Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

This paper develops a Fuzzy Adaptive Backstepping Control (FABC) and a Fuzzy Adaptive Backstepping Sliding Mode Control (FABSMC) for Single-Input Single-Output (SISO) nonlinear-systems with unmeasured states. The proposed adaptive schemes are fully compared. Thus, the Fuzzy Type-2 (FT2) concept and the High-Order Integral-Chain Differentiator (HOICD) are used as two universal approximators. Indeed, the first one is employed to approximate the nonlinear system model’s and the second one to estimate the unknown states. Special attention is paid for the used approximators robustness under unmodeled dynamics, parameter variations and process noise. It should be noted that the asymptotic stability of both the fuzzy adaptive controls and the observer convergence for each scheme have been proved. In addition, the employed schemes have been simulated on a two-tank coupled nonlinear system. Thus, from simulation results, we can prove that the proposed methods guarantee that all signals for closed loop systems are both regular and bounded. Specifically, it can be shown that the performances of the proposed Fuzzy Interval Type-2 (FIT2) schemes are significantly improved compared with Fuzzy Type-1 (FT1) schemes in presence of external disturbances.

This study proposes a novel Disturbance Observer based Adaptive Backstepping Sliding Mode Controller (DOABS-SMC) to overcome the adverse effects of parametric uncertainties and external disturbances on a single-phase inverter in stand-alone power supply mode in addition to improving the tracking time of the output voltage. Firstly, the dynamical model of the inverter in stand-alone power supply mode is established with parametric uncertainties. Thereafter, a traditional Backstepping controller (BSC) is combined with disturbance observers (DO) to remove the adverse effects of the disturbances and confirm the system's stability. To ensure the robustness of the controller, an SMC is combined with the BSC to further improve the performance of the controller. The effectiveness of the proposed controller is then experimentally verified by comparing it with traditional BSC and PID controllers. The results indicated that the proposed controller's performance is better than the other ones under the parametric disturbances and changing load.

Neural network approximation-based backstepping sliding mode control for spacecraft with input saturation and dynamics uncertainty

For flight simulator system, a kind of Adaptive Backstepping Sliding Mode Controller (ABSMC) based on Radial Base Function Neural Network (RBFNN) observer is presented. The sliding mode control theory is famous by its characteristic that it is insensitive to the external disturbances and parameters uncertainties. Combining this characteristic with Backstepping method can simplifies the controller design. And the addition of the terminal attractor can make the arrival time shorten greatly. However, too large external disturbances and parameters uncertainties are still not allowed to the system, and the design process of ABSMC does not have the upper bound information of disturbance until a RBFNN observer is designed to solve the problems. The simulation results show that the proposed scheme can improve the tracking precision and reduce the chattering of the control input, and the system has a higher robustness.

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.

The Switched Reluctance Motors (SRMs), with many outstanding advantages, are gradually being widely applied in industries, households, and recommended in many works. However, most studies in the field of SRM control only concentrate on the mathematical model of the motor itself, neglecting the nonlinearity introduced by the inverter, which is responsible for switching between phases to drive the motor. This paper proposes an adaptive backstepping sliding mode control algorithm based on the SRM nonlinear model that combines both the motor and the inverter. Firstly, a backstepping sliding mode controller is used to track the desired value and ensure the stability of the system according to the Lyapunov criterion. Secondly, a fuzzy logic system is added to adjust the controller parameters to account for uncertainty and external disturbance, as well as to minimize the chattering phenomenon. Finally, a few simulation scenarios are performed to assess the effectiveness of the proposed controller. The simulation results clearly demonstrate that the proposed controller surpasses the previously published H infinity controller in terms of speed control quality for the combined nonlinear model of SRM. The proposed controller exhibits zero steady-state error, zero overshoot, and a short settling time of approximately 0.5 seconds. Moreover, the system's output quickly stabilizes when affected by disturbance noise.

An intelligent backstepping sliding-mode control system using radial basis function network (RBFN) for a two-axis motion control system using permanent magnet linear synchronous motors (PMLSMs) is proposed. First, single-axis motion dynamics with the introduction of a lumped uncertainty, including cross-coupled interference between the two-axis mechanism, is derived. Then, to improve the control performance in reference contour tracking, a backstepping sliding-mode approach is proposed to compensate for uncertainties occurring in the motion control system. The bound of the lumped uncertainty is necessary in the design of the backstepping sliding-mode control system and is difficult to obtain in advance in practical applications. Therefore, an RBFN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping sliding-mode control system. An adaptive learning algorithm, which can learn the parameters of the RBFN online, is derived using Lyapunov stability theorem. The proposed control algorithms are implemented in a TMS320C32 DSP-based control computer, and the motions in the x-axis and y-axis are controlled separately. The simulated and experimental results of circle and four leaves reference contours show that the motion tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference and frictional forces can also be obtained using the proposed controller.

An efficient and robust technique is presented in this study to control the voltage and current of the inverters in MasterSlave Organized AC Smart Island. In the smart island, voltage/frequency and current control of inverters-based renewable energy resources are challenged by parametric uncertainty and external disturbances. It is consequently essential to adopt a stable and robust control approach. This study proposes an adaptive hybrid robust controller using a Sliding Mode Controller (SMC) and backstepping controller. SMC can also be robust to many external disturbances and uncertainties. The structure of the Backstepping Sliding Mode Controller (BSMC) includes an adaptive mechanism. The switching gain is adaptively updated in real-time for the adaptive BSMC. The Lyapunov theory has been used to prove the closed-loop system’s stability. Matlab/Simulink is used to implement the suggested control technique, and several case studies are analyzed to evaluate its effectiveness. According to simulations, the suggested controller maintains islanded microgrid stability by controlling voltage and current precisely regardless of external disturbances and uncertainty.

