Adaptive Backstepping Control System Research Articles

Self-Stabilizing Control Strategy of Stabilized Platform for Rotary Steerable Drilling System Based on Adaptive Backstepping Control

To enhance the robustness of the toolface angle control in a fully rotary steerable drilling tool, a backstepping control law and a drilling fluid flow adaptive law are devised based on the dynamic model of the stable platform. The Lyapunov function is constructed, and it is proven that the adaptive backstepping control system of the stabilized platform is stable. Furthermore, in order to address problems such as the friction dead zone and excessive rotational kinetic energy in the stabilized platform system, which could cause toolface angle oscillations and the rapid rotation of the stabilized platform, we additionally propose an online estimation method for the balancing torque and velocity-angle control switching strategy. By combining the backstepping control law, drilling fluid flow adaptive law, and velocity-angle control switching strategy, a self-stabilizing control strategy for the stabilized platform is established. In comparison with the PID control method, the simulation results show the superiority of the proposed scheme under complex disturbances from a downhole environment. And the drilling simulation experiment results indicate that the proposed control method has a good anti-interference capability under various conditions, including drilling fluid flow rate, inclination angle, and drill collar rotational speed. Therefore, the proposed control method can improve the robustness of the stabilized platform control system.

Research on RBF neural network adaptive control of three-point contactless measuring device for CNC roller grinder

Abstract In order to improve the CNC roll grinder production efficiency, a novel three-point non-contact measurement device is proposed. An adaptive back-stepping control system that combines with a radial basis function neural network (RBFNN) was designed to control the measurement device working position and also to track the periodic reference movement trajectory. In the proposed control system, the RBFNN approximation and tracking ability are used to identify the unknown measuring device dynamic information. Then, the Lyapunov stability theorem is used to derive the adaptive online learning algorithm. All control algorithms are placed in the control chip based on the TMS320F28335 archive. The simulation results show that the proposed control system has a good control effect when applied to the three-point non-contact measuring device in CNC roll grinders.

Higher-order tracking properties of adaptive backstepping control systems

This paper shows that an adaptive backstepping control system with relative degree ρ has the higher-order tracking properties: the ith-order derivatives of the tracking error e(t)=y(t)−ym(t) (between the system output y(t) and its reference trajectory ym(t) whose jth-order derivatives are bounded, for j=0,1,…,ρ) converge to zero asymptotically with time t, for i=1,…,ρ−1, in addition to the commonly-seen zero-order tracking property: e(t) converges to zero. Such new higher-order tracking properties hold for various adaptive backstepping control systems with asymptotic zero convergence of e(t), indicating that the tracking capacity of such systems is more than what has been known.

Performance improvement of modern variable-velocity wind turbines technology based on the doubly-fed induction generator (DFIG)

In the latest decades, Green energies received significant support from the international community and became the key solution for the global environmental issues caused by the increasing consumption of fossil fuels. One of the cleanest sources of renewable energy that allow producing green energy is wind power which is developing with a fast rate and becoming mature and more economically competitive due to the advanced techniques for extraction. Today, the modern wind power systems technology is based on the variable-velocity Doubly Fed Induction Generators (DFIGs). To improve the operation, the production and the stability of the modern variable-velocity wind turbines technology, the current paper proposes an adaptive backstepping control structure for a wind power system based on the doubly-fed Induction generator (DFIG) in power grid-connected mode. This adaptive nonlinear control structure is proposed to cope with nonlinearity and effects of the parameter uncertainties and variations in the DFIG wind power system under study, whose main purpose is the power extract optimization from variable-velocity DFIG wind turbine while enhancing the stability of the whole system. Design of the adaptive nonlinear backstepping control system as rotational velocity and rotor current regulators have been implemented based on the wind power system dynamic model with unknown parameters. The system’s unknown parameters are dynamically estimated through the parameters adaptive update laws formulated during the design process, and these estimation laws together with the control actions for the DFIG-wind turbine are derived in such a way that the stability of the whole system according to the Lyapunov stability theory is ensured. Numerical simulation tests were conducted to evaluate the robustness and effectiveness of the proposed adaptive control structure. The obtained results show clearly that the proposed method has advanced robustness to cope with the system parameter variations and uncertainties.

