Adaptive Backstepping Control Approach Research Articles

Observer-based adaptive fuzzy control of stabilized platform in rotary steerable system with input saturation and output constraint

This paper explores the control strategy for toolface tracking in the stabilized platform of a rotary steerable system, considering both input saturation and output constraint. To mitigate the impact of unknown friction torque and modeling error on the stabilized platform, we propose an adaptive fuzzy backstepping control approach. A fuzzy state observer is specifically devised to handle the uncertain state arising from unknown friction torque and modeling errors. Introducing an auxiliary system effectively compensates for input saturation, and the resolution of the output error constraint is achieved through the construction of a barrier Lyapunov function. Furthermore, employing the Lyapunov method establishes that all signals in the entire closed-loop control system are semi-globally uniformly ultimately bounded. Simulation results confirm the efficacy of the proposed control methodology.

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

AbstractA type‐2 fuzzy logic‐based adaptive backstepping control (T2FABSC) approach is designed for an H‐bridge inverter. This inverter has an LC filter to decrease the level of total harmonic distortion (THD) that can affect the efficiency of the system. Reduction of THD and stability insurance of the filter are challenging trade‐offs, tackled here by a controller design. The performance, however, is not suitable for actual applications under a wider range of disturbances, and the parameters need to be adjusted once more for more dependable operations. Backstepping control is given a Lyapunov definition‐based adaptive mechanism that can improve this scheme's stability and robustness in the face of numerous disturbances. Additionally, the system is viewed as a “Black box” without the need for a precise mathematical model, which might lead to a lighter computing burden and simpler implementation. Moreover, a fuzzy type‐2 structure is adopted that can optimize the gains of the adaptive Backstepping controller (ABSC) in challenging conditions. The antlion optimization algorithm is employed to optimize the parameters of the membership functions in the fuzzy system, hence improving the performance of the controller. In addition, compared with different optimization algorithms, it can more quickly and accurately locate the best solutions. Finally, the findings of both the simulations and the experimental outputs are examined, proving the T2FABSC's significant robustness and faster dynamics in a variety of challenging circumstances.

A new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning

In this paper, a new optimal adaptive backstepping control approach for nonlinear systems under deception attacks via reinforcement learning is presented in this paper. The existence of nonlinear terms in the studied system makes it very difficult to design the optimal controller using traditional methods. To achieve optimal control, RL algorithm based on critic–actor architecture is considered for the nonlinear system. Due to the significant security risks of network transmission, the system is vulnerable to deception attacks, which can make all the system state unavailable. By using the attacked states to design coordinate transformation, the harm brought by unknown deception attacks has been overcome. The presented control strategy can ensure that all signals in the closed-loop system are semi-globally ultimately bounded. Finally, the simulation experiment is shown to prove the effectiveness of the strategy.

Power transfer control within the framework of vehicle-to-house technology

<span lang="EN-US">The emerging vehicle-to-grid (V2G) technology has gained a lot of praise in the last few years, following its experimental validation in several countries. As a result, this technology is being investigated for standalone houses under the name of vehicle-to-house (V2H). This latter proposes a two-way power transfer between the electric vehicles and isolated houses relying on renewable sources for power supply. In this paper an implementation of the V2H technology is investigated, using the adaptive backstepping control approach for the bidirectional half-bridge and the integral sliding mode control for the DC-DC converter. The robustness of the controller and its capability to respond to the desired performances were tested using different realistic scenarios. The obtained results yielded, a perfect sinusoidal output voltage with a voltage level of 220 V and a frequency of 50 Hz. This is further been validated by a frequency analysis resulting in a THD of 0.25%.</span>

Nonlinear Adaptive Back-Stepping Optimization Control of the Hydraulic Active Suspension Actuator

