Study and comparison of non linear and LPV control approaches for vehicle stability control

International audienceThis paper proposes a study and a comparison between two efficient and novel vehicle control dynamics strategies, namely, the non linear Flatness control strategy and the LPV/Hinf control strategy. The first one concerns a controller based on the differential algebraic flatness of non linear systems and an algebraic non linear estimation applied to commercial vehicles. The second one is a LPV/Hinf (Linear Varying Parameter with the Hinf norm ) control using a stability monitoring system to achieve the vehicle dynamics control objective. These two strategies use Active Steering and Electro- Mechanical Braking actuators and aim at improving the vehicle stability and steerability by designing a multivariable controller that acts simultaneously on the lateral and longitudinal dynam- ics of the car. Simulations are performed on a complex nonlinear full vehicle model, the same driving scenario is applied for the two control strategies. The model parameters are those of a Renault Mégane Coupé (see table.I), obtained by identification with real data. Promising simulations results are obtained. Comparison between the two proposed strategies and the uncontrolled vehicle show the reliability and the robustness of the proposed solutions, even if one is governed within the linear control framework while the other one is a non linear control approach

Suboptimal Stabilization of Unknown Nonlinear Systems via Extended State Observers

This paper introduces a locally optimal stabilizer for multi-input muti-output autonomous nonlinear systems of any order with totally unknown dynamics. The control scheme proposed in this paper lies at the intersection of the active disturbance rejection control (ADRC) and the state-dependent Riccati equation (SDRE) technique. It is shown that using an extended state observer (ESO), a state-dependent coefficient matrix for the nonlinear system is obtainable which is used by the SDRE technique to construct a SDRE+ESO controller. As the SDRE technique is not guaranteed to be globally asymptotically stable, for systems with known linearization at the equilibrium, an algorithmic method is proposed for an approximated estimation of its region of attraction (ROA). Then, it is shown that the global asymptotic stability is achievable using a switching controller constructed by the SDRE+ESO method and ADRC for inside and outside the estimated ROA, respectively.Comment: 6 pages, 1 figur

Integrating Vehicle Slip and Yaw in Overarching Multi-Tiered Automated Vehicle Steering Control to Balance Path Following Accuracy, Gracefulness, and Safety

Balancing path following accuracy and error convergence with graceful motion in steering control is challenging due to the competing nature of these requirements, especially across a range of operating speeds and conditions. This paper demonstrates that an integrated multi-tiered steering controller considering the impact of slip on kinematic control, dynamic control, and steering actuator rate commands achieves accurate and graceful path following. This work is founded on multi-tiered sideslip and yaw-based models, which allow derivation of controllers considering error due to sideslip and the mapping between steering commands and graceful lateral motion. Observer based sideslip estimates are combined with heading error in the kinematic controller to provide feedforward slip compensation. Path following error is compensated by a continuous Variable Structure Controller (VSC) using speed-based path manifolds to balance graceful motion and error convergence. Resulting yaw rate commands are used by a backstepping dynamic controller to generate steering rate commands. A High Gain Observer (HGO) estimates sideslip and yaw rate for output feedback control. Stability analysis of the output feedback controller is provided, and peaking is resolved. The work focuses on lateral control alone so that the steering controller can be combined with other speed controllers. Field results provide comparisons to related approaches demonstrating gracefulness and accuracy in different complex scenarios with varied weather conditions and perturbations

Distributed Particle Filtering over Sensor Networks for Autonomous Navigation of UAVs

State estimation and control over sensor networks is a problem met in several applications such as surveillance and condition monitoring of large-scale systems, multi-robot systems and cooperating UAVs. In sensor networks the simplest kind of architecture is centralized. Distributed sensors send measurement data to a central processing unit which provides th

