Model-Free Tracking Control via Adaptive Dynamic Sliding Mode Control With Application To Robotic Systems

Adaptive dynamic sliding mode control strategy for the overload control of anti-ship missiles using Lyapunov stability theory is proposed. By using a function augmented sliding hyperplane, it is guaranteed that the output tracking error converges to zero in finite time. In addition, an adaptive method is adopted to attenuate the uncertainties. The simulation shows validity of the proposed method.

Speed tracking control of pneumatic motor servo systems using observation-based adaptive dynamic sliding-mode control

Extended state observer-based adaptive dynamic sliding mode control for power level of nuclear power plant

Adaptive Dynamic Sliding Mode Control with Fuzzy Logic and Neural Network for Mecanum Wheels Mobile Robots

Highway on-ramp merging zones constitute critical bottlenecks for traffic flow optimization. Vehicle-to-everything (V2X) communication and connected automated vehicle (CAV) provide new solutions improving merging efficiency and safety. In this study, the virtual mapping method is used to organize vehicles in the merging area into in-lane and cross-lane platoons. To better accommodate the control objectives and communication conditions of different platoons, this study designs separate in-lane and cross-lane control models and demonstrates their string stability. In the in-lane control model, the safety potential field is integrated into the information weights, effectively mitigating the influence of unsafe vehicles on the following vehicles. In the cross-lane control model, the Variable Time Headway Policy Based on Adaptive Dynamical Sliding Mode Control (ADSMC-VTH Policy) is proposed, allowing vehicles to flexibly adjust headway when facing virtually mapped vehicles that suddenly appear. This study explores the application of the proposed strategy in a mixed traffic environment through a preemptive control method. Numerical simulations show that the proposed strategy enhances stability, efficiency, and safety compared to baseline methods, achieving 23.5% and 26.8% average reductions in fuel consumption and emissions in pure CAV and mixed traffic environments, respectively.

Robust tracking control for vehicle electronic throttle using adaptive dynamic sliding mode and extended state observer

This brief proposes an adaptive sliding mode control law for an underactuated vessel via parameter estimation to follow a desired path. An adaptive continuous PI sliding mode scheme is devoted to enhancing the robustness of the control system and reducing the system oscillation in presence of unknown plant parameters and environmental disturbances. To address model uncertainties and unknown environmental disturbances, an adaptive parameters estimation and an upper bound estimation scheme are incorporated with the control scheme. In addition, a novel virtual control approach based on backstepping and adaptive dynamical sliding mode control is designed to relax the assumption of passive boundedness in sway motion. Under this kind of consideration, a certain nonlinear scheme is proposed to guarantee sway velocity uniformly ultimately bounded by Lyapunov direct method. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed method.

For the congestion control of the transmission control protocol (TCP) networks in presence of input saturation and the lumped uncertainty, an active queue management algorithm is proposed based on adaptive sliding mode control. One adaptive law is designed to estimate the lumped uncertainty such that the need for knowing the bound in advance is eliminated. The other adaptive law is proposed to ensure the system stability considering the TCP networks subjected to input saturation. Using these adaptive laws, an adaptive sliding mode controller is presented to guarantee asymptotic convergence and robustness against the lumped uncertainty. Numerical simulations are performed to show that the proposed scheme is effective in various network scenarios.

This paper investigates the precise trajectory tracking of unmanned aerial vehicles (UAV) capable of vertical take-off and landing (VTOL) subjected to external disturbances. For this reason, a robust higher-order-observer-based dynamic sliding mode controller (HOB-DSMC) is developed and optimized using the fractional-order firefly algorithm (FOFA). In the proposed scheme, the sliding surface is defined as a function of output variables, and the higher-order observer is utilized to estimate the unmeasured variables, which effectively alleviate the undesirable effects of the chattering phenomenon. A neighboring point close to the sliding surface is considered, and as the tracking error approaches this point, the second control is activated to reduce the control input. The stability analysis of the closed-loop system is studied based on Lyapunov stability theorem. For a better study of the proposed scheme, various trajectory tracking tests are provided, where accurate tracking and strong robustness can be simultaneously ensured. Comparative simulation results validate the proposed control strategy’s effectiveness and its superiorities over conventional sliding mode controller (SMC) and integral SMC approaches.

This paper describes fault reconstruction issue for single input linear systems with uncertainty, using adaptive fuzzy dynamic sliding mode control (AFDSMC). The adaptive fuzzy control is used to access the control signal of dynamic sliding mode control and the adaptive law is derived. In AFDSMC sliding surface is used as the basis for reconstruction the fault signals. In AFDSMC an integrator is placed in front of the system to be controlled and the augmented system (the system plus the integrator) is one dimension bigger than the actual system and then, the plant model should be completely known. To overcome on this difficulty, an optimal loop transfer recovery observer (LTRO) is used. Comparing this method to other fault reconstruction methods, the advantage of the proposed method is to have the system controlled as well as its main task i.e. fault reconstruction.