The displacement tracking performance of the electro-hydraulic servo actuator is critical for hydraulic active suspension control. To tackle the problem of slow time-varying parameters in the existing actuator dynamics model, a nonlinear adaptive back-stepping control (ABC) approach is adopted. Simultaneously, the parameters of the nonlinear ABC are difficult to configure, resulting in a poor control effect. An enhanced particle swarm optimization (PSO) approach integrating crazy particles (CP) and time-varying acceleration coefficients (TVAC) is suggested to optimize the controller settings. Furthermore, in order to obtain satisfactory dynamic characteristics of the transition process, the absolute value of the error time integral performance index is used as the minimum performance index function of parameter selection, and the square term of the control input is added to the performance index function to prevent excessive controller energy. Finally, it can be observed from the simulation results of the highest value emax of the displacement tracking error, the average value eμ of error, and the standard deviation eσ of error that the performance of the ABC parameters optimized by PSO+CP+ATVC is superior to the manually given ABC parameters. Therefore, this control method significantly improves the stability and speed of the control system. It provides a new research idea for the parameter optimization of controllers.

Adaptive backstepping controller design for DC/DC buck converter optimised by grey wolf algorithm

AbstractA Lypunov‐based Adaptive Backstepping Control (ABSC) approach is designed for a power Buck converter. This strategy is an advanced version of the Backstepping method utilising Lyapunov stability function to reach a higher stability and a better disturbance rejection behaviour in the practical applications. In addition, to reduce the computational burden and increase ease of implantation, Black‐box technique is considered assuming no accurate mathematical model for the system. Nonetheless, in real‐time environments, disturbances with wider ranges including: supply voltage variation, parametric variation, and noise can negatively impact the operation of this method. To compensate for this problem, the gains of the controller should be tuned again for better adaptability with the working condition. Therefore, to satisfy this need and enhance the controller's performance, a metaheuristic algorithm is applied in the control scheme called Grey Wolf Optimisation (GWO) algorithm. GWO is a well‐behaved nature‐inspired algorithm with faster decision‐making dynamics along with more accuracy over different optimisation algorithms. To better elaborate the merits of this approach, conventional BSM and PSO‐based PID schemes are also designed and tested in different situations.

Design of a novel robust type‐2 fuzzy‐based adaptive backstepping controller optimized with antlion algorithm for buck converter

AbstractA type‐2 fuzzy logic‐based adaptive backstepping control (T2F‐ABSC) approach is presented for a DC/DC Buck converter. Lyapunov‐based backstepping control (BSC), which can guarantee convergence along with asymptotic stability of the system. Also, the black‐box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. Type‐2 fuzzy structure is adopted here to optimize the gains of the ABSM in challenging conditions. Type‐2 fuzzy (T2F) has higher efficiency and faster dynamics with more adaptability to the system. To enhance the performance of the T2F, antlion optimization (ALO) has been used in this structure. ALO is a modern nature‐inspired algorithm, and it has some advantages over other optimizing algorithms. Furthermore, it has ability to find optimal answers in a shorter time, more accurately in contrast to the other optimization algorithms and is used to tune the membership function parameters of the (T2F) under the diverse search spaces. To depict the strength of the designed work, fuzzy‐PID and backstepping schemes proposed to carry out an analysis with this work.

Adaptive Neural Backstepping Control Approach for Tracker Design of Wheelchair Upper-Limb Exoskeleton Robot System

In this study, the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance is investigated. For this reason, an adaptive neural network using a backstepping control strategy is designed. The difference between the actual values of the upper-limb exoskeleton robot system and the desired values is considered as the tracking error. Afterward, the auxiliary variable based on the tracking error is defined and the virtual control input is obtained. Then, by using the backstepping control procedure and Lyapunov stability concept, the convergence of the position tracking error is proved. Moreover, for the compensation of the model uncertainty and the external disturbance that exist in the upper-limb exoskeleton robot system, an adaptive neural-network procedure is adopted. Furthermore, for the estimation of the unknown coefficient related to the parameters of the neural network, the adaptive law is designed. Finally, the simulation results are prepared for demonstration of the effectiveness of the suggested method on the upper-limb exoskeleton robot system.

