Adaptive backstepping radial basis function neural network controller design for a Mars lander during the powered descent phase

Kang, Sang-Wook; Bang, Hyochoong; Lee, Sang-Ryool

doi:10.1177/0954410017712537

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Moreover, the artificial neural network (ANN) is originated from the biological network, which is one kind of dynamic complex networks [15][16][17] closely related to the graph theory. Due to the universal approximation capability, the ANN is widely used as an approximator of unknown nonlinear function in the controller design [16,[18][19][20][21][22][23]. As a result, the tracking performances of the uncertain nonlinear system have been improved by using the ANN to compensate the model uncertainties.…”

Section: Introductionmentioning

confidence: 99%

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

Song

et al. 2022

Complexity

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Aerial work platform is a kind of engineering vehicle which is used for hoisting personnel to the appointed place for maintenance or installation. Based on the dynamics model considering the flexible deformation existing in the arm system of folding-boom aerial platform vehicle, this study presents a NN-based backstepping controller used for trajectory tracking control of work platform. The proposed controller can reduce tracking error of work platform and suppress the vibration simultaneously by using the RBF neural network system to compensate model uncertainties and disturbances. Furthermore, we prove that the whole system is stable and convergent by Lyapunov stability theorem. In addition, we give the simulation results which show that the good control performance of the designed controller for trajectory tracking and vibration inhabiting of work platform in the case of model uncertainties.

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Section: Introductionmentioning

confidence: 99%

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

Song

et al. 2022

Complexity

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“…[14][15][16] Considering the nonlinearity of the AMB system, coupled multibody dynamics, and the imbalance of large but uncertain payload, a high-precision control method of multibody spacecraft is derived after dynamical model proposed. Some nonlinear control methods have been studied in the literature for the nonlinear and coupled system, such as robust control, 17,18 sliding mode control, 19,13 backstepping control, [20][21][22][23][24] and iterative learning control. 25,26 These methods are investigated for attitude and AMB system control.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and adaptive backstepping control of multibody spacecraft connected with active magnetic bearing

Zhao

Geng

Tao

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, to realize the high-resolution observation mission, a new type of multibody optical spacecraft connected with active magnetic bearing (AMB) is introduced. Then, to address the attitude control problem facing high-precision observation for multibody spacecraft, the dynamic model of the multibody system is developed and an adaptive backstepping controller (ABC) is subsequently proposed. First, a high-precision electromagnetic force model of AMB is developed. Different from traditional models that only consider rotor position and current, the relative attitude between rotor and platform is considered. Then, based on the AMB model, the dynamic and kinematic model of multibody spacecraft is derived. Additionally, considering the electromagnetic bearing is unstable statically, an ABC method is proposed. The stability of the closed-loop system is guaranteed by the Lyapunov theorem. Finally, to indicate the effectiveness of the proposed method, some numerical simulations of comparison with the iterative learning control (ILC) method are performed. As indicated by the simulation results, the ABC is capable of eliminating periodic deviation, and it is more effective than the ILC in solving the control problem caused by periodic disturbance.

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“…Similarly, in [4], the powered descent phase of Mars is focused on. An adaptive neural network approach is used to precisely track the desired reference.…”

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confidence: 99%

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

et al. 2021

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It is imperative to find new places other than Earth for the survival of human beings. Mars could be the alternative to Earth in the future for us to live. In this context, many missions have been performed to examine the planet Mars. For such missions, planetary precision landing is a major challenge for the precise landing on Mars. Mars landing consists of different phases (hypersonic entry, parachute descent, terminal descent comprising gravity turn, and powered descent). However, the focus of this work is the powered descent phase of landing. Firstly, the main objective of this study is to minimize the landing error during the powered descend landing phase. The second objective involves constrained optimization in a predictive control framework for landing at non-cooperative sites. Different control algorithms like PID and LQR have been developed for the stated problem; however, the predictive control algorithm with constraint handling’s ability has not been explored much. This research discusses the Model Predictive Control algorithm for the powered descent phase of landing. Model Predictive Control (MPC) considers input/output constraints in the calculation of the control law and thus it is very useful for the stated problem as shown in the results. The main novelty of this work is the implementation of Explicit MPC, which gives comparatively less computational time than MPC. A comparison is done among MPC variants in terms of feasibility, constraints handling, and computational time. Moreover, other conventional control algorithms like PID and LQR are compared with the proposed predictive algorithm. These control algorithms are implemented on quadrotor UAV (which emulates the dynamics of a planetary lander) to verify the feasibility through simulations in MATLAB.

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Adaptive backstepping radial basis function neural network controller design for a Mars lander during the powered descent phase

Cited by 5 publications

References 26 publications

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

A Backstepping Controller with the RBF Neural Network for Folding‐Boom Aerial Work Platform

Dynamics and adaptive backstepping control of multibody spacecraft connected with active magnetic bearing

Performance Analysis of Mars-Powered Descent-Based Landing in a Constrained Optimization Control Framework

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