Hyeongjun Park

Real-Time Model Predictive Control for Shipboard Power Management Using the IPA-SQP Approach

Shipboard integrated power systems, the key enablers of ship electrification, call for effective power management control (PMC) to achieve optimal and reliable operation in dynamic environments under hardware limitations and operational constraints. The design of PMC can be treated naturally in a model predictive control (MPC) framework, where a cost function is minimized over a prediction horizon subject to constraints. The real-time implementation of MPC-based PMC, however, is challenging due to computational complexity of the numerical optimization. In this paper, an MPC-based PMC for a shipboard power system is developed and its real-time implementation is investigated. To meet the requirements for real-time computation, an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) algorithm is applied to solve a constrained MPC optimization problem. Several operational scenarios are considered to evaluate the performance of the proposed PMC solution. Simulations and experiments show that real-time optimization, constraint enforcement, and fast load following can be achieved with the IPA-SQP algorithm. Different performance attributes and their tradeoffs can be coordinated through proper tuning of the design parameters.

Model predictive control for spacecraft rendezvous and docking with a rotating/tumbling platform and for debris avoidance

A Model Predictive Control (MPC) approach is developed for spacecraft rendezvous and docking to a rotating/tumbling platform and for debris avoidance maneuvers. With this approach, the constraints on thrust, approach velocity and spacecraft positioning within the Line-of-Sight cone from the docking port are systematically treated. The trajectories are simulated and time-to-dock and fuel consumption are evaluated as cost function parameters are varied. Debris avoidance maneuvers are considered, with the debris in the spacecraft rendezvous path.

Shipboard power management using constrained nonlinear model predictive control

Both new and existing naval vessels of all sizes face ever-increasing power supply requirements to support advanced mission loads including high power sensors, weapons, and launchers. Adding additional conventional generators to support these loads is infeasible given size and weight constraints and given the pulsed nature of those new loads. Instead, an optimization-based Power Management Controller (PMC) is used to dynamically control power system sources and loads in real time in order to serve system needs with a minimal amount of power supply equipment. In this paper, a Model Predictive Control (MPC) approach is used to dynamically coordinate sources and loads based on future demand. A cost function is used to prioritize various ship goals and objectives, and constraints are added to reflect hardware limitations. A Constrained Nonlinear MPC algorithm is then used to minimize the cost over a finite future horizon and generate control commands in real-time. The PMC is demonstrated to successfully control and improve system performance on a hardware test bed for ship power system research.

Model Predictive Control of Spacecraft Docking with a Non-rotating Platform

Abstract A Model Predictive Control (MPC) framework is developed for control of spacecraft rendezvous and docking to a non-rotating/non-tumbling platform. The approach is based on combining conventional linear quadratic MPC with dynamically reconfigurable and changing in real-time constraints, that approximate the original constraints. An explicit solution is constructed in the form of a piecewise affine control law, and its complexity is evaluated.

Dynamic Air-Bearing Hardware-in-the-Loop Testbed to Experimentally Evaluate Autonomous Spacecraft Proximity Maneuvers

Ground-based testbeds are critical to develop and test different elements of spacecraft guidance, navigation, and control subsystems. This paper provides an in-detail description of a state-of-the-...

Analysis and Experimentation of Model Predictive Control for Spacecraft Rendezvous and Proximity Operations with Multiple Obstacle Avoidance

In this paper, Model Predictive Control (MPC) approaches are applied to multiple obstacle avoidance maneuvers for spacecraft rendezvous and docking. For safe obstacle avoidance, keep-out constraints are introduced by bounding ellipsoids around obstacles. In a linear quadratic MPC (LQ-MPC) framework, the rotating hyperplane method is used to convexify the obstacle avoidance constraints. A new method using two hyperplanes for convexification of the constraints is also proposed to improve performance of the LQ-MPC approach. A nonlinear MPC (NMPC) approach that deals with the nonlinear obstacle constraints directly is also applied to solve the spacecraft proximity maneuvering problems by using the nonlinear programming solver IPOPT (Interior Point OPTimizer). Real-time implementation of the MPC solutions is analyzed and compared on a physical test bed using several test cases. Numerical simulations and experiments demonstrate the obstacle avoidance as well as real-time operation capabilities of the considered control approaches.

Model Predictive Control of Spacecraft Relative Motion Maneuvers Using the IPA-SQP Approach

In this paper, a Model Predictive Controller (MPC) based on the Integrated Perturbation Analysis and Sequential Quadratic Programming (IPA-SQP) is designed and analyzed for spacecraft relative motion maneuvering. To evaluate the effectiveness of the IPA-SQP MPC, the results are compared with the linear quadratic MPC algorithm developed in [4, 13–15]. It is shown that the IPA-SQP algorithm can handle directly nonlinear constraints on thrust magnitude without resorting to saturation or polyhedral norm approximations. Spacecraft fuel consumption related metrics are examined for performance evaluation and comparison.Copyright

Modeling and experimental parameter identification of a multicopter via a compound pendulum test rig

In this paper, a method to identify parameters of a multicopter is proposed via a compound pendulum test rig and data from an optical position tracking system. Moments of inertia and thrust parameters of a hexacopter are evaluated. In addition, a specific method is introduced to identify the torque by a propeller using a floating test bed. Then, nonlinear dynamic model is derived based on the obtained parameters. To verify the identification method, simulation results using the nonlinear model are compared with experimental results from flight tests.

Adaptive model predictive control in the IPA-SQP framework

In this paper, we propose an approach and a specific algorithm to integrate a parameter estimation with the receding horizon model predictive control. We derive this adaptive MPC algorithm based on the integrated perturbation analysis and sequential quadratic programming (IPA-SQP) framework. Previously this approach was exploited for repeated constrained optimization in MPC when the initial conditions change. It is now shown that a similar algorithm can be derived to perform MPC updates when model parameters change. The detailed algorithm derivation is presented, along with discussions on the performance and implementation. An example based on the nonlinear dynamics of an inverted pendulum on a cart is included to demonstrate the effectiveness of the proposed algorithm.

A tutorial overview of IPA-SQP approach for optimization of constrained nonlinear systems

This paper reviews the integrated perturbation analysis - sequential quadratic programming (IPA-SQP) approach. The IPA-SQP approach has been proposed to address computational challenges in nonlinear model predictive control (MPC) problems. This approach combines the complementary features of perturbation analysis and sequential quadratic programming in a unified framework. An overview of the IPA-SQP approach is provided, its methodological extension to adaptive MPC is discussed, and computational issues related to nonlinear MPC are discussed. Several successful applications of the IPA-SQP will also be highlighted. References to relevant literature are included.