Dead-zone Nonlinearity Research Articles

Adaptive Dead Zone Compensation Control Method for Electro-Hydrostatic Actuators Under Low-Speed Conditions

AbstractThis study proposes an accurate dead zone compensation control method for electro-hydrostatic actuators (EHAs) under low-speed conditions. Specifically, the nonlinear dead zone characteristics under low-speed conditions are summarized based on numerous EHA experiments. An adaptive compensation function (ACF) is then constructed for the dead zone. Next, this study proposes an adaptive dead zone compensation control method for EHAs by integrating the ACF with a virtual decomposition controller (VDC) based on the established EHA model. The stability of the proposed control method is also proven. Finally, the proposed control method is verified using an EHA platform. The test results show that the dead zone trajectory tracking errors of EHAs are significantly reduced when combined with the ACF. Furthermore, since most EHAs are controlled by adjusting the motor speed, the method presented in this study is simpler and easier to use than methods that employ flow compensation.

Adaptive Fuzzy Position and Force Control for Cooperative Multimanipulators With System Uncertainties and Input Dead-Zone Nonlinearities

Adaptive Fuzzy Position and Force Control for Cooperative Multimanipulators With System Uncertainties and Input Dead-Zone Nonlinearities

Finite-Time Adaptive Control for Electro-Hydraulic Braking Gear Transmission Mechanism with Unilateral Dead Zone Nonlinearity

Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time adaptive control strategy was designed for unilateral dead zone nonlinearity. Initially, the unilateral dead zone nonlinearity was reformulated into a matched disturbance term and an unmatched disturbance term to reduce the adverse effects of disturbances, thereby enhancing system controllability. Then, the “complexity explosion” in the design of the control strategy was avoided by command filtering, and the design process of the controller was simplified. Furthermore, the finite-time control theory was employed to boost the system’s convergence speed, thereby enhancing control performance. In order to ensure the stability of the system under the dead zone disturbance, the unknown disturbance terms were estimated. The stability of the control strategy was validated through the finite-time stability theorem and the Lyapunov function. Eventually, simulations and hardware-in-the-loop (HIL) experiments validated the feasibility and availability of the finite-time adaptive control strategy.

Two-dimensional identification of time-varying batch nonlinear processes with input dead-zone nonlinearity

ABSTRACT In the time dimension, the batch process with input dead-zone nonlinearity often has the characteristics of short operation time and time-varying process parameters. A two-dimensional recursive least squares (2D-RLS) identification method for time-varying nonlinear systems with input dead zone is proposed. By mining system dynamic information from the time direction of the same batch and different batch directions, the identification parameters are updated. The proposed two-dimensional identification strategy converts the time-varying parameter estimation problem of time dimension into the equivalent time-invariant parameter estimation problem of batch dimension. Sufficient production data in batch dimension ensure that the proposed identification algorithm can eliminate the influence of random measurement noise. The identification strategy with unequal length of batch data is also given. The convergence of the proposed algorithm is analysed. Finally, numerical simulation and experimental tests are used to verify the effectiveness and superiority of the proposed algorithm.

Enhancing pressure control of low-friction pneumatic actuator with eddy current damper via valve dead-zone compensation

This article addresses the high-performance pressure control of a pneumatic actuator that incorporates an air-bearing piston and an eddy current damper (ECD). After optimizing the pneumatic actuator, achieving precise pressure control relies on maintaining an accurate and stable airflow through the control valve. A terminal sliding mode adaptive control method (TSMAC) is proposed, proficiently integrating considerations for dead zone nonlinearities in the proportional valve and the effects of unmodeled disturbances in the pneumatic actuator. Using the opening area of the proportional valve as the input to the pneumatic system greatly simplifies the control process and reduces the workload required for valve parameter calibration. The Lyapunov-based stability analysis demonstrates that excellent asymptotic output tracking can be achieved with low steady-state error simply by knowing the boundaries of the valve opening area. The control effect of the proposed control strategy is compared with an adaptive backstepping sliding mode control method based on a linearly fitted proportional valve model on an established constant pressure control test bench. Experimental results under various loads and operating conditions illustrate the effectiveness of the proposed approach.

