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Using the linear approach to design a controller is still prevalent. The state feedback control (SFC) is applied in this paper to improve the dynamic response of permanent magnet synchronous machine (PMSM) speed regulation systems. First, a third-order augmented system is constructed for the reason that a higher-order system has better disturbance rejection. It can be found through analysis and comparison that the order of the proposed speed controller is increased. The parameters of SFC are selected by utilizing the linear quadratic regulator (LQR), and the influence of matrix Q on dynamic performance is detailed through the Bode diagram. Additionally, considering parametric uncertainties and unmodeled dynamics, a disturbance observer (DOB) using the Luenberger observer is designed to further boost anti-disturbance performance. Finally, plenty of experimental results verify the effectiveness of the proposed methods.
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This paper addresses the predefined-time distributed optimization of nonlinear multi-agent system using a hierarchical control approach. Considering unknown nonlinear functions and external disturbances, we propose a two-layer hierarchical control framework. At the first layer, a predefined-time distributed estimator is employed to produce optimal consensus trajectories. At the second layer, a neural-network-based predefined-time disturbance observer is introduced to estimate the disturbance, with neural networks used to approximate the unknown nonlinear functions. A neural-network-based anti-disturbance sliding mode control mechanism is presented to ensure that the system trajectories can track the optimal trajectories within a predefined time. The feasibility of this hierarchical control framework is verified by utilizing the Lyapunov method. Numerical simulations are conducted separately using models of robotic arms and mobile robots to validate the effectiveness of the proposed method.
Assuntos
Algoritmos , Simulação por Computador , Redes Neurais de Computação , Dinâmica não Linear , Robótica , Fatores de TempoRESUMO
We propose a reliability-based anti-disturbance control (RADC) method for systems with parametric stochastic uncertainty based on the linear matrix inequality (LMI) and the limit state function. Differing from the existing anti-disturbance control, the parametric stochastic uncertainty is considered in both the concerned system and the exogenous disturbance system. With this consideration, the condition for system stability and performance robustness is described by a stochastic LMI which holds with a certain probability (reliability). Through the limit state function method, the stochastic LMI is subtly transformed into two probabilistic LMIs for two different cases. The proposed probabilistic LMIs contain two probabilistic parameters of reliability indexes that quantify the effect of parametric stochastic uncertainty. At different prescribed reliability indexes, controllers with different reliability can be flexibly and reliably designed. Two illustrative examples with Monte-Carlo verification are presented to demonstrate the feasibility and effectiveness of the proposed RADC method.
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This paper proposes a composite sliding mode control (SMC) to optimize the tracking performance and the anti-disturbance performance of permanent magnet synchronous machine (PMSM) speed regulation systems. The differential term in the control law can magnify the measurement noise, resulting in more discontinuity. To filter out the high frequency noise and make the control law smoother, the first-order differentiator (FOD) is employed to estimate the speed error and its derivative. Since the feedforward compensation can improve the robustness of the system, a disturbance observer (DOB) based on the sliding mode observer (SMO) is designed to reinforce the dynamic performance under disturbance variation. Under the effect of the feedforward compensation, chattering can be further weakened by decreasing the switching gain appropriately. Finally, the effectiveness of the proposed methods is confirmed by various experimental results.
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Stretchable ionic hydrogels with superior all-round properties that can detect multimodal sensations with excellent discriminability and robustness against external disturbances are highly required for artificial electronic skinapplications. However, some critical material parameters exhibit intrinsic tradeoffs with each other for most ionic hydrogels. Here, a microphase-separated hydrogel is demonstrated by combining three strategies: (1) using of a low crosslinker/monomer ratio to obtain highly entangled polymer chains as the first network; (2) the introduction of zwitterions into the first network; (3) the synthesis of an ultrasoft polyelectrolyte as the second network. This all-round elastic ionic hydrogel exhibits a low Young's modulus (< 60 kPa), large stretchability (> 900%), high resilience (> 95%), unique strain-stiffening behavior, excellent fatigue tolerance, high ionic conductivity (> 2.0 S mâ»1), and anti-freezing capability, which have not been achieved before. These properties allow the ionic hydrogel to operate as a stretchable multimodal sensor that can detect and decouple multiple stimuli (temperature, pressure, and proximity) with excellent discriminability, high sensitivity, and strong sensing-robustness against strains or temperature perturbations. The ionic hydrogel sensor exhibits great potential for intelligent electronic skin applications such as reliable health monitoring and accurate object identification.
