The issue of asynchronous passive control is addressed for Markov jump systems in this technical note. The asynchronization phenomenon appears between the system modes and controller modes, which is described by a hidden Markov model. Accordingly, a hidden Markov jump model is used to name the resultant closed-loop system. By utilizing the matrix inequality technique, three equivalent sufficient conditions are obtained, which can guarantee the hidden Markov jump systems to be stochastically passive. Based on the established conditions, the design of asynchronous controller, which covers the well-known mode-independent controller and synchronous controller as special cases, is addressed. The DC motor device is applied to demonstrate the practicability of the derived asynchronous synthesis scheme.

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Passivity-Based Asynchronous Control for Markov Jump Systems

Constrained Model Predictive Control

Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.

Research Advances and Challenges of Autonomous and Connected Ground Vehicles

Variable time delays exist between the driver's inputs and the responses of the vehicle dynamic states during a critical steering course. And due to the delay of active brake actuators, a sideslip or a rollover may occur even to a vehicle with a traditional stability control system. In addition, the unnecessary intervention of rollover prevention controller may deteriorate yaw stability of a vehicle in these situations. To mitigate the adverse effect of time delay on vehicle stability control and to realize coordinated stability control, a novel three-dimensional dynamic stability controller (3DDSC) is designed for yaw stability control, yaw–roll stability control and rollover prevention control. The framework consists of a supervisor, an upper controller, and a lower controller. A nonlinear vehicle model is used in the supervisor to predict the vehicle's future states and to determine the control mode and the related controllable areas with active brake method. Then model predictive control is used in the upper controller to calculate the desired tire forces of four wheels under the constraints of the given controllable area; then, the desired tire forces are realized by a lower hydraulic pressure controller. The proposed 3DDSC is evaluated with a CarSim–MATLAB cosimulation and hardware-in-the-loop simulation. The results show that 3DDSC can achieve a seamless integration of lateral stability and rollover prevention in complicated steering maneuvers.

A Three-Dimensional Dynamics Control Framework of Vehicle Lateral Stability and Rollover Prevention via Active Braking With MPC

The autonomous vehicle is a mobile robot integrating multi-sensor navigation and positioning, intelligent decision making and control technology. This paper presents the control system architecture...

https://journals.sagepub.com/doi/pdf/10.5772/51314

Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID

This paper describes a comparative study of steering and yaw moment control maneuvers in the model predictive control and linear quadratic control approaches for path-following control of an autonomous vehicle. The authors present the effectiveness of the model predictive control and linear quadratic control approaches for stability control of the vehicle’s lateral position and yaw angle for different control maneuvers: two-wheel steering, four-wheel steering and direct yaw moment control. The authors then propose model predictive control with a feedforward controller to minimize the tracking errors of the lateral position and the yaw angle in an active front steering maneuver, and these are compared with the results from linear quadratic control that has a feedforward controller. Model predictive control is designed on the basis of the simple yaw–lateral motions of a single-track vehicle with a linear tire model (i.e. a bicycle model), which is an approximation of the more realistic model of a vehicle with double-track yaw–roll motion with a non-linear tire model (i.e. the Pacejka model). The linear quadratic controller is designed on the basis of the same approach as adopted for the model predictive controller to achieve a fair comparison. On the basis of a given trajectory, the authors simulate the maneuver of the vehicle at a low speed, a middle speed and a high speed because load transfer effects will influence the roll dynamics especially at a high speed. The authors also perform simulations on low-road-friction surfaces in a double-lane-change scenario with the aim of following the desired trajectory as closely as possible while maintaining the vehicle stability. The simulation results show that model predictive control through two-wheel steering and four-wheel steering with direct yaw moment control performed better in terms of trajectory tracking at a high forward speed and low road surface variation. The proposed model predictive control with a feedforward controller is shown to be effective in minimizing the trajectory tracking errors. For all control maneuvers, model predictive control gives a better tracking performance than linear quadratic control does. In addition, when the roll dynamics are considered, model predictive control significantly improves the vehicle stability and the trajectory along the desired path.

Comparative study of autonomous path-following vehicle control via model predictive control and linear quadratic control:

This paper presents and studies various selected literature primarily from conference proceedings, journals and clinical tests of the robotic, mechatronics, neurology and biomedical engineering of rehabilitation robotic systems The present paper focuses of three main categories: types of rehabilitation robots, key technologies with current issues and future challenges Literature on fundamental research with some examples from commercialized robots and new robot development projects related to rehabilitation are introduced Most of the commercialized robots presented in this paper are well known especially to robotics engineers and scholars in the robotic field, but are less known to humanities scholars The field of rehabilitation robot research is expanding; in light of this, some of the current issues and future challenges in rehabilitation robot engineering are recalled, examined and clarified with future directions This paper is concluded with some recommendations with respect to rehabilitation robots

Recent trends for practical rehabilitation robotics, current challenges and the future.

This paper compares the Model predictive control (MPC) and Linear quadratic control (LQC) of heavy vehicles via active front steering for rollover prevention in inclement environments. In both control methods, a Disturbance rejection control (DRC) that negates the effects of wind and road bank acting on the vehicle is designed. Load transfer ratio (LTR) is applied to judge rollover by mitigating the absolute value of LTR as much as possible. We tested and compared two different controllers, i) MPC with DRC and ii) LQC with DRC. Two types of environmental conditions were considered, i) typhoon and ii) typhoon on a bumpy road. The simulation results demonstrate that MPC was more successfully implemented than LQC during LTR suppression. This paper also proposes an MPC for coordination of active rear steering and differential braking control maneuvers to prevent rollover in inclement environments. For a feasible comparison, the LQC controller was designed using the same approach adopted for the MPC controller. Results show the proposed coordinated control yields better performance for rollover prevention than LQC.

Comparative study of MPC and LQC with disturbance rejection control for heavy vehicle rollover prevention in an inclement environment

This paper contributes to increase knowledge about a Model Predictive Control (MPC) approach to untripped rollover prevention of heavy vehicles which make a panic lane change maneuver in order to avoid an obstacle in the path. Active differential braking control shows good effect on limit roll angle during an urgent situation. However, at the same time of avoiding rollover accidents, prohibiting the vehicle from the driver's intended and the vehicle's actual lane can also yield other accidents such as bumping into guard rails or cones. Thus, it is necessary to track the driver's desired path as closely as possible while preventing the vehicle from rollover. Here, a new control method of switching the MPC controllers which uses differential braking with dead zone and active rear steer are proposed. Simultaneously, the trade-off between rollover prevention and path tracking is highlighted through simulation results. The effectiveness of using switching controllers designed for the trade-off solution is also confirmed through simulation results.

Yasuchika Mori

Papers

Comparative study of autonomous path-following vehicle control via model predictive control and linear quadratic control:

Recent trends for practical rehabilitation robotics, current challenges and the future.

Comparative study of MPC and LQC with disturbance rejection control for heavy vehicle rollover prevention in an inclement environment

Rollover prevention with Predictive Control of differential braking and rear wheel steering

Enhancing Path Following Control Performance of Autonomous Ground Vehicle through Coordinated Approach under Disturbance Effect