Fixed-Time Adaptive Parameter Estimation for Hammerstein Systems Subject to Dead-Zone

The Hammerstein model has proven its ability for modeling many industrial servo systems, but the corresponding parameter estimation remains as a challenging problem, especially for the purpose of achieving fast convergence. This paper presents a fixed-time (FxT) adaptive parameter estimation scheme for Hammerstein systems with an asymmetric dead-zone to ensure the convergence of estimated parameters in fixed-time. A continuous piecewise linear (CPL) function is adopted to model the nonlinear dead-zone dynamics to remedy the use of intermediate variable. To avoid using the system state information, a K-filter is used to construct a relationship between the unknown system parameters and the input/output signals. Then a new adaptive law based on the parameter error and the FxT scheme is developed to online estimate the lumped unknown parameters and ensure the fixed-time convergence, where an online updated auxiliary variable of regressor is suggested to eliminate the impact of the regressor on the convergence performance. Both numerical simulations and experimental results are given to demonstrate the effectiveness of proposed methods.

Adaptive Fuzzy Sliding Mode Control and Dynamic Modeling of Flap Wheel Polishing Force Control System

Polishing force is one of the key process parameters in the polishing process of blisk blades, and its control accuracy will affect the surface quality and processing accuracy of the workpiece. The contact mechanism between the polishing surface and flap wheel was analyzed, and the calculation model of the polishing force and nonlinear dynamic model of the polishing force control system was established. Considering the influence of friction characteristics, parameter perturbation, and nonlinear dead zone on the control accuracy of the polishing force system, an adaptive fuzzy sliding mode controller (AFSMC) was designed. AFSMC uses a fuzzy system to adaptively approximate the nonlinear function terms in the sliding mode control law, adopts an exponential approach law in the switching control part of the sliding mode control (SMC), and designs the adaptive law for adjustable parameters in the fuzzy system based on the Lyapunov Theorem. Simulation and experimental results show that the designed AFSMC has a fast dynamic response, strong anti-interference ability, and high control accuracy, and it can reduce SMC high-frequency chatter. Polishing experiments show that compared with traditional PID, AFSMC can improve the form and position accuracy of the blade by 42% and reduce the surface roughness by 50%.

Adaptive neural network anti-disturbance control of a cable-driven flexible-joint-based underwater vehicle manipulator system with dead-zone input nonlinearities via multiple neural observers

To achieve the requirements of lightweight, low energy consumption, and low inertia of an underwater vehicle manipulator system, a cable-driven manipulator is installed on the underwater vehicle to form a cable-driven flexible-joint-based underwater vehicle manipulator system (CDFJ–UVMS). The CDFJ–UVMS is a complex nonlinear system subject to model uncertainties, complex marine environment disturbances, and actuator dead-zone nonlinearity. To design track controllers, the CDFJ–UVMS dynamics is divided into two parts: known and unknown. Subsequently, a radial basis function neural network is adopted to approximate the unknown nonlinearity. A neural network performance observer is constructed, whose estimation error is then used to design a novel neural disturbance observer (NDO) to estimate the total disturbance. Finally, an adaptive neural network control method is proposed for the CDFJ–UVMS based on the NDO, neural network compensator, and neural performance observer. The stability of the closed-loop system is analyzed using the Lyapunov method. The proposed control algorithm is applied to a CDFJ–UVMS with two cable-driven joints and compared with other control methods to show the effectiveness of the proposed control algorithm.

Deep Reinforcement Learning-Based Control for Asynchronous Motor-Actuated Triple Pendulum Crane Systems With Distributed Mass Payloads

For transporting large cargoes in practice, the triple pendulum hoisting pattern is widely utilized, where the distributed mass payload (DMP) is suspended under the hanger, the hanger is suspended on the hook, and the hook is connected with the hoisting mechanism. However, existing studies mainly focus on single pendulum and double pendulum hoisting patterns, and few of them pay attention to triple pendulum hoisting patterns. Compared with single pendulum and double pendulum cranes, triple pendulum cranes, which control 4 degrees of freedom (DoFs) with merely 1 control input, have a higher underactuation degree, with higher control difficulties. In addition, the nonlinear characteristics of alternating current (AC) asynchronous motors, such as unknown nonlinear dead zones and time delays, will degrade tracking accuracy and control performance. To solve the above problems, a model-free deep reinforcement learning-based trajectory planning method is proposed for triple pendulum DMP cranes. Furthermore, an identification model for AC asynchronous motors is established. On this basis, a trajectory tracking method including an adaptive dead zone compensation (ADC) and a modified Smith predictor (MSP) is proposed. Finally, experiments are carried out on a self-built 2-ton crane experimental platform, which is actuated by AC asynchronous motors. Hardware experiments demonstrate that triple pendulum cranes can accurately track the planned trajectory, and the DMP residual swing is effectively suppressed.