A nonlinear adaptive excitation controller design for two‐axis models of synchronous generators in multimachine power systems to augment the transient stability during severe faults

AbstractThis paper presents an excitation controller design process for multimachine power systems with synchronous generators using an adaptive backstepping approach to ensure robust performance against parametric uncertainties as well as to improve the transient stability during large disturbances. In this work, electrical dynamics of synchronous generators are described using two‐axis models while excitations systems are represented as the IEEE Type II exciter which mainly captures electrical dynamics while swing equations are used to capture mechanical dynamics. The proposed adaptive backstepping control approach is then employed to derive the excitation control law. The proposed adaptive backstepping scheme uses all nonlinearities, which are mainly due to the interconnections and rotations of rotors in synchronous generators, within the dynamical models to improve the transient stability during severe disturbances. Furthermore, this excitation control scheme uses estimated values of parameters appearing in the dynamical models of power system which are estimated using adaptation laws based on real‐time measurements and hence, it provides robustness against parametric uncertainties. The theoretical stability of the proposed scheme is assessed using the Lyapunov stability theory, that is, by checking the negative definiteness or semi‐definiteness of the derivative of control Lyapunov functions (CLFs). Rigorous simulations are conducted on an IEEE 39‐bus 10‐machine test power system for evaluating the performance of the proposed scheme under different operating conditions. Simulation results clearly demonstrate the superiority of the adaptive backstepping excitation controller over existing nonlinear controllers including an existing adaptive backstepping excitation controller that is designed using the classical model of synchronous generators.

Robust adaptive backstepping control of uncertain fractional-order nonlinear systems with input time delay

In the present work, a fractional adaptive backstepping control approach is proposed for controlling a class of uncertain fractional-order nonlinear systems subjected to unknown external disturbance and input delay. In control design, by introducing fractional command-filter, the key issue of the common backstepping method called the “explosion of complexity” is addressed. Furthermore, the Szász–Mirakyan operator is used as a simple but efficient universal approximator to approximate uncertain or unknown terms and reduce the design complexity. In addition, to enhance the capability of the proposed controller in dealing with the approximation error and the unknown disturbance, an efficient robust control term is also incorporated into it. One advantage of the suggested control approach is that the input-delay fractional-order system is effectively treated by introducing a new variable as a non-delayed fractional-order system. The other advantage is that the number of tuning variables is considerably low; specifically, for an n-order fractional nonlinear system, only n adaptive parameters are adjusted online. This makes the implementation of the proposed approach simpler. The Lyapunov stability theorem is used to prove that all of the closed-loop system’s signals are semi-globally uniformly ultimately bounded. The feasibility of the suggested control approach is shown by comparing the simulation results obtained with the fuzzy system.

Synchronization of Rossler chaotic systems via hybrid adaptive backstepping/sliding mode control

In this paper, a hybrid control approach combining adaptive backstepping and fuzzy sliding mode control is proposed for the synchronization of two Rossler chaotic systems. The proposed approach provides fast, high accuracy chaos synchronization in presence of unknown system parameters and external disturbances. The adaptive backstepping control law is developed to stabilize the synchronization error dynamics arising from the mismatch between the drive and response systems. Both unknown system parameters and unknown external disturbance are estimated by the adaptive backstepping control approach in a way that the overall system with the controller becomes stable. As a result, the requirement to have a priori knowledge about the unknown parameters, e.g., disturbance bounds, is nullified. The fuzzy sliding mode controller is designed to accompany the adaptive backstepping to confine the synchronization error and enhance the synchronization speed between the two chaotic systems. Performance of the proposed approach is investigated for a numerical example and compared against commonly used chaos synchronization techniques including optimal control, active control, and adaptive backstepping control method. Simulation results indicate the superior performance of the hybrid control method over state-of-the-art for the synchronization of Rossler chaotic systems.