In this paper, an adaptive recurrent-neural-network (ARNN) motion control system for a biaxial motion mechanism driven by two field-oriented control permanent magnet synchronous motors (PMSMs) in the computer numerical control (CNC) machine is proposed. In the proposed ARNN control system, a RNN with accurate approximation capability is employed to approximate an unknown dynamic function, and the adaptive learning algorithms that can learn the parameters of the RNN on line are derived using Lyapunov stability theorem. Moreover, a robust controller is proposed to confront the uncertainties including approximation error, optimal parameter vectors, higher-order terms in Taylor series, external disturbances, cross-coupled interference and friction torque of the system. To relax the requirement for the value of lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is investigated. Using the proposed control, the position tracking performance is substantially improved and the robustness to uncertainties including cross-coupled interference and friction torque can be obtained as well. Finally, some experimental results of the tracking of various reference contours demonstrate the validity of the proposed design for practical applications.

This paper is concerned with the trajectory tracking control of unmanned surface vehicles (USVs) subject to input quantization, actuator faults and dead zones. In scenarios with dense marine facilities, there are constraints on the tracking performance and convergence time of USVs. First, the designed control signal is quantized by a hysteresis quantizer to reduce the transmission rate. Second, to guarantee the transient and steady-state tracking performance of the USV, a prescribed performance control technology with a predefined settling time is employed. Third, a predefined-time adaptive sliding mode control (SMC) method is designed by integrating the auxiliary function and the barrier function. Moreover, the lumped uncertainties caused by quantization, actuator faults, and dead zones are simultaneously processed using control gain based on barrier function. The proposed control method guarantees that the tracking error and sliding variable converge to the corresponding predefined bounds within a predefined time. The predefined bounds are independent of the upper bound on the lumped uncertainty. The stability of the controlled system is proven via the Lyapunov theorem. Finally, the effectiveness of the designed controller is verified by numerical simulations.

Combined adaptive backstepping-SMC schemes to design both static and dynamical adaptive controllers for uncertain nonlinear systems have been presented. It has been shown that, for the class of nonlinear systems with parametric uncertainties entering linearly in the system dynamics and transformable into FGOCF, adaptive dynamical SMC may be designed via an adaptive input-output linearization procedure, based upon the fundamental ideas of the backstepping algorithm. This control design method is applicable to uncertain nonlinear systems without explicit transformations to parametric-pure or parametric-strict feedback forms. The proposed controlled strategy has been applied to the output tracking control of the average Buck-Boost converter model and it has been shown to be remarkably robust with respect to external stochastic bounded perturbation input signals.

In this paper, an adaptive robust control is investigated in order to deal with the unmatched and matched uncertainties in the manipulator dynamics and the actuator dynamics, respectively. Because these uncertainties usually include smooth and unsmooth functions, two adaptive mechanisms were investigated. First, an adaptive mechanism based on radial basis function neural network (RBFNN) was used to estimate the smooth functions. Based on the Taylor series expansion, adaptive laws derive for not only the weighting vector of the RBFNN, but also for the means and standard derivatives of the RBFs. The second one was the adaptive robust laws, which is designed to estimate the boundary of the unsmooth function. The robust gains will increase when the sliding variable leave the predefined region. Conversely, they will significantly decrease when the variable approaches the region. So, when these adaptive mechanisms are derived with the backstepping technique and sliding mode control, the proposed controller will compensate the uncertainties to improve the accuracy. In order to prove stability and robustness of the controlled system, the Lyapunov approach, based on backstepping technique, was used. Some simulation and experimental results of the proposed methodology in the electrohydraulic manipulator were presented and compared to other control to show the effectiveness of the proposed control.

A new adaptive fuzzy sliding-mode control (FSMC) scheme was proposed, which incorporated the fuzzy logic into sliding-mode control using fuzzy logic to approximate the equivalent control and the upper bound of uncertainty which involved the disturbance and approximation error, thus the influence of modeling error was reduced and the gain of sliding-mode control part was more fitting, such that the chattering effects could be alleviated . Besides, inspired by the thought of fully tuned RBF neural networks, a fully tuned fuzzy logic was proposed which enhanced the approximating ability of fuzzy logic system. Lyapunov stability theorem was used to prove the stability of the system and the adaptive laws were deduced. Finally, simulation results of some BTT missile were included to illustrate the effectiveness of the adaptive fuzzy sliding-mode control scheme.