Robust Adaptive Backstepping Control Research Articles

Velocity Estimation and Robust Non-linear Path Following Control of Autonomous Surface Vehicles

This work addresses the problem of non-linear path following control for under-actuated autonomous surface vehicles in the horizontal plane. The presence of multiple unknowns is considered, including unmodelled hydrodynamics, internal parametric model uncertainties, and unmeasurable disturbances due to wind, waves, and ocean currents, whereas the surge, sway, and yaw velocities are also considered to be unmeasured. Firstly, a non-linear extended state observer is applied to recover the unmeasured velocities and estimate the lumped generalised disturbances, that include all unknown terms previously detailed. Secondly, regarding the path following control, a surge-guided line-of-sight guidance law is applied to simultaneously compute the surge and heading/yaw references, while a simplified robust-adaptive backstepping control strategy is proposed. The effectiveness and robustness of the proposed estimation and control strategy is verified in simulation considering challenging disturbance and current profiles.

A new robust adaptive neural network backstepping control for single machine infinite power system with TCSC

For a single machine infinite power system with thyristor controlled series compensation &#x0028 TCSC &#x0029 device, which is affected by system model uncertainties, nonlinear time-delays and external unknown disturbances, we present a robust adaptive backstepping control scheme based on the radial basis function neural network &#x0028 RBFNN &#x0029 . The RBFNN is introduced to approximate the complex nonlinear function involving uncertainties and external unknown disturbances, and meanwhile a new robust term is constructed to further estimate the system residual error, which removes the requirement of knowing the upper bound of the disturbances and uncertainty terms. The stability analysis of the power system is presented based on the Lyapunov function, which can guarantee the uniform ultimate boundedness &#x0028 UUB &#x0029 of all parameters and states of the whole closed-loop system. A comparison is made between the RBFNN-based robust adaptive control and the general backstepping control in the simulation part to verify the effectiveness of the proposed control scheme.

Robust adaptive backstepping fast terminal sliding mode controller for uncertain quadrotor UAV

The problem of controlling the quadrotor orientation and position is considered in the presence of parametric uncertainties and external disturbances. Previous works generally assume that the flight controller parameters are constants. In reality, these parameters depend on the desired trajectory. In this article, a complete mathematical model of a quadrotor UAV is presented based on the Euler-Newton formulation. A robust nonlinear fast control structured for the quadrotor position and attitude trajectory tracking is designed. The position loop generates the actual thrust to control the altitude of the quadrotor and provides the desired pitch and roll angles to the attitude loop, which allow the control of the quadrotor center of gravity in the horizontal plane. The attitude loop generates the rolling, pitching and yawing torques that easily allow the insurance of the quadrotors stability. The outer loop (position loop) uses the robust adaptive backstepping (AB) control to get the desired Euler-angles and the control laws. The inner loop (attitude loop) employs a new controller based on a combination of backstepping technique and fast terminal sliding mode control (AB-ABFTSMC) to command the yaw angle and the tilting angles. In order to estimate the proposed controller parameters of the position and the upper bounds of the uncertainties and disturbances of the attitude, online adaptive rules are proposed. Furthermore, the Lyapunov analysis is used to warranty the stability of the quadrotor UAV system and to ensure the robustness of the controllers against variation. Finally, different simulations were performed in the MATLAB environment to show the efficiency of the suggested controller. The sovereignty of the proposed controller is highlighted by comparing its performance with various approaches such as classical sliding mode control, integral backstepping and second order sliding mode controls.

An Observer-Based Robust Adaptive Fuzzy Back-Stepping Control of Ball and Beam System

In this paper, an observer-based robust adaptive fuzzy back-stepping control is provided for a ball and beam system while accessible state variables of the system are position and angle of the ball and beam, respectively. Firstly, based on the mentioned measureable signals, a velocity observer is employed to estimate rate of the available states of the system and then a robust adaptive fuzzy back-stepping control is accomplished by the estimated states of the system. Asymptotic stability of the observer-based control system in the presence of un-modeled dynamics and uncertainties is shown by Lyapunov theory. Simulation results illustrate proper functioning of the adaptive fuzzy back-stepping control method on the ball and beam system. Also, the results of the proposed method are compared with an existing method in order to show performance and accuracy of the controller.

Robust integral of neural network and precision motion control of electrical–optical gyro-stabilized platform with unknown input dead-zones

Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.