Adaptive state-feedback stabilization of stochastic nonholonomic systems with an unknown time-varying delay and perturbations

The problem of adaptive state-feedback stabilization of stochastic nonholonomic systems with an unknown time-varying delay and perturbations is studied in this paper. Without imposing any assumptions on the time-varying delay, an adaptive state-feedback controller is skillfully designed by using the input-state scaling technique and an adaptive backstepping control approach. Then, by adopting the switching strategy to eliminate the phenomenon of uncontrollability, the proposed adaptive state-feedback controller can guarantee that the closed-loop system has an almost surely unique solution for any initial state, and the equilibrium of interest is globally asymptotically stable in probability. Finally, the simulation example shows the effectiveness of the proposed scheme.

A Fuzzy Adaptative Backstepping Control Strategy for Marine Current Turbine under Disturbances and Uncertainties

Marine current energy is attracting more and more attention in the world as a reliable and highly predictable energy resource. However, conventional proportional integral (PI) control will be sensitive to the numerous challenges that exist in a marine current turbine system (MCTs) such as marine current disturbance, torque disturbance and other uncertain parameters. This paper proposes a fuzzy adaptive backstepping control (F-A-BC) approach for a marine current turbine system. The proposed F-A-BC strategy consisted of two parts. First, an adaptive backstepping control approach with the compensation of disturbance and uncertainty was designed to improve anti-interference of the MCT so that the maximum power point tracking (MPPT) was realized. Then, a fuzzy logic control approach was combined to adjust parameters of an adaptive backstepping control approach in real time. The effectiveness of the proposed controller was verified by the simulation of a direct-drive marine current turbine system. The simulation results showed that the F-A-BC has better anti-interference ability and faster convergence compared to the adaptive backstepping control, sliding mode control and fuzzy PI control strategies under disturbances. The error percentage of rotor speed could be reduced by 3.5% under swell effect compared to the conventional controller. Moreover, the robustness of the F-A-BC method under uncertainties was tested and analyzed. The simulation results also indicated that the proposed approach could slightly improve the power extraction capability of the MCTs under variable marine current speed.

Adaptive neural decentralized output-feedback control for nonlinear large-scale systems with input time-varying delay and saturation

For a class of nonstrict-feedback nonlinear large-scale systems with input delay, saturation and unknown virtual control gains, we propose a decentralized output-feedback control scheme in this paper. In the process of control design, a novel auxiliary system is proposed to overcome the difficulty caused by time-varying input delay. The convex combination technique is used to overcome the difficulty caused by unknown virtual control gains, and further construct the state observer. Then a decentralized output-feedback controller is designed by using the adaptive neural backstepping control approach. By Lyapunov stability theory, it is proved that the proposed decentralized output-feedback controller can ensure that tracking errors converge to a small neighborhood of the origin and other closed-loop states are bounded. Finally, the results of simulation example further verify the validity of this decentralized output-feedback control scheme.

DESIGNING A ROBUST ADAPTIVE TRACKING BACKTEPPING CONTROLLER CONSIDERING ACTUATOR SATURATION FOR A WHEELED MOBILE ROBOT TO COMPENSATE UNKNOWN SLIPPAGE

This article highlights a robust adaptive tracking backstepping control approach for a nonholonomic wheeled mobile robot (WMR) by which the bad problems of both unknown slippage and uncertainties are dealt with. The radial basis function neural network (RBFNN) in this proposed controller assists unknown smooth nonlinear dynamic functions to be approximated. Furthermore, a technical solution is also carried out to avoid actuator saturation. The validity and efficiency of this novel controller, finally, are illustrated via comparative simulation results.