Advanced embedded generalized predictive controller based on fuzzy gain scheduling for agricultural sprayers with dead zone nonlinearities

Variable rate application of pesticides in agriculture can improve pest control and also increase food production. Nevertheless, incorrect spraying poses risks to the environment and human health, as well as may increase the total cost of production. Nowadays, it is quite known the importance of innovation in techniques and technologies to improve the spraying process in a variable rate application for pest control. This work presents a generalized predictive control (GPC) strategy to cope with nonlinearities. An extension of the stability analysis for constrained GPC controller for infinite horizons is also developed, which guarantees the stability of a fuzzy GPC for a limited variation of its tuning parameters λ and δ. Results show the usefulness in adding a fuzzy logic system, to cope with nonlinearities, leading to a conception of an advanced fuzzy GPC for a limited variation of its tuning parameters.

Adaptive Observer-based Funnel Heading Control of Surface Vessels with Rudder Actuator Nonlinearity

In this work, an adaptive state observer based on the strictly positive real (SPR) theory in combination with funnel control is proposed for heading control of surface vessels with uncertainty in the model and subject to rudder dead-zone actuator nonlinearity. The rudder command is designed to guarantee predetermined transient and steady-state tracking performances for both heading angle and filtered virtual control tracking errors. The unknown nonlinear function in the model is tackled using an adaptive neural network and an adaptive mechanism is proposed to cope with the unknown control gain. To further improve the performance, an adaptive continuous chattering-free robust structure is considered. Using SPR Lyapunov synthesis approach, not only all the adaptive laws are derived but also it is proved that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB). Simulation results validate the efficiency and applicability of the proposed method.

Neighbor event-triggered adaptive distributed control for multiagent systems with dead-zone inputs

<abstract><p>The paper focused on the distributed tracking problem for a specific class of multi-agent systems, characterized by bandwidth constraint and dead zone actuators, where the bandwidth limitations exist in neighbor agents and the dead zone nonlinearity refers to a generalized mathematical model. Initially, a series of event-triggered mechanisms with relative thresholds were established for neighbor agents, ensuring that control signals were transmitted only when necessary. Next, the generalized dead zone models were decomposed into two parts: indefinite terms with control coefficients and disturbance-like terms, resulting in unpredictability and damaging effects. Subsequently, based on the backstepping procedure, final consensus controllers with multiple polynomial compensators were constructed. These controllers offset the coupling coefficients caused by event-triggered mechanisms and dead zone non-smooth. Stability analysis was given to substantiate the theoretical correctness of this method and support the claim of Zeno behavior avoidance. Finally, simulation studies were performed for the feasibility of our proposed methodology.</p></abstract>

Observer Based Control for a Class of Systems with Output Deadzone Nonlinearity

Observer Based Control for a Class of Systems with Output Deadzone Nonlinearity

An Error Learning Scenario-Based Scheme to Quantized Identification in Wiener-Hammerstein Systems Subject to Deadzone Nonlinearity

An Error Learning Scenario-Based Scheme to Quantized Identification in Wiener-Hammerstein Systems Subject to Deadzone Nonlinearity

Adaptive fixed-time consensus control of nonlinear multiagent systems with dead-zone output

This paper proposes a fixed-time leader-follower consensus tracking control scheme for uncertain multiagent systems with unknown dead-zone output. First, a novel approximate dead-zone model was introduced to accurately represent the dead-zone phenomenon in the output channel of nonlinear multiagent systems. Furthermore, the model presented is a smooth function, which greatly simplifies the process of fusing it with the backstepping technique. Second, we present a Nussbaum function to compensate for the uncertain gain from the output dead-zone nonlinearity. Third, unlike previous works, we first consider practical fixed-time consensus issues for multiagent systems with output dead-zone. Finally, according to the fixed-time control theory, the tracking error of the multiagent systems converges to the region around zero within a fixed time, which is confirmed by the simulations.