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A permanent magnet linear synchronous motor (PMLSM) with discontinuous driving coils potentially applied in the underground pipeline freight delivery system is introduced in this article. Firstly, the detailed structure of the PMLSM with discontinuous driving coils is designed and presented, and the dynamic models of the PMLSM during three different processes are developed. In addition, based on the proportional-integral (PI) and the adaptive disturbance observer (DOB), a multi-loop control strategy is designed for the PMLSM with discontinuous driving coils. The critical performances of PMLSM, such as the speed precision and anti-disturbance ability, are testified in the experiment, and the results validate that the multi-loop control model with adaptive DOB used in the PMLSM with discontinuous driving coils has better speed precision than the PI+PI model by suppressing the detent force.
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The bio-brain presents robustness function to external stimulus through its self-adaptive regulation and neural information processing. Drawing from the advantages of the bio-brain to investigate the robustness function of a spiking neural network (SNN) is conducive to the advance of brain-like intelligence. However, the current brain-like model is insufficient in biological rationality. In addition, its evaluation method for anti-disturbance performance is inadequate. To explore the self-adaptive regulation performance of a brain-like model with more biological rationality under external noise, a scale-free spiking neural network(SFSNN) is constructed in this study. Then, the anti-disturbance ability of the SFSNN against impulse noise is investigated, and the anti-disturbance mechanism is further discussed. Our simulation results indicate that: (i) our SFSNN has anti-disturbance ability against impulse noise, and the high-clustering SFSNN outperforms the low-clustering SFSNN in terms of anti-disturbance performance. (ii) The neural information processing in the SFSNN under external noise is clarified, which is a dynamic chain effect of the neuron firing, the synaptic weight, and the topological characteristic. (iii) Our discussion hints that an intrinsic factor of the anti-disturbance ability is the synaptic plasticity, and the network topology is a factor that affects the anti-disturbance ability at the level of performance.
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Autonomous Underwater Vehicle are widely used in industries, such as marine resource exploitation and fish farming, but they are often subject to a large amount of interference which cause poor control stability, while performing their tasks. A decoupling control algorithm is proposed and A single control volume-single attitude angle model is constructed for the problem of severe coupling in the control system of attitude of six degrees of freedom Autonomous Underwater Vehicle. Aiming at the problem of complex Active Disturbance Rejection Control (ADRC) adjustment relying on manual experience, the PSO-ADRC algorithm is proposed to realize the automatic adjustment of its parameters, which improves the anti-interference ability and control accuracy of Autonomous Underwater Vehicle in dynamic environment. The anti-interference ability and control accuracy of the method were verified through experiments.
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Aiming at the problem that agricultural quadrotor UAV is easily disturbed in ultra-low altitude phenotype remote sensing and precision hovering of spraying, an adaptive composite anti-disturbance attitude controller is proposed for ground effect and propeller failure disturbances rejection. The adaptive composite disturbance rejection control (ACDRC) is composed of active disturbance rejection control (ADRC) and disturbance observer (DO) based on nominal inverse model, which is used to estimate wind disturbance, payload disturbance and propeller failure disturbance in real time. For the bandwidth tuning of the extended state observer (ESO), an online tuning method based on iterative learning control (ILC) is proposed to realize the adaptive extended state observer (ESO). And the stability of the composite anti-disturbance controller is analyzed. In the experiments, the wind disturbance experiments under the side-down flow and the horizontal flow, the failure experiments under the single propeller failure and twin propeller failure, and the composite disturbances experiments under the simultaneous action of the wind disturbance, propeller failure and payload disturbance are carried out. The experimental results show that under wind disturbance, the anti-disturbance performance of ACDRC is increased by 82.5%; under the disturbance of propeller fault, the anti-disturbance performance of ACDRC is increased by 60%; under the composite disturbance, the anti-disturbance performance of ACDRC is increased by 50%. Finally, the effectiveness of ACDRC is further verified in vegetable and cotton fields.
