Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization

Li, Zhijun; Xia, Yuanqing; Su, Chun‐Yi; Deng, Jun; Fu, Jun; He, Wei

doi:10.1109/tnnls.2014.2345734

Cited by 91 publications

(60 citation statements)

References 34 publications

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“…We use CVX to solve (5), the exact throughput solutions are shown in Figure 2. Then we assume that the future link capacity is unknown for each time slot, and use CVX to calculate the MPC solutions from (7), which are shown in Figure 3. It can be seen that the heuristic MPC solution with rough estimation of future link capacities is very close to the exact solution with exact values of all link capacities.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…At first, we combine the constraints (7b) and (7c): We use a new vector C t to express the total link capacities (include the exact current link capacities C k and the predicted future link capacities C l ), ie, C t = [C k ;Ĉ l ]. Then the problem (7) becomes to the following formulation just like problem 5:…”

Section: Heuristic Policymentioning

confidence: 99%

“…Model predictive control (MPC) 2 is an advanced method of process control for describing and modeling dynamic systems, which has been in use in the industrial control, 3,4 power supply, 5,6 and intelligent control. 7,8 In recent years, it has also been used in system balancing models. The main advantage of MPC is the fact that it allows the current time slot to be optimized, while keeping future time slots in account.…”

Section: Introductionmentioning

confidence: 99%

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An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

Zheng

Zhao

Tan

et al. 2017

Satell Commun Network

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Summary In this paper, we consider the problem of maximizing the throughput of remote sensing satellite networks. In such networks, the link capacities and routing matrices are varying over time. We propose a convex optimization‐based analytical framework for the problem. To maximize the network throughput under the premise of satisfying the delay constraint, we formulate the data transmission schedule into an optimization problem aiming at maximizing the delay‐constrained throughput. Considering the fact that the future link capacities cannot be accurately known in the actual situation, we propose a heuristic and distributed framework on the basis of model predictive control for approximately solving the problem. This framework can be used to design remote sensing data transmission schedules under various scenarios. We adopt a generic example to simulate and analyze the framework. The simulation results show that the proposed analytic framework can obtain the approximate solution that is very close to the optimal solution by solving the convex optimization problem step‐by‐step. The heuristic algorithm based on model predictive control can obtain the approximate solution, which is very close to the optimal solution in distributed scenario.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: Heuristic Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

Zheng

Zhao

Tan

et al. 2017

Satell Commun Network

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“…Several optimal controls for robots have been realized using neural network optimization [23][24][25]. In this paper, considering the physical constraints on biped motions, a novel neurodynamics-based energy-efficiency optimization controller is developed.…”

Section: Introductionmentioning

confidence: 99%

Constrained Quadratic Programming and Neurodynamics-Based Solver for Energy Optimization of Biped Walking Robots

Wang

Chen

Jiang

et al. 2017

Mathematical Problems in Engineering

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The application of biped robots is always trapped by their high energy consumption. This paper makes a contribution by optimizing the joint torques to decrease the energy consumption without changing the biped gaits. In this work, a constrained quadratic programming (QP) problem for energy optimization is formulated. A neurodynamics-based solver is presented to solve the QP problem. Differing from the existing literatures, the proposed neurodynamics-based energy optimization (NEO) strategy minimizes the energy consumption and guarantees the following three important constraints simultaneously: (i) the force-moment equilibrium equation of biped robots, (ii) frictions applied by each leg on the ground to hold the biped robot without slippage and tipping over, and (iii) physical limits of the motors. Simulations demonstrate that the proposed strategy is effective for energyefficient biped walking.

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“…Fortunately, neural network optimization emerges as a promising approach for dealing with heavy online computational burden. There are a number of works in which neural networks are applied for constrained optimization problems for real-time applications [24], [25]. In [25], for the multilegged robot, a control approach using primal dual neural-network is developed, with consideration of both the ground contact forces for each leg and the physical limit of the joint torques.…”

mentioning

confidence: 99%

Model Predictive Control of Nonholonomic Chained Systems Using General Projection Neural Networks Optimization

Xiao

Yang

et al. 2015

IEEE Trans. Syst. Man Cybern, Syst.

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132

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Abstract-In this paper, a class of nonholonomic chained systems is first converted into two subsystems, and then an explicit exponential decaying term is introduced into the input of the first subsystem to guarantee its controllability. After a state-scaling transformation, a model predictive control (MPC) scheme is proposed for the nonholonomic chained systems. The proposed MPC scheme employs a general projection neural network (GPN) to iteratively solve a quadratic programming (QP) problem over a finite receding horizon. The GPN employed in this paper is proved to be stable in the sense of Lyapunov, and its global convergence to the optimal solution is guaranteed for the reformulated QP. A simulation study is performed to show stable and convergent control performance under the proposed method, irrespective of whether the control input u 1 vanishes or not.Index Terms-General projection neural networks (GPNs), model predictive control (MPC), nonholonomic chained systems, scaling transformation.

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Missile Guidance Law Based on Robust Model Predictive Control Using Neural-Network Optimization

Cited by 91 publications

References 34 publications

An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

Constrained Quadratic Programming and Neurodynamics-Based Solver for Energy Optimization of Biped Walking Robots

Model Predictive Control of Nonholonomic Chained Systems Using General Projection Neural Networks Optimization

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