Adaptive Inverse Compensation Fault-Tolerant Control for a Flexible Manipulator With Unknown Dead-Zone and Actuator Faults

This study presents an adaptive inverse compensation control of a flexible single-link manipulator with an unknown dead-zone and actuator faults. First, a dead-zone inverse model and a smooth inverse operator are constructed to address dead-zone nonlinearity. Second, an adaptive fault-tolerant control is utilized to compensate for the partial loss of the effectiveness of the actuator. Third, the coupling errors of the dead-zone and faults are effectively removed and resolved by introducing an estimate for unknown bounds. Then, the direct Lyapunov theory is used to guarantee uniformly ultimately bounded stability in the controlled system. Finally, the simulations and experiments demonstrate the efficiency of the presented method.

An adaptive bearing rigid formation control of multi-agent systems with nonlinear dead-zone inputs

In this paper, an adaptive bearing rigid formation control strategy for a class of nonlinear system with unknown dead-zone inputs and external disturbance is proposed. Firstly, the I-Type fuzzy system is used to approximate the unknown nonlinear dynamics of the formation model, and the approximation errors and unknown external disturbance are eliminated by the parameter adaptive estimation. Furthermore, the adaptive dynamic estimation algorithm is utilized to estimate and compensate the unknown dead-zone parameters, effectively suppressing the impact of dead-zone on formation system performance. Finally, the stability of the formation system is proved based on LaSalle’s invariance principle, and the effectiveness of the algorithm is verified by simulation results.

EHB Gear-Drive Symmetric Dead-Zone Finite-Time Adaptive Control

Intelligent driving vehicles require more accurate and stable braking control. Electrohydraulic braking (EHB) systems can better adapt to the development of autonomous driving technology. The gear transmission system plays a crucial role in EHB deceleration and torque increase mechanisms. However, its dead-zone nonlinearity poses challenges for EHB control. To address the position-control problem in the EHB gear transmission system, we propose a finite-time adaptive control method for the symmetrical dead zone. This approach combines adaptive control theory with finite-time control theory and designs parameter-updating laws for the unknown parameters in the system. Boundary estimates are introduced into the parameter-update laws and control laws to compensate for unknown disturbances. By adjusting the relevant parameters, the convergence rate can be improved, ensuring that errors converge within a specified range within a limited time. After modifying the parameter-updating laws and control laws, all closed-loop signals remain bounded. Finally, we validate the proposed control strategy through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate that the control strategy developed in this study achieves high tracking accuracy and stability even in the presence of dead zones, unknown parameters, and unknown interferences in the EHB gear-drive servo system.

Current harmonic suppression for PMSM driven system utilizing PI-DFT-RC controller

The permanent magnet synchronous motor (PMSM) driven by the two-level voltage inverters features some drawbacks, such as dead-zone time, nonlinearity and other factors, leading to high amount harmonics in the stator currents. Additionally, the current harmonics amplitude will be decayed with the increasing of the harmonic order, and the main current harmonic components can be illustrated as 6th control signal harmonics. To improve the current harmonics suppression efficiency and maintain the simplicity of the PMSM driven system, the relationships between traditional repetitive controller and bandpass filter are analysed firstly. And then, a proportional–integral​ discrete Fourier transform repetitive control (PI-DFT-RC) scheme is proposed through adopting independent band-pass filters of state frequencies, which can provide infinite gain in stated frequency. Secondly, the discrete periodic signals for RC controller are analysed, and the lead step for DFT-RC controller is also studied, which can increase the signal tracking accuracy of the driven system. Furthermore, the stability of the driven system used PI-DFT-RC controller is analysed through Lyapunov theory. Finally, the effectiveness of the proposed PI-DFT-RC controller is verified by simulation results.

Research on sparse identification method for aeroelastic dynamic response prediction

Nonlinear aeroelastic system has the characteristics of complex structure, difficult modeling and difficult calculation of dynamic response. For the analysis of nonlinear aeroelastic systems, model identification is a very attractive method. However, the models identified by traditional methods are often relatively complex and limited in scope of use, so it is necessary to develop an interpretable equivalent reduced model. In this paper, sparse regression method and sequential threshold least squares technique are used to establish sparse identification method for complex aeroelastic systems. This method has the ability to identify reduced models containing only required nonlinear terms through measurement data. Then, the sparse identification method is used to identify the binary wing with dead zone nonlinearity and cubic stiffness nonlinearity. The obtained model can provide rapid and accurate prediction of the response of the system according to the sensor measurement, and can also be used as an explicit surrogate model for aeroelastic optimization design, thus verifying the superiority of the proposed method.