Finite-time adaptive fuzzy backstepping control of drug dosage regimen in cancer treatment

In this paper, the design of robust finite-time adaptive fuzzy backstepping control for multi-input multi-output (MIMO) cancer immunotherapy system with fully unknown parameters is addressed. The aim of the treatment is to reduce the tumor cells by providing a suitable scheduling scheme for drug dosage. The main contribution of the proposed control scheme is that it not only has the advantages of the conventional backstepping control, but also guarantees the finite-time convergence. To approximate the unknown nonlinear parts of the system, the fuzzy system is employed. Using the Lyapunov stability theory, the fuzzy system parameters are tuned online using the derived adaptation laws. Compared with conventional backstepping control, the proposed controller does not include the time derivative of the virtual control law. Therefore, the proposed control structure has a simple robust structure and the problem of “explosion of terms” is avoided. Numerical simulation results are conducted that demonstrate that, as a result of drug treatment, the number of tumor cells is rapidly reduced to zero value.

Adaptive super-twisting sliding mode observer based robust backstepping sensorless speed control for IPMSM

This paper proposes a higher-order sliding mode observer based robust backstepping control to realize high-performance sensorless speed regulation for the interior permanent magnet synchronous motor (IPMSM). A new robust adaptive super-twisting higher-order sliding mode based observer is proposed to estimate the rotor position. The proposed observer has advantages of sliding chattering reduction and robustness against uncertainties. And, a new robust integral adaptive backstepping control with sliding mode actions is designed to achieve precise speed regulation. The uncertainties with unknown bounds can be stabilized by the sliding mode actions. And both transient and steady performance can be achieved by using the sliding mode and integral actions simultaneously. Then, a sensorless scheme is put forward to by combining the presented observer and the proposed controller. The stability of the observer and controller are verified. Simulation and experiment results validate the proposed approach.

Fractional order chattering-free robust adaptive backstepping control technique

This paper proposes an observer-based fractional order robust adaptive backstepping control scheme for incommensurate fractional order systems with partial measurable state. The chattering phenomenon is carefully analyzed, and then a class of chattering-free controllers are proposed. To handle the time-varying disturbance, a robust adaptive control scheme is developed via the backstepping procedure. The method to generate the required fractional order differential signals online is provided. After designing the controller, the stability of the resulting closed-loop system is analyzed systematically. To highlight the efficiency of our findings, one illustrative example is provided at last.

Adaptive Prescribed Performance Fault Tolerant Control for a Flexible Air-Breathing Hypersonic Vehicle With Uncertainty

This paper investigates a tracking control problem of a flexible air-breathing hypersonic vehicle (FAHV), where prescribed performance, actuator fault, and aerodynamic uncertainty are considered. Based on the backgrounds of FAHV and for designing controller simply, an uncertain control-oriented model is reasonably decomposed into velocity subsystem and altitude subsystem. Then, the dynamic inversion controller and robust adaptive back-stepping controller are, respectively, designed for each subsystem. At the level of control design, the upper bound of the lumped uncertainty is not to be known in advance, and the uncertainty is handled by the adaptive technique that is also utilized to deal with actuator fault. A novel first-order filter is designed to solve the “explosion of terms” problem inherent in back-stepping control. Third, the prescribed performance on tracking error that features the transient performance constraint on the tracking error is resolved by transforming the constrained problem into an unconstrained problem. Furthermore, the detailed stability analysis of the closed-loop system is carried out within the framework of Lyapunov theory, in which the tracking error converges to an arbitrarily small neighborhood around zero with the prescribed performance. Finally, compared simulation result illustrates the property of the designed control strategy in tackling prescribed performance, actuator fault, and aerodynamic uncertainty.