Optimal planning and control for hazard avoidance of front-wheel steered ground vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 124-128).Hazard avoidance is an important capability for safe operation of robotic vehicles at high speed. It is also an important consideration for passenger vehicle safety, as thousands are killed each year in passenger vehicle accidents caused by driver error. Even when hazard locations are known, high-speed hazard avoidance presents challenges in real-time motion planning and control of nonlinear and potentially unstable vehicle dynamics. This thesis presents methods for planning and control of optimal hazard avoidance maneuvers for a bicycle model with front-wheel steering and wheel slip. The planning problem is posed as an optimization problem in which constrained dynamic quantities, such as friction circle utilization, are minimized, while ensuring a minimum clearance from hazards. These optimal trajectories can be computed numerically, though real-time computation requires simple models and constraints. To simplify the computation of optimal avoidance trajectories, analytical solutions to the optimal planning problem are presented for a point mass subject to an acceleration magnitude constraint, which is analogous to a tire friction circle constraint. The optimal point mass solutions are extended to a nonlinear bicycle model by defining a flatness-based trajectory tracking controller using tire force control. This controller decouples the bicycle dynamics into a point mass at the front center of oscillation with an additional degree of freedom related to the vehicle yaw dynamics. Structure is identified in the yaw dynamics and is exploited to characterize stability limits. Simulation results verify the stability properties of the yaw dynamics. These results were applied to a semi-autonomous driver assistance system and demonstrated experimentally on a full-sized passenger vehicle. Efficient computation of point mass avoidance maneuvers was used as a cost-to-go for real-time numerical optimization of trajectories for a bicycle model. The experimental system switches control authority between the driver and an automatic avoidance controller so that the driver retains control authority in benign situations, and the automatic controller avoids hazards automatically in hazardous situations.by Steven C. Peters.Ph.D

Game Theoretic Model Predictive Control for Autonomous Driving

This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop a game theoretic four-stage model predictive controller (GT4SMPC). The controller is responsible for both longitudinal and lateral movements of Subject Vehicle (SV) . It includes a Stackelberg game as a high level controller and a model predictive controller (MPC) as a low level one. Specifically, GT4SMPC constantly establishes and solves games corresponding to multiple gaps in front of multiple-candidate vehicles (GCV) when SV is interacting with them by signaling a lane change intention through turning light or by a small lateral movement. SV’s payoff is the negative of the MPC’s cost function , which ensures strong connection between the game and that the solution of the game is more likely to be achieved by a hybrid MPC (HMPC). GCV’s payoff is a linear combination of the speed payoff, headway payoff and acceleration payoff. . We use decreasing acceleration model to generate our prediction of TV’s future motion, which is utilized in both defining TV’s payoffs over the prediction horizon in the game and as the reference of the MPC. Solving the games gives the optimal gap and the target vehicle (TV). In the low level , the lane change process are divided into four stages: traveling in the current lane, leaving current lane, crossing lane marking, traveling in the target lane. The division identifies the time that SV should initiate actual lateral movement for the lateral controller and specifies the constraints HMPC should deal at each step of the MPC prediction horizon. Then the four-stage HMPC controls SV’s actual longitudinal motion and execute the lane change at the right moment. Simulations showed the GT4SMPC is able to intelligently drive SV into the selected gap and accomplish both discretionary land change (DLC) and mandatory lane change (MLC) in a dynamic situation. Human-in-the-loop driving simulation indicated that GT4SMPC can decently control the SV to complete lane changes with the presence of human drivers. Second, we propose a differential game theoretic model predictive controller (DGTMPC) to address the drawbacks of GT4SMPC. In GT4SMPC, the games are defined as table game, which indicates each players only have limited amount of choices for a specific game and such choice remain fixed during the prediction horizon. In addition, we assume a known model for traffic vehicles but in reality drivers’ preference is partly unknown. In order to allow the TV to make multiple decisions within the prediction horizon and to measure TV’s driving style on-line, we propose a differential game theoretic model predictive controller (DGTMPC). The high level of the hierarchical DGTMPC is the two-player differential lane-change Stackelberg game. We assume each player uses a MPC to control its motion and the optimal solution of leaders’ MPC depends on the solution of the follower. Therefore, we convert this differential game problem into a bi-level optimization problem and solves the problem with the branch and bound algorithm. Besides the game, we propose an inverse model predictive control algorithm (IMPC) to estimate the MPC weights of other drivers on-line based on surrounding vehicle’s real-time behavior, assuming they are controlled by MPC as well. The estimation results contribute to a more appropriate solution to the game against driver of specific type. The solution of the algorithm indicates the future motion of the TV, which can be used as the reference for the low level controller. The low level HMPC controls both the longitudinal motion of SV and his real-time lane decision. Simulations showed that the DGTMPC can well identify the weights traffic vehicles’ MPC cost function and behave intelligently during the interaction. Comparison with level-k controller indicates DGTMPC’s Superior performance

