Model-Predictive Dynamic Control Allocation Scheme for Reentry Vehicles

Luo, Yu; Serrani, Andrea; Yurkovich, Stephen; Oppenheimer, Michael W.; Doman, David B.

doi:10.2514/1.25473

Cited by 93 publications

(53 citation statements)

References 27 publications

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“…An integrated approach to flight control and control allocation design is investigated in (Jung, Lowenberg & Jones 2006), while (Bosković & Mehra 2002) describes two control allocation methods that adapt the weight matrices of a pseudoinverse like control allocation law in order to avoid saturation and rate constraints. Control allocation methods that explicitly take into account linear actuator dynamics have been proposed, using linear constrained MPC, (Luo et al 2004, Luo et al 2005, Luo, Serrani, Yurkovich, Oppenheimer & Doman 2007, or optimization in the framework of linear matrix inequalities (LMIs), (Kishore, Sen, Ray & Ghoshal 2008). In order to reduce effective time delay due to actuator rate limitations in case of quickly changing commanded moments, and thereby reduce the risk of pilot induced oscillations, (Yildiz & Kolmanovsky 2011) proposed and studied a control allocation method that also penalizes the difference between the time-derivatives of the commanded and allocated moments.…”

Section: Aircraftmentioning

confidence: 99%

Control allocation—A survey

2013

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The control algorithm hierarchy of motion control for over-actuated mechanical systems with a redundant set of effectors and actuators commonly includes three levels. First, a high-level motion control algorithm commands a vector of virtual control efforts (i.e. forces and moments) in order to meet the overall motion control objectives. Second, a control allocation algorithm coordinates the different effectors such that they together produce the desired virtual control efforts, if possible. Third, low-level control algorithms may be used to control each individual effector via its actuators. Control allocation offers the advantage of a modular design where the high-level motion control algorithm can be designed without detailed knowledge about the effectors and actuators. Important issues such as input saturation and rate constraints, actuator and effector fault tolerance, and meeting secondary objectives such as power efficiency and tear-and-wear minimization are handled within the control allocation algorithm. The objective of the present paper is to survey control allocation algorithms, motivated by the rapidly growing range of applications that have expanded from the aerospace and maritime industries, where control allocation has its roots, to automotive, mechatronics, and other industries. The survey classifies the different algorithms according to two main classes based on the use of linear or nonlinear models, respectively. The presence of physical constraints (e.g input saturation and rate constraints), operational constraints and secondary objectives makes optimization-based design a powerful approach. The simplest formulations allow explicit solutions to be computed using numerical linear algebra in combination with some logic and engineering solutions, while the more challenging formulations with nonlinear models or complex constraints and objectives call for iterative numerical optimization procedures. Experiences using the different methods in aerospace, maritime, automotive and other application areas are discussed. The paper ends with some perspectives on new applications and theoretical challenges.

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Section: Aircraftmentioning

confidence: 99%

Control allocation—A survey

2013

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“…In order to solve the problem of energy management and power allocation in steady-state conditions, domestic and foreign scholars have done much research [3][4][5][6]. In order to solve the problem of coordination control of the engine and the motors in dynamic conditions, domestic and foreign scholars have also done some research [7][8][9]. However, most of the coordination control research focus on how to reduce the torque ripple in the mode switching process.…”

Section: Introductionmentioning

confidence: 99%

Research on Fuel Economy Aimed Coordination Control Strategy for the Dual-mode Hybrid System under Dynamic Conditions

Zhao¹,

Zhao²,

Huang³

et al. 2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-A simulation of dynamic conditions was carried out based on the energy management strategy of "multi-objective power flow optimization based on engine optimal fuel economy curve". In dynamic conditions, without considering the response time of the adjustment of engine speed and engine torque, the motor torque loads too fast, the speed control of the engine and the torque control of the motors are not coordinated, which makes the accuracy and stability of dynamic condition control poor. In order to solve the incoordination problem in dynamic conditions, the coordination control strategy of "The target motor torque signal is subjected to first-order hysteresis treatment + The target engine speed signal is subjected to slope control treatment" was proposed, and the simulation research was carried out. The simulation results show that the coordination control strategy can effectively improve the tracking of the optimal fuel economy curve, improve the fuel economy performance of the engine, and improve the accuracy and stability of the dynamic condition control.

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“…With the analytical expressions for the gradient and Hessian derived in the previous section any second order optimization method, such as a sequential quadratic programming approach [32,33], can be applied to solve the control allocation problem. The solver presented here is based on the Levenberg-Marquardt algorithm [34].…”

Section: Strategy 3: Nonlinear Control Allocationmentioning

confidence: 99%

Nonlinear Multivariate Spline-Based Control Allocation for High-Performance Aircraft

Tol

Visser

Kampen

et al. 2014

Journal of Guidance, Control, and Dynamics

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High performance flight control systems based on the nonlinear dynamic inversion (NDI) principle require highly accurate models of aircraft aerodynamics. In general, the accuracy of the internal model determines to what degree the system nonlinearities can be canceled; the more accurate the model, the better the cancellation, and with that, the higher the performance of the controller. In this paper a new control system is presented that combines NDI with multivariate simplex spline based control allocation. We present three control allocation strategies which use novel expressions for the analytical Jacobian and Hessian of the multivariate spline models. Multivariate simplex splines have a higher approximation power than ordinary polynomial models, and are capable of accurately modeling nonlinear aerodynamics over the entire flight envelope of an aircraft. This new method, indicated as SNDI, is applied to control a high performance aircraft (F-16) with a large flight envelope. The simulation results indicate that the SNDI controller can achieve feedback linearization throughout the entire flight envelope, leading to a significant increase in tracking performance compared to ordinary polynomial based NDI.

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Model-Predictive Dynamic Control Allocation Scheme for Reentry Vehicles

Cited by 93 publications

References 27 publications

Control allocation—A survey

Control allocation—A survey

Research on Fuel Economy Aimed Coordination Control Strategy for the Dual-mode Hybrid System under Dynamic Conditions

Nonlinear Multivariate Spline-Based Control Allocation for High-Performance Aircraft

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