Robust Adaptive Neural-Network Backstepping Control Design for High-Speed Permanent-Magnet Synchronous Motor Drives: Theory and Experiments

This paper presents a robust adaptive backstepping control (RABC) for high-speed permanent-magnet synchronous motor (HSPMSM) drive system. The proposed RABC achieves high performance operation by incorporating an ideal backstepping controller (IBC), a recurrent radial basis function neural network (RRBFNN) uncertainty observer, and a robust controller. The Lyapunov stability theorem is utilized to design the IBC as a position controller of the HSPMSM servo drive system. To enhance the disturbance rejection capability during parameter changes, certain information is needed within the backstepping control law so that the system performance would not sorely be affected. To mitigate the need for the lumped parameter uncertainties within the backstepping controller, an online adaptive observer based on RRBFNN is designed to estimate the nonlinear parameter uncertainties. Moreover, the robust controller is intended to retrieve the remaining of the RRBFNN approximation errors. To assure the stability of the proposed RABC, the Lyapunov stability analysis is used to derive the online adaptive control laws. The performance of the proposed RABC is verified by simulation and experimental analysis under different operating conditions and parameter uncertainties. The test results validate the effectiveness of the proposed RABC scheme to achieve preferable tracking performance regardless of external disturbances and parameter uncertainties.

Robust Adaptive Backstepping Control with Improved Parametric Convergence

The paper addresses the problem of performance improvement of robust adaptive backstepping control for a class of disturbed nonlinear systems presentable in parametric-strict-feedback form. The proposed solution is based on backstepping procedure reducing to actual control and an error model. Later motivates design of robust σ-modification of adaptation algorithm with improved parameters tuning of the actual control. Tuning improvement is achieved by applying Kreisselmeier’s scheme of adaptation algorithm with memory regressor extension (MRE). MRE is provided by involving a linear filter into adaptation algorithm. On the one hand this filter records the regressor over past period of time (with some forgetting) and, as a result, accelerates the tuning of control. On the other hand the filter allows to generate the higher order time derivatives of adjustable parameters used for calculation of virtual and actual controls. The actual control obtained is robust with respect to disturbance, is free from overparameterization, does not contain tuning functions and completely compensates for nonlinear dynamics together with uncertainties of the plant. The efficiency of proposed solution is demonstrated via simulation.

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

This paper presents a robust adaptive integral backstepping control strategy with friction compensation for realizing accurate and stable control of opto-electronic tracking system in the presence of nonlinear friction and external disturbance. With the help of integral control term to decrease the steady-state error of the system and combining robust adaptive control approach with the backstepping design method, a novel control method is constructed. Nonlinear modified LuGre observer is designed to estimate friction behavior. Robust adaptive integral backstepping control strategy is developed to compensate the changes in friction behavior and external disturbance of the servo system. The stability of the opto-electronic tracking system is proved by Lyapunov criterion. The performance of robust adaptive integral backstepping controller is verified by the opto-electronic tracking system with modified LuGre model in simulation and practical experiments. Compared to the adaptive integral backstepping sliding mode control method, the root mean square of angle error is reduced by 26.6% when the proposed control method is used. The experiment results demonstrate the effectiveness and robustness of the proposed strategy.

Robust Adaptive Backstepping Control of the Induction Machine, Using Parallel Rotor Resistance and Stator Resistance Observer

This paper presents a robust control of the induction machine based on adaptive backstepping technique and using MRAS technique for parallel estimation of the rotor resistance and stator resistance. The flux components are estimated by using a sliding mode observer. This control offers and maintains good performances even in presence of parameters variations. The validation of this control is made by Matlab simulation; the obtained results prove its effectiveness and its good level of robustness.

Adaptive non‐linear trajectory tracking control for lane change of autonomous four‐wheel independently drive electric vehicles

Since autonomous four-wheel independently drive electric vehicles have the characteristics of parameter uncertainties, non-linearities and redundant actuators, trajectory tracking control for lane change of autonomous electric vehicles is regarded as a challenging task. A novel non-linear trajectory tracking control strategy is designed for lane changing manoeuvre. First, a dynamic trajectory planning strategy is proposed to update the desired trajectory according to the real-time information acquired through vehicle-to-vehicle communications. Second, a robust adaptive non-linear fuzzy backstepping controller is presented to produce the generalised forces/moment of autonomous electric vehicles, and the stability of this proposed adaptive controller is proven by the Lyapunov theory. Then, the quadratic optimisation goal function of tire energy dissipated power is constructed, and the optimal control allocation method is proposed to produce the desired longitudinal and lateral tire forces of autonomous electric vehicles. Finally, simulation results manifest that the proposed adaptive control strategy has the distinguished tracking performance.

Robust Adaptive Backstepping Controller Design for Aircraft Autonomous Short Landing in the Presence of Uncertain Aerodynamics

AbstractTo achieve tracking performance and robustness simultaneously during an aircraft’s autonomous short landing phase, a three-dimensional automatic landing system designed using robust adaptiv...