Advanced control for miniature helicopters : modelling, design and flight test

Unmanned aerial vehicles (UAV) have been receiving unprecedented development during the past two decades. Among different types of UAVs, unmanned helicopters exhibit promising features gained from vertical-takeoff-and-landing, which make them as a versatile platform for both military and civil applications. The work reported in this thesis aims to apply advanced control techniques, in particular model predictive control (MPC), to an autonomous helicopter in order to enhance its performance and capability. First, a rapid prototyping testbed is developed to enable indoor flight testing for miniature helicopters. This testbed is able to simultaneously observe the flight state, carry out complicated algorithms and realtime control of helicopters all in a Matlab/Simulink environment, which provides a streamline process from algorithm development, simulation to flight tests. Next, the modelling and system identification for small-scale helicopters are studied. A parametric model is developed and the unknown parameters are estimated through the designed identification process. After a mathematical model of the selected helicopter is available, three MPC based control algorithms are developed focusing on different aspects in the operation of autonomous helicopters. The first algorithm is a nonlinear MPC framework. A piecewise constant scheme is used in the MPC formulation to reduce the intensive computation load. A two-level framework is suggested where the nonlinear MPC is combined with a low-level linear controller to allow its application on the systems with fast dynamics. The second algorithm solves the local path planning and the successive tracking control by using nonlinear and linear MPC, respectively. The kinematics and obstacle information are incorporated in the path planning, and the linear dynamics are used to design a flight controller. A guidance compensator dynamically links the path planner and flight controller. The third algorithm focuses on the further reduction of computational load in a MPC scheme and the trajectory tracking control in the presence of uncertainties and disturbances. An explicit nonlinear MPC is developed for helicopters to avoid online optimisation, which is then integrated with a nonlinear disturbance observer to significantly improve its robustness and disturbance attenuation. All these algorithms have been verified by flight tests for autonomous helicopters in the dedicated rapid prototyping testbed developed in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Game Theoretic Model Predictive Control for Autonomous Driving

This study presents two closely-related solutions to autonomous vehicle control problems in highway driving scenario using game theory and model predictive control. We first develop a game theoretic four-stage model predictive controller (GT4SMPC). The controller is responsible for both longitudinal and lateral movements of Subject Vehicle (SV) . It includes a Stackelberg game as a high level controller and a model predictive controller (MPC) as a low level one. Specifically, GT4SMPC constantly establishes and solves games corresponding to multiple gaps in front of multiple-candidate vehicles (GCV) when SV is interacting with them by signaling a lane change intention through turning light or by a small lateral movement. SV’s payoff is the negative of the MPC’s cost function , which ensures strong connection between the game and that the solution of the game is more likely to be achieved by a hybrid MPC (HMPC). GCV’s payoff is a linear combination of the speed payoff, headway payoff and acceleration payoff. . We use decreasing acceleration model to generate our prediction of TV’s future motion, which is utilized in both defining TV’s payoffs over the prediction horizon in the game and as the reference of the MPC. Solving the games gives the optimal gap and the target vehicle (TV). In the low level , the lane change process are divided into four stages: traveling in the current lane, leaving current lane, crossing lane marking, traveling in the target lane. The division identifies the time that SV should initiate actual lateral movement for the lateral controller and specifies the constraints HMPC should deal at each step of the MPC prediction horizon. Then the four-stage HMPC controls SV’s actual longitudinal motion and execute the lane change at the right moment. Simulations showed the GT4SMPC is able to intelligently drive SV into the selected gap and accomplish both discretionary land change (DLC) and mandatory lane change (MLC) in a dynamic situation. Human-in-the-loop driving simulation indicated that GT4SMPC can decently control the SV to complete lane changes with the presence of human drivers. Second, we propose a differential game theoretic model predictive controller (DGTMPC) to address the drawbacks of GT4SMPC. In GT4SMPC, the games are defined as table game, which indicates each players only have limited amount of choices for a specific game and such choice remain fixed during the prediction horizon. In addition, we assume a known model for traffic vehicles but in reality drivers’ preference is partly unknown. In order to allow the TV to make multiple decisions within the prediction horizon and to measure TV’s driving style on-line, we propose a differential game theoretic model predictive controller (DGTMPC). The high level of the hierarchical DGTMPC is the two-player differential lane-change Stackelberg game. We assume each player uses a MPC to control its motion and the optimal solution of leaders’ MPC depends on the solution of the follower. Therefore, we convert this differential game problem into a bi-level optimization problem and solves the problem with the branch and bound algorithm. Besides the game, we propose an inverse model predictive control algorithm (IMPC) to estimate the MPC weights of other drivers on-line based on surrounding vehicle’s real-time behavior, assuming they are controlled by MPC as well. The estimation results contribute to a more appropriate solution to the game against driver of specific type. The solution of the algorithm indicates the future motion of the TV, which can be used as the reference for the low level controller. The low level HMPC controls both the longitudinal motion of SV and his real-time lane decision. Simulations showed that the DGTMPC can well identify the weights traffic vehicles’ MPC cost function and behave intelligently during the interaction. Comparison with level-k controller indicates DGTMPC’s Superior performance