Several non-isothermal dispersion reactions accounting for energy and mass transport in a recycle stream are carried out in tubular reactor with recycle. These processes often have spatiotemporal distribution, stochas...
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ISBN:
(纸本)9798350373707;9798350373691
Several non-isothermal dispersion reactions accounting for energy and mass transport in a recycle stream are carried out in tubular reactor with recycle. These processes often have spatiotemporal distribution, stochastic disturbances, and recycle loop dead-time characteristics, described by nonlinear parabolic partial differential equations (PDEs). This paper is devoted to the design of a modelpredictivepathintegral (MPPI) controller to stabilize the axial temperature profile control in tubular reactor. An array of reduced-order approximations by virtue of Karhunen-Loeve decomposition (KLD), and then a nonlinear model suitable for control purposes is developed. Subsequently, the reduction model is embedded into the design of MPPI controller. Finally, the simulation results demonstrate that the MPPI controller presented a good behavior to eliminate the oscillations of the temperature profile in tubular reactor.
We present a sampling-based modelpredictivecontrol method that uses a generic physics simulator as the dynamical model. In particular, we propose a model predictive path integral controller (MPPI) that employs the G...
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We present a sampling-based modelpredictivecontrol method that uses a generic physics simulator as the dynamical model. In particular, we propose a model predictive path integral controller (MPPI) that employs the GPU-parallelizable IsaacGym simulator to compute the forward dynamics of the robot and environment. Since the simulator implicitly defines the dynamic model, our method is readily extendable to different objects and robots, allowing one to solve complex navigation and contact-rich tasks. We demonstrate the effectiveness of this method in several simulated and real-world settings, including mobile navigation with collision avoidance, non-prehensile manipulation, and whole-body control for high-dimensional configuration spaces. This is a powerful and accessible open-source tool to solve many contact-rich motion planning tasks.
The safety issue arises as one of the most important requirements for autonomous quadrotor flight. Recently, control barrier function (CBF) technique has been developed to provide necessary and sufficient conditions o...
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The safety issue arises as one of the most important requirements for autonomous quadrotor flight. Recently, control barrier function (CBF) technique has been developed to provide necessary and sufficient conditions on safety for robot systems. Previous works unified CBF with control techniques in an optimization fashion. However, these methods could not provide consistent performance, especially in clutter environments. In this letter, we present a novel predictivecontrol algorithm that combines CBF and pathintegralcontrol to develop a safety-critical controller for quadrotors. An efficient sequential projection algorithm is adopted to steer unsafe sampled inputs to a feasible region limited by safety barrier certificates. Comparing to existing methods, one distinguishing feature of our proposed method is that we provide collective thrusts and body rates (CTBR) control policies. Simulations and experiments demonstrate that our controller can deliver more robust maneuvers in the course of obstacle avoidance than state-of-the-art safe controllers.
This paper presents a tutorial overview of pathintegral (PI) approaches for stochastic optimal control and trajectory optimization. We concisely summarize the theoretical development of pathintegralcontrol to compu...
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This paper presents a tutorial overview of pathintegral (PI) approaches for stochastic optimal control and trajectory optimization. We concisely summarize the theoretical development of pathintegralcontrol to compute a solution for stochastic optimal control and provide algorithmic descriptions of the cross -entropy (CE) method, an open -loop controller using the receding horizon scheme known as the modelpredictivepathintegral (MPPI), and a parameterized state feedback controller based on the pathintegralcontrol theory. We discuss policy search methods based on pathintegralcontrol, efficient and stable sampling strategies, extensions to multi -agent decision -making, and MPPI for the trajectory optimization on manifolds. For tutorial demonstrations, some PI -based controllers are implemented in Python, MATLAB and ROS2/Gazebo simulations for trajectory optimization. The simulation frameworks and source codes are publicly available at the github page.
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