Wire rope tension control of hoisting systems using a robust nonlinear adaptive backstepping control scheme

This paper concerns wire rope tension control of a double-rope winding hoisting system (DRWHS), which consists of a hoisting system employed to realize a transportation function and an electro-hydraulic servo system utilized to adjust wire rope tensions. A dynamic model of the DRWHS is developed in which parameter uncertainties and external disturbances are considered. A comparison between simulation results using the dynamic model and experimental results using a double-rope winding hoisting experimental system is given in order to demonstrate accuracy of the dynamic model. In order to improve the wire rope tension coordination control performance of the DRWHS, a robust nonlinear adaptive backstepping controller (RNABC) combined with a nonlinear disturbance observer (NDO) is proposed. Main features of the proposed combined controller are: (1) using the RNABC to adjust wire rope tensions with consideration of parameter uncertainties, whose parameters are designed online by adaptive laws derived from Lyapunov stability theory to guarantee the control performance and stability of the closed-loop system; and (2) introducing the NDO to deal with uncertain external disturbances. In order to demonstrate feasibility and effectiveness of the proposed controller, experimental studies have been conducted on the DRWHS controlled by an xPC rapid prototyping system. Experimental results verify that the proposed controller exhibits excellent performance on wire rope tension coordination control compared with a conventional proportional-integral (PI) controller and adaptive backstepping controller.

Trajectory Tracking Control of Autonomous Quadrotor Helicopter Using Robust Neural Adaptive Backstepping Approach

AbstractThis paper presents the design and analysis of a robust neural adaptive backstepping control (RNABC) for an autonomous quadrotor helicopter perturbed by time-varying external disturbances. ...

Robust adaptive backstepping control for hierarchical multi‐agent systems with signed weights and system uncertainties

This study is concerned with the robust adaptive backstepping control for hierarchical multi-agent systems with signed weights and non-linear system uncertainties. The considered network model is divided into several levels, and the corresponding Laplacian matrix in each level may be non-positive definiteness due to the existence of signed weights, which can lead to the instability of the whole system. To solve this problem, two definitions on recovery Laplacian and controlled Laplacian are firstly introduced, and by combining with a novel graph theoretical result, an effective robust backstepping control scheme is then developed to ensure the stability of hierarchical multi-agent systems. In particular, this study is a generalisation of the prior works with non-negative weights. Finally, an example on hierarchical multi-vehicle systems is given to demonstrate the validity of the proposed method.

Improved robust adaptive backstepping control approach on STATCOM for non‐linear power systems

The problem of large disturbance attenuation in the single-machine infinite-bus system with a static synchronous compensator (STATCOM) is proposed to be alleviated by an improved robust backstepping algorithm, which utilises error compensation and sliding mode variable structure control methods to achieve stability. For STATCOM systems with internal and external disturbances, the adaptive backstepping method is used to construct the storage function, and a non-linear L 2 gain interference rejection controller and parameter substitution law are applied simultaneously. The improved method differs from the traditional adaptive backstepping design in terms of error-compensation design methodology, in order to ensure robustness and insensitivity to large disturbances of the STATCOM system, and furthermore, preserving useful non-linearities and the real-time estimation of uncertain parameters. Finally, a simulation demonstrates that the improved method is more efficient than traditional adaptive backstepping with respect to the speed of adaptation and the response of the STATCOM system. Hence, the effectiveness and practicality of the proposed control method is demonstrated by engineers in applications.

Robust adaptive backstepping control for a class of constrained non-affine nonlinear systems via self-organizing Hermite-polynomial-based neural network disturbance observer

The article proposes a robust control approach based on self-organizing Hermite-polynomial-based neural network disturbance observer for a class of non-affine nonlinear systems with input saturation, state constraint, and unknown compound disturbance. Using Taylor series expansion, a hyperbolic tangent function, the non-affine nonlinear system with input saturation is transformed into time-varying affine system without input saturation, which can reduce step n + 1 of the backstepping technique compared with conventional method. Next, a self-organizing Hermite-polynomial-based neural network disturbance observer is proposed to estimate the compound disturbance online. Then, the auxiliary systems are designed to solve state constraint for subsystems, and hyperbolic tangent function is used to approximate the saturated control input. Simulation results proved the effectiveness of the proposed control scheme.