In this article, adaptive backstepping sliding mode controller and adaptive fractional backstepping sliding mode controller methods are proposed to control an electrostatic microplate with a piezoelectric layer. Based on the modified couple stress theory, a size-dependent mathematical model is proposed, in which the microplate is modeled using the Kirchhoff plate theory. To take into account the geometric nonlinearities, the von Kármán nonlinear strains are considered in the mathematical model. The Hamilton’s principle is used to obtain the nonlinear equation of motion of the system, which is then converted into a nonlinear ordinary differential equation via the Galerkin technique. The validity of the results obtained from the proposed reduced-order model is checked through direct numerical simulation of the partial differential equation using the finite element method. Finally, two Lyapunov-based control approaches that are adaptive backstepping sliding mode and adaptive fractional backstepping sliding mode are applied to the system. In the adaptive fractional backstepping sliding mode controller method, the adaptive control law is used to evaluate the upper bound of uncertainties and random disturbances. The fractional-order form of sliding surface is used to reduce the amount of chattering and also to improve the tracking error. In the results section, first, numerical studies are conducted to study the effect of system parameters, such as material length scale parameter, aspect ratio, fractional order, and robustness of the controller, on the performance of the system. In addition, the performances of the two control methods are compared, and the merits and demerits of each method are discussed.

In this paper, a controller combining backstepping and adaptive supertwisting sliding mode control method is proposed for altitude and velocity tracking control of air-breathing hypersonic vehicles (AHVs). Firstly, the nonlinear longitudinal model of AHV is introduced and transformed into a strict feedback form, to which the backstepping method can be applied. Considering the longitudinal trajectory tracking control problem (altitude control and velocity control), the altitude tracking control system is decomposed to several one-order subsystems based on the backstepping method, and an adaptive supertwisting sliding mode controller is designed for each subsystem, in order to obtain the virtual control variables and actual control input. Secondly, the overall stability of the closed-loop system is proved by the Lyapunov stability theory. At last, the simulation is carried out on an AHV model. The results show that the proposed controller has good control performances and good robustness in the parameter perturbation case.

Hydraulic actuators are highly nonlinear when they are subjected to different types of model uncertainties and dynamic disturbances. These unfavorable factors adversely affect the control performance of the hydraulic actuator... | Find, read and cite all the research you need on Tech Science Press

This paper presents an adaptive synchronous sliding mode control for a dual-arm robot under presence of the parameter variations. The proposed control designed based on the mathematical formulations of the dual-arm robot includes the sliding mode control and adaptive laws. The adaptive laws are provided to adjust the robust gains of the sliding mode control to scope with the uncertainties. In order to guarantee the certain kinematic relationships between robots, synchronous terms are integrated into the control design. As a result, the proposed control will guarantee asymptotic convergence to zero of both position tracking and synchronization errors simultaneously. The effectiveness of the proposed method is analyzed in theory by using Lyapunov approach. Finally, some simulations are conducted on MATLAB Simulink to prove the effectiveness of the approach.KeywordsAdaptive controlSliding mode controlSynchronous controlDual-arm robot

This paper develops a novel methodology for voltage regulation and equal current distribution of parallel-buck DC-DC converters. Such converters are modeled as linear averaged state-space system models with adjustable loads. Two measurements, such as output voltage and inductor current, are used to accomplish the design of an adaptive backstepping sliding mode controller for the parallel-buck DC-DC converters. Based on the singular perturbation method, a reduced order model is derived in order to show that the effect of equivalent series resistance (ESR) of the capacitor can be ignored and the parallel converters can be well approximated by the reduced order model. With the reduced order model, an adaptive sliding mode control utilizing backstepping approach is proposed to control the parallel-buck DC-DC converters. This proposed control method has been verified by computer simulations, and has also been proven capable of giving satisfactory voltage regulation performance under input voltage variations and load changes.

This study investigates the control of theodolite rotating system, and proposes an adaptive backstepping sliding mode control framework to overcome the non-linearities and uncertainties of the system. In terms of practical operation, a non-linear model is established by describing the relationship between the state variables of theodolite rotating system and the control voltage. For designing the controller, sliding mode control and adaptive control are introduced to effectively deal with non-linearity and uncertainty effect. Simulation results show that the adaptive sliding mode controller has reasonable robustness and tracking performance against PID control, and the dynamics and stability of theodolite rotating surveying system have been improved.

This paper proposes an adaptive backstepping sliding mode control (ABSMC) scheme for autonomous underwater vehicles (AUVs) subject to the dynamic uncertainty, external disturbance and quantization error. The control input signals including control forces and moment are quantized by a hybrid quantizer which is the combinination of a logarithmic quantizer and a uniform quantizer. The kinematic controller is designed by the backstepping control technique and the dynamic controller is developed using the sliding mode control method. In order to further improve the robustness of the closed‐loop system, an adaptive law is employed to estimate the upper bound of the total uncertainties in real time. The stability of the closed‐loop system is proved based on the Lyapunov theory and indicates that the proposed control method can force the AUV to track the desired trajectory. Simulation results demonstrate the effectiveness of the proposed control strategy.