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Aimed at the objective of anti-disturbance and reducing data transmission, this article discusses a novel dynamic neural network (DNN) modeling-based anti-disturbance control for a system under the framework of an event trigger. In order to describe dynamical characteristics of irregular disturbances, exogenous DNN disturbance models with different excitation functions are firstly introduced. A novel disturbance observer-based adaptive regulation (DOBAR) method is then proposed, which can capture the dynamics of unknown disturbance. By integrating the augmented triggering condition and the convex optimization method, an effective anti-disturbance controller is then found to guarantee the system stability and the convergence of the output. Meanwhile, both the augmented state and the system output are constrained within given regions. Moreover, the Zeno phenomenon existing in event-triggered mechanisms is also successfully avoided. Simulation results for the A4D aircraft models are shown to verify the availability of the algorithm.
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The paper centers on the H∞ finite-time (FT) composite anti-disturbance switching control (ADSC) issue for switched systems (SSs) suffered from multiple disturbances, the unavailable modeled disturbance and the available un-modeled disturbance. The aim is to arrive at the multiple disturbance suppression in FT. A switching estimator is firstly constructed to observe the modeled disturbance combined with the unavailable system state. A switching controller and a switching law are then co-established to restrain the multiple disturbances in the SSs. A condition is attained to ensure that the H∞ FT composite ADSC issue for the SSs is solvable, even the issue is not solvable for subsystems. A verification example is served to text the reasonability of the developed theory.
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This paper develop a novel composite anti-disturbance control (CADC) method for a class of stochastic nonlinear systems in which input saturation and multiple heterogeneous disturbances occur. The disturbances include additive white noise, multiplicative white noise and modeled disturbances with coupling of energy-limited white noise and state signals. The input saturation item of system is approximated by the polytopic bounding method. In order to estimate the modeled disturbances, a novel stochastic saturating disturbance observer (SSDO) is designed. The CADC scheme is presented by integrating SSDO with convex hull expression of saturated linear feedback law, such that the superior anti-disturbance performance to be achieved while overcoming the effects of input saturation. Finally, the validity and usability of the developed scheme are testified by the numerical simulation and practical application example.
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This paper presents an anti-disturbance speed control of low-speed high-torque permanent magnet synchronous motor (PMSM) based on the second-order non-singular terminal sliding mode load observer. According to the coordinate transformation theory, the mathematical model of PMSM is established. Subsequently, the second-order non-singular terminal sliding mode observer (SNTSMO) is designed to observe the changes of load disturbance in the PMSM system. The SNTSMO combines the advantages of both high-order sliding mode and non-singular terminal sliding mode to achieve the fast convergence and no chattering. Next, the sliding mode controller (SMC) is designed to achieve speed loop control of PMSM. Then, the anti-disturbance compound speed controller is established on the basis of SMC and SNTSMO, wherein the feed-forward compensation is used to reduce the disturbance from the load. Finally, the numerical simulations and experiments are presented according to the schematic diagram of the designed compound speed controller of PMSM. The results demonstrate that the designed SNTSMO can precisely estimate the load disturbance and suppress the effects of buffeting in the traditional sliding mode observer (SMO). Additionally, the designed compound speed controller of PMSM can achieve smooth speed control in the presence of load disturbance, achieve the purpose of anti-disturbance speed control and further improve the robustness of the control system.
