No abstract

This paper presents a novel solution for the posture control and ride comfort between the proposed wheel-legged robot (FWLR, Four Wheel-Legged Robot) and the unstructured terrain by means of a passively-actively transformable suspension system. Unlike most traditional robots, each leg of FWLR is independent from each other with a spring-damping system (passive system) is connected in series with an actuator (active system), so the posture control and ride comfort in complex terrain can be realized by the combination between active and passive systems. In order to verify the performance of posture control in complex terrain, a prototype and complex terrain are established firstly, then a posture control model, algorithm, and controller considering the suspension system are proposed and verified by the comparison between co-simulation and experiment, the results showed that the pitch angle and roll angles in complex terrain can be controlled. To show the impact of passively-actively transformable suspension system on ride comfort (vibration isolation performance), different dynamic models with different DOF (Degree of Freedom) are established, the co-simulation results showed that the passive system and active posture control system can also effectively improve the vibration isolation performance and ride comfort of FWLR in complex terrain. The research results of this paper have important reference significance and practical value for enriching and developing the mechanism design and theoretical research of wheel-legged robot and promoting the engineering application of all terrain robot.

Model predictive control (MPC) is the mainstream method in the motion control of autonomous vehicles. However, due to the complex and changeable driving environment, the perturbation of vehicle parameters will cause the steady-state error problem, which will lead to the degradation of controller performance. In this paper, the offset-free MPC control method is proposed to solve the steady-state error problem systematically. The core idea of this method is to model the model mismatch, control input offset, and external disturbances as disturbance terms, then use filters to observe these disturbances and finally eliminate the influence of these disturbances on the steady-state error in the MPC solution stage. This paper uses the Kalman filter as an observer, which is integrated into our latest designed MPC solver. Based on state of the art sparse quadratic programming (QP) solver OSQP, an offset free model predictive control (OF-MPC) framework based on disturbance observation and MPC is formed. The proposed OF-MPC solver can efficiently deal with common model mismatch problems such as tire stiffness mismatch, steering angle offset, lateral slope disturbance, and so on. This framework is very efficient and completes all calculations less than 7ms when the horizon length is 50. The efficiency and robustness of the algorithm are verified on our newly designed ROS-Unreal4-CarSim real-time co-simulation platform and real vehicle experiments.

No abstract

No abstract