Neuroadaptive integral robust control of visual quadrotor for tracking a moving object

Shao, Xingling; Liu, Ning; Wang, Zhuqing; Zhang, Wendong; Yang, Wei

doi:10.1016/j.ymssp.2019.106513

Cited by 46 publications

(18 citation statements)

References 31 publications

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“…Then (46) can be further rewritten in the following concise form (23) and DSC (27). For any bounded initial conditions satisfying − i _ρ i (0) < e pi (0) <¯ i ρ i (0), i = 1, 2, there always exist constants defined by (54), such that all the involved signals in MEMS gyroscope system converge to small residual sets and the prescribed tracking performance, i.e., − i _ρ i (t) < e pi (t) <¯ i ρ i (t) can be satisfied.…”

Section: Quantizedcontrol Designmentioning

confidence: 99%

“…Although most existing works [22]- [24] can realize high-accuracy identification of unknown disturbances by means of universal approximation of NNs, it is worth pointing out that almost all of them suffer from the problem of learning explosion. Inspired by the minimum-learning-parameter (MLP) technique in [26], [27] and [36], we employ MLP to reconstruct the lumped disturbances consisting of external disturbances, parametric uncertainties and coupling between two operation modes, which greatly reduces learning dimension, such that the computational load is remarkably eased without decreasing the identification accuracy. What's more, the application of DSC technique effectively eliminates the problem of term explosion caused by analytical differentiation, such that the feasibility of algorithm is notably improved.…”

Section: Introductionmentioning

confidence: 99%

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MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

Shao

Zhang

2020

IEEE Access

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In this paper, a minimum-learning-parameter (MLP) based neural control method is proposed for micro-electro-mechanical system (MEMS) gyroscope with prescribed performance and input quantization. For the first time, a logarithmic quantizer (LQ) is employed to generate smooth input control signal for MEMS gyroscope, which greatly reduces the communication data size as well as actuator bandwidth. To improve the performance of MEMS gyroscope in the presence of quantization error, a prescribed performance control scheme consisting of preselected performance boundaries and an error transformation is utilized, such that preselected transient and steady-state properties can be assured. In contrast to the neural control strategies subject to the issue of learning explosion, a MLP-based neural network (NN) is introduced to estimate the unknown uncertainties using the norm of neural weight. To eliminate the effect of quantization error induced by LQ, a robust quantized control is designed to further ensure the closed-loop system suffering from discontinuous dynamics with prescribed ultimately uniformly bounded (UUB) performance. In the end, a series of simulations are presented to validate the superiority of the proposed control methodology.

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Section: Quantizedcontrol Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

Shao

Zhang

2020

IEEE Access

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“…Remark 1: Compared to the hybrid tracking differentiator (HTD) [33], the STD presented in [29] preserves a fast and precise signal derivation capability, meanwhile the undesirable chattering phenomena can be also mitigated. Besides, unlike dynamics surface control (DSC) [42], [44], which requires a strict condition that the initial filtered state should be identical with that of the input signal to avoid the peaking phenomena, STD can obtain precise tracking and differential signal without the requirement of precise knowledge of initial input signal. Consequently, considering the peculiarity of MEMS gyroscope featured as fast dynamics, and to achieve signal derivation without causing extra performance deterioration, we employ STD in our recursive control design process.…”

Section: Control Designmentioning

confidence: 99%

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

2020

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This paper investigates an event-triggered output feedback control problem with guaranteed tracking performance for microelectromechanical system (MEMS) gyroscope subject to the lumped disturbances, including parameter uncertainties, coupling between driving mode and sensing mode, and unknown external disturbances. Firstly, to make the system output tracking errors evolve within the predetermined performance envelopes, a prescribed performance control (PPC) is employed in our controller design, such that a prescribed trajectory tracking with preselected settling time and steady-state tracking accuracy can be ensured. Besides, to simultaneously realize disturbance rejection and estimation of the immeasurable velocity state, linear extended state observers (LESOs) are designed to reconstruct the lumped disturbances and immeasurable velocity states for both MEMS gyroscope driving and sensing modes. In general, excessively energy and resource consumption are fundamentally neglected in most of the existing output feedback controllers for MEMS gyroscope, which will dramatically reduce the operational time of the close-loop system. To address this problem, we embed a fixed threshold event-triggered mechanism into the controller design procedure, such that the communication data size can be drastically reduced without sacrificing the tracking accuracy. Finally, to obtain the close-loop stability for MEMS gyroscope, robust control laws are designed to compensate for the measurement error and estimation error, meanwhile Zeno phenomena can be effectively eliminated. The simulation results and comparisons validate the effectiveness of the proposed scheme. INDEX TERMS Output feedback, event-triggered control, prescribed performance, MEMS gyroscope.

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“…[20], a randomly weighted cubature Kalman filter (RWCKF) is proposed by using randomly weighted factor instead of original factor to reduce the influence of the system noise. Compared with CKF, RWCKF has higher tracking accuracy for nonlinear systems, which provides a new way to solve the tracking problem of nonlinear maneuvering targets [21][22][23]. However, both CKF and RWCKF have poor robustness and accuracy when the target state changes suddenly or the model matching is not accurate.…”

Section: Introductionmentioning

confidence: 99%

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

Jin

Guo

2020

IEEE Access

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In this paper, an improved interactive multiple model adaptive strong tracking random weighted cubature Kalman filter (IIMM-ASTRWCKF) algorithm is developed to overcome the low tracking accuracy and easy divergence when dealing with complex maneuvering situations. The algorithm is improved in two aspects: On the one hand, ASTRWCKF is used as the sub filter of IMM algorithm to filter different motion models. By introducing the random weight factor to replace the original weight factor, the accuracy of the algorithm is improved. At the same time, the adaptive strong tracking filter is added to update the prediction covariance matrix and noise covariance matrix for the stability of the algorithm. On the other hand, this algorithm proposes a new method to improve the probability conversion accuracy of IMM by adding time-varying factor to adjust Markov probability transfer matrix. Compared with the performance of IMM-CKF and in dealing with maneuvering problems, IIMM-ASTRWCKF algorithm has better tracking accuracy in solving maneuvering problem.

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Neuroadaptive integral robust control of visual quadrotor for tracking a moving object

Cited by 46 publications

References 31 publications

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

MLP-Based Neural Guaranteed Performance Control for MEMS Gyroscope With Logarithmic Quantizer

Event-Triggered Output Feedback Control for MEMS Gyroscope With Prescribed Performance

Combination of IMM Algorithm and ASTRWCKF for Maneuvering Target Tracking

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