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In the aerospace field, compact optoelectronic platforms (COPs) are being increasingly equipped on unmanned aircraft systems (UAS). They assist UAS in a range of mission-specific tasks such as disaster relief, crop testing, and firefighting. However, the strict constraint of structure space makes COPs subject to multi-source disturbances. The application of a low-cost and low-precision sensor also affects the system control performance. A composite hierarchical anti-disturbance control (CHADC) scheme with multisensor fusion is explored herein to improve the motion performance of COPs in the presence of internal and external disturbances. Composite disturbance modelling combining the characteristic of wire-wound moment is presented in the inner layer. The adaptive mutation differential evolution algorithm is implemented to identify and optimise the model parameters of the system internal disturbance. Inverse model compensation and finite-time nonlinear disturbance observer are then constructed to compensate for multiple disturbances. A non-singular terminal sliding mode controller is constructed to attenuate disturbance in the outer layer. A stability analysis for both the composite disturbance compensator and the closed-loop system is provided using Lyapunov stability arguments. The phase lag-free low-pass filter is implemented to interfuse multiple sensors with different order information and achieve satisfactory noise suppression without phase lag. Experimental results demonstrate that the proposed CHADC strategy with a higher-quality signal has an improved performance for multi-source disturbance compensation.
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This study proposes anti-disturbance dynamic surface control scheme for nonlinear strict-feedback systems subjected simultaneously to unknown asymmetric dead-zone nonlinearity, unmatched external disturbance and uncertain nonlinear dynamics. Radial basis function-neural network (RBF-NN) is invoked to approximate the uncertain dynamics of the system, and the dead-zone nonlinearity is represented as a time-varying system with a bounded disturbance. The nonlinear disturbance observer (NDO) is proposed to estimate the unmatched external disturbance which further will be used to compensate the effect of the disturbance. Then, by integrating RBF-NN, NDO and dynamic surface control (DSC) approaches, the proposed anti-disturbance control scheme is designed. Stability analysis of the closed-loop system shows that all signals of the closed-loop system are semi-globally uniformly ultimately bounded and the tracking error can be made arbitrarily small by proper selection of the design parameters. In comparison with the existing methods, the proposed scheme deals with the unmatched external disturbance, uncertain dynamics and unknown asymmetric dead-zone nonlinearity, simultaneously; it avoids the "explosion of complexity" problem and develops the simple control law without singularity concern. Furthermore, some imposed assumptions to the dead-zone input and disturbances are relaxed. Simulation and comparison results verify the effectiveness of the proposed approach.
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The problem of anti-disturbance control has been an eternal topic along with the development of the control theory. However, most methodologies can only deal with systems subject to a single equivalent disturbance which was merged by various types of uncertainties. In this paper, a review on anti-disturbance control is presented for systems with multiple disturbances. First, the classical control methods are briefly reviewed for disturbance attenuation or rejection problems. Then, recent advances in disturbance observer based control (DOBC) theory are introduced and especially, the composite hierarchical anti-disturbance control (CHADC) is firstly addressed. A comparison of different approaches is briefly carried out. Finally, focuses in the field on the current research are also addressed with emphasis on the practical application of the techniques.
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Transducer is applied to automatic control system extensively,but it causes certain electromagnetic interference to control circuit and electricity equipments around it and sometimes may result in wrong action.This paper carries on the experimental analysis and formulates some anti-disturbance measures.
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The working condition of the high frequency electronic knife is quite complicated.How to ensure it to run unfailingly is an important demand in the design of the control system of the high frequency electronic knife.This paper introduces the anti-disturbance method and measure of the control system of the high frequency elec-tronic knife briefly.The hardware measure and software method are introduced mainly.In the aspect of hardware,the techniques include choosing microprocessor correctly,distributing printed-plank area reasonably,noise shield,etc.IN software,there are such as establishing the trap of program,designing program for Watchdog,digital filtering,Owing to adopting above anti-disturbance techniques,the safety and reliability of the whole system are improved.