1,018 research outputs found
Data-Driven Predictive Control for Multi-Agent Decision Making With Chance Constraints
In the recent literature, significant and substantial efforts have been
dedicated to the important area of multi-agent decision-making problems.
Particularly here, the model predictive control (MPC) methodology has
demonstrated its effectiveness in various applications, such as mobile robots,
unmanned vehicles, and drones. Nevertheless, in many specific scenarios
involving the MPC methodology, accurate and effective system identification is
a commonly encountered challenge. As a consequence, the overall system
performance could be significantly weakened in outcome when the traditional MPC
algorithm is adopted under such circumstances. To cater to this rather major
shortcoming, this paper investigates an alternate data-driven approach to solve
the multi-agent decision-making problem. Utilizing an innovative modified
methodology with suitable closed-loop input/output measurements that comply
with the appropriate persistency of excitation condition, a non-parametric
predictive model is suitably constructed. This non-parametric predictive model
approach in the work here attains the key advantage of alleviating the rather
heavy computational burden encountered in the optimization procedures typical
in alternative methodologies requiring open-loop input/output measurement data
collection and parametric system identification. Then with a conservative
approximation of probabilistic chance constraints for the MPC problem, a
resulting deterministic optimization problem is formulated and solved
efficiently and effectively. In the work here, this intuitive data-driven
approach is also shown to preserve good robustness properties. Finally, a
multi-drone system is used to demonstrate the practical appeal and highly
effective outcome of this promising development in achieving very good system
performance.Comment: 10 pages, 6 figure
Control and Optimization for Aerospace Systems with Stochastic Disturbances, Uncertainties, and Constraints
The topic of this dissertation is the control and optimization of aerospace systems under the influence of stochastic disturbances, uncertainties, and subject to chance constraints. This problem is motivated by the uncertain operating environments of many aerospace systems, and the ever-present push to extract greater performance from these systems while maintaining safety. Explicitly accounting for the stochastic disturbances and uncertainties in the constrained control design confers the ability to assign the probability of constraint satisfaction depending on the level of risk that is deemed acceptable and allows for the possibility of theoretical constraint satisfaction guarantees.
Along these lines, this dissertation presents novel contributions addressing four different problems: 1) chance-constrained path planning for small unmanned aerial vehicles in urban environments, 2) chance-constrained spacecraft relative motion planning in low-Earth orbit, 3) stochastic optimization of suborbital launch operations, and 4) nonlinear model predictive control for tracking near rectilinear halo orbits and a proposed stochastic extension. For the first problem, existing dynamic and informed rapidly-expanding random trees algorithms are combined with a novel quadratic programming-based collision detection algorithm to enable computationally efficient, chance-constrained path planning. For the second problem, a previously proposed constrained relative motion approach based on chained positively invariant sets is extended in this dissertation to the case where the spacecraft dynamics are controlled using output feedback on noisy measurements and are subject to stochastic disturbances. Connectivity between nodes is determined through the use of chance-constrained admissible sets, guaranteeing that constraints are met with a specified probability. For the third problem, a novel approach to suborbital launch operations is presented. It utilizes linear covariance propagation and stochastic clustering optimization to create an effective software-only method for decreasing the probability of a dangerous landing with no physical changes to the vehicle and only minimal changes to its flight controls software. For the fourth problem, the use of suboptimal nonlinear model predictive control (NMPC) coupled with low-thrust actuators is considered for station-keeping on near rectilinear halo orbits. The nonlinear optimization problems in NMPC are solved with time-distributed sequential quadratic programming techniques utilizing the FBstab algorithm. A stochastic extension for this problem is also proposed. The results are illustrated using detailed numerical simulations.PHDAerospace EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162992/1/awbe_1.pd
Stochastic Model Predictive Control with a Safety Guarantee for Automated Driving
Automated vehicles require efficient and safe planning to maneuver in
uncertain environments. Largely this uncertainty is caused by other traffic
participants, e.g., surrounding vehicles. Future motion of surrounding vehicles
is often difficult to predict. Whereas robust control approaches achieve safe,
yet conservative motion planning for automated vehicles, Stochastic Model
Predictive Control (SMPC) provides efficient planning in the presence of
uncertainty. Probabilistic constraints are applied to ensure that the maximal
risk remains below a predefined level. However, safety cannot be ensured as
probabilistic constraints may be violated, which is not acceptable for
automated vehicles. Here, we propose an efficient trajectory planning framework
with safety guarantees for automated vehicles. SMPC is applied to obtain
efficient vehicle trajectories for a finite horizon. Based on the first
optimized SMPC input, a guaranteed safe backup trajectory is planned, using
reachable sets. The SMPC input is only applied to the vehicle if a safe backup
solution can be found. If no new safe backup solution can be found, the
previously calculated, still valid safe backup solution is applied instead of
the SMPC solution. Recursive feasibility of the safe SMPC algorithm is proved.
Highway simulations show the effectiveness of the proposed method regarding
performance and safety
Reliable autonomous vehicle control - a chance constrained stochastic MPC approach
In recent years, there is a growing interest in the development of systems capable of performing
tasks with a high level of autonomy without human supervision. This kind of systems are known as
autonomous systems and have been studied in many industrial applications such as automotive,
aerospace and industries. Autonomous vehicle have gained a lot of interest in recent years and have
been considered as a viable solution to minimize the number of road accidents. Due to the
complexity of dynamic calculation and the physical restrictions in autonomous vehicle, for example,
deterministic model predictive control is an attractive control technique to solve the problem of
path planning and obstacle avoidance. However, an autonomous vehicle should be capable of driving
adaptively facing deterministic and stochastic events on the road. Therefore, control design for
the safe, reliable and autonomous driving should consider vehicle model uncertainty as well
uncertain external influences. The stochastic model predictive control scheme provides the
most convenient scheme for the control of autonomous vehicles on moving horizons, where chance
constraints are to be used to guarantee the reliable fulfillment of trajectory constraints and
safety against static and random obstacles. To solve this kind of problems is known as chance
constrained model predictive control. Thus, requires the solution of a chance constrained
optimization on moving horizon. According to the literature, the major challenge for solving chance
constrained optimization is to calculate the value of probability. As a result, approximation
methods have been proposed for solving this task.
In the present thesis, the chance constrained optimization for the autonomous vehicle is solved
through approximation method, where the probability constraint is approximated by using a smooth
parametric function. This methodology presents two approaches that allow the solution of chance
constrained optimization problems in inner approximation and outer approximation. The aim of this
approximation methods is to reformulate the chance constrained optimizations problems as a sequence
of nonlinear programs. Finally, three case studies of autonomous vehicle for tracking and obstacle
avoidance are presented in this work, in which three levels probability of reliability are
considered
for the optimal solution.Tesi
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