Direct Adaptive Neural Network Control for Ship Manoeuvring Modelling Group Model-Based Uncertain Nonlinear Systems in Non-Affine Pure-Feedback Form

In this article, a direct neural network based adaptive backstepping control approach is proposed for a class of uncertain non-affine ship manoeuvring pure-feedback nonlinear systems. To carry out the backstepping design, the high fidelity 3 degrees of freedom Manoeuvring Modelling Group (MMG) model with external disturbances is transformed into the ship manoeuvring systems in non-affine pure-feedback form. Then, by combing the Implicit Function Theorem, Mean Value Theorem and dynamic surface control technique, the proposed approach is able to avoid completely the circularity problem and complexity growing problem exist in the adaptive neural network controller. During the controller design, the uncertain nonlinear functions are approximated by neural networks. Following this control approach, it is worth noting that the direct adaptive backstepping control for the high fidelity MMG model based ship manoeuvring nonlinear systems is achieved firstly, and the controller structure is simpler. Furthermore, it is shown via stability analysis that all signals in the closed-loop system are uniformly ultimately bounded. At last, two reference signals consist of a constant and a realistic performance requirement of ship are applied to simulation studies to illustrate the utility and merits of the proposed control scheme.

CONTROL OF DOUBLE THREE-PHASE PMSM SERIES-CONNECTED SYSTEM BASED ON ADAPTIVE BACK-STEPPING METHOD

To research space gap magnetic field harmonic influence on the decouple control and unknown parameters of double three-phase permanent magnet synchronous motor permanent magnet synchronous motor (PMSM) two-motor series-connected system, the nonlinear adaptive back-stepping control approach is presented.

Adaptive Backstepping Controller of PMLSM

In this paper, a nonlinear adaptive speed controller for permanent magnet linear synchronous motors based on a newly developed adaptive recursive Backstepping control approach for a permanent magnet synchronous motor drive is discussed and analyzed. The Backstepping technique provides a systematic method to address this type of problem. It combines the notion of Lyapunov function and a controller procedure recursively. The adaptive Backstepping control approach is utilized to obtain the robustness for mismatched parameter uncertainties. The overall stability of the system is shown using Lyapunov stability theorem. The simulation results clearly show that the proposed scheme can track the speed reference.

Improved adaptive backstepping sliding mode control for a three‐phase PWM AC–DC converter

The stability problem of a three-phase voltage source PWM AC-DC converter is addressed through an improved adaptive backstepping method, which combines the following contents: input-output linearisation, adaptive backstepping method, error compensation and sliding mode control. The state equation of the three phase system is obtained by the power balance relationship between the input current and the output voltage, and then the coordinate conversion is carried out in order to design the controller. When the input-output linearisation method transfers the system model to a strict-feedback form, an improved adaptive backstepping control approach is given to stabilise the converter. Finally, the simulation results show that the improved method has three benefits: retain a faster stability of rectifier, reduce overshoot of DC voltage and bring about a real-time parameter estimation.

Adaptive Backstepping Control of a Pneumatic System With Unknown Model Parameters and Control Direction

The performance of the pneumatic elements and the micro-controller steadily increases as the price of them decreases. This trend promotes high performance tracking control research on pneumatic servo systems. However, it is very difficult to obtain exact model parameters, which is one of the main obstacles to design a high performance controller. Moreover, in some application cases, the control direction is undetermined because of the possible false or incorrect valve outlets connection. At the same time, the inaccuracy of the proportional valve zero points also degrades the tracking performance of pneumatic servo systems. In this paper, an adaptive backstepping control approach is proposed for a pneumatic position servo system considering unknown model parameters, unknown control direction, and inaccurate valve zero points. The proposed method combines the Nussbaum function and backstepping technique to design a position tracking controller of a pneumatic servo system with the aforementioned uncertainties. By using the Lyapunov method, the designed controller is proved to be stable, and the tracking error asymptotically converges to zero. The experimental results demonstrate the effectiveness and the superiority of the proposed approach as compared with some existing methods, even for negative direction case for which the other methods fail. The performance of the proposed controller is further improved by considering the proportional valve zero points.