirm the ability of the proposed closed-loop framework at successfully avoiding collisions with moving obstacles while ensuring that the controlled vehicle remains within its limits of stability. Furthermore, the robustness of the proposed control framework to variations in road friction changes is demonstrated by simulating an evasive manoeuvre at high-speeds for wide range of road friction conditions. Comparing the performance of the proposed control framework to a vehicle without the corrective actions available via torque vectoring highlight the additional benefits provided by the proposed closed-loop scheme at ensuring lateral-yaw stability under emergency scenarios

Antinociceptive effects of aqeous extract Launaea acanthodes gum in mice

Introduction: Previous studies indicated that Launaea acanthodes (LA) can be effective against epilepsy, nervous disorder, local and articular pain and convulsion disorders. The aim of this study was to assessment the antinoceceptive effects of&nbsp; LA on peripheral and visceral pain in mice by using hot plate (HP), tail flick (TF), formalin (FT) and writhing (WR) tests. Materials and Methods: In this study 160 young adult male albino mice (25-30 g) were used (n = 8 in each group). Aqueous extract of LA gum (50, 100, and 200 mg/Kg IP) and saline (SAL) were injected 30 min before the pain evaluation tests. Acute and chronic pain was assessed by HP, TF and FT models and visceral pain was assessed by writhing test. Results: Results indicated that LA has analgesic effects (P< 0.05) in comparison with the control and saline groups&nbsp; and higher dose of LA was more effective (P< 0.001). Conclusion: The above findings showed that LA have modulatory effects on acute, chronic and visceral pai