Linear permanent magnet synchronous motor drive system using AAENNB Control system with error compensation controller and CPSO

Due to nonlinear friction and uncertainty effects of linear permanent magnet synchronous motor (LPMSM), the existing linear controllers cannot achieve good control performance. In order to increase the robustness of system under some uncertainties disturbances, the proposed adaptive amended Elman neural network backstepping (AAENNB) control system with error compensation controller is adopted for controlling the LPMSM drive system. Firstly, the field-oriented control (FOC) is applied to formulate the dynamic equation of the LPMSM drive system. Secondly, a backstepping approach is proposed for controlling the motion of LPMSM drive system. The proposed backstepping control system and the mover position of the LPMSM drive system possess good transient control performance under the uncertainties action for the tracking of periodic references. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) with the adaptive law is proposed to estimate the required lumped uncertainty. Moreover, the error compensation controller with the error estimation law is proposed to compensate the minimum reconstructed error according to Lyapunov stability theorem. Furthermore, to improve convergent speed and to obtain better learning performance, the varied learning rate of the weight in the AENN is regulated by use of the corrected particle swarm optimization (CPSO) algorithm with segment regulation mechanics, that is, the innovativeness for using the CPSO algorithm. At last, the usefulness of the proposed control system is confirmed by experimental results.

Switched reluctance motor circuit drive system using adaptive nonlinear backstepping control with mended recurrent Romanovski polynomials neural network and mended particle swarm optimization

AbstractA switched reluctance motor (SRM) circuit drive system caused many nonlinear effects due to convex construction. The linear control methods were hard to achieve good performance for the SRM circuit drive. The adaptive nonlinear backstepping control system using switching function is proposed for controlling the SRM drive system to obtain good performance. To reduce chattering of control effort, the adaptive nonlinear backstepping control system using adaptive law is proposed to estimate the required lumped uncertainty. When the inertia of the counterweight is varying, this proposed method cannot get a satisfactory performance. The adaptive nonlinear backstepping control system using mended recurrent Romanovski polynomials neural network with adaptive law and error‐estimated law is proposed for controlling the SRM drive system to raise robustness of the SRM drive system. Furthermore, two variable learning rates in the mended recurrent Romanovski polynomials neural network are adopted by using mended particle swarm optimization (PSO) algorithm to speed up parameter's convergence.Finally, comparative performances through some experimental results are verified that the proposed control system has better control performances than those of the proposed methods for the SRM drive system.

Retracted: Adaptive nonlinear backstepping control using mended recurrent Romanovski polynomials neural network and mended particle swarm optimization for switched reluctance motor drive system

A switched reluctance motor (SRM) drive system has highly nonlinear uncertainties owing to a convex construction. It is hard for the linear control methods to achieve good performance for the SRM drive system. An adaptive nonlinear backstepping control system using the mended recurrent Romanovski polynomials neural network and mended PSO with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SRM drive system. Additionally, in accordance with the Lyapunov stability theorem, the adaptive law in the mended recurrent Romanovski polynomials neural network and the error estimated law are established. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the two varied learning rates of the two parameters in the mended recurrent Romanovski polynomials neural network. Finally, some experimental results and a comparative analysis are verified that the proposed control scheme has better control performances for controlling the SRM drive system.

Retracted: SCRIM drive system using adaptive backstepping control and mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization

SummaryDue to air‐gap field harmonic, cogging torque, stator's current time harmonic, and the influence of flux saturation, a six‐phase copper rotor induction motor (SCRIM) drive system has highly nonlinear uncertainties. Thus, the linear control method for the SCRIM drive system is difficult to achieved good performance under the nonlinear uncertainty action. To obtain better control performance, the adaptive backstepping control system using switching function is firstly proposed for controlling the SCRIM drive system to overcome the uncertainty influence. With the proposed control system, the SCRIM drive system holds in robustness to these uncertainties for the tracking of periodic reference trajectories. To enhance the robustness of the SCRIM drive system, the adaptive backstepping control system using adaptive law is proposed for estimating the required lumped uncertainty to reduce chattering phenomenon. When the inertia of the counterweight is varying, this proposed method can perform well in general situations but cannot get a satisfactory performance. The adaptive backstepping control system using mended recurrent Romanovski polynomials neural network with reformed particle swarm optimization (PSO) is thus proposed to estimate the lumped uncertainty and to compensate estimated error for obtaining better control performance. Furthermore, two variable learning rates of the weights in the mended recurrent Romanovski polynomials neural network are adopted by using reformed PSO to speed up parameter's convergence. Finally, some experimental results with comparative control performances are demonstrated, and then, the effectiveness of proposed control system with better control performance is verified for the position tracking of periodic reference inputs.

Precision Motion Control of a Linear Permanent Magnet Synchronous Machine Based on Linear Optical-Ruler Sensor and Hall Sensor

The linear optical-ruler sensor with 1 μm precision mounted in the linear permanent magnet synchronous machine (LPMSM) is used for measuring the mover position of LPMSM in order to enhance the precision of a measured mover position. Due to nonlinear friction and uncertainty effects, linear controllers are very hard to achieve good mover positioning of LPMSM. The proposed adaptive amended Elman neural network backstepping (AAENNB) control system is adopted for controlling the LPMSM drive system to bring about the mover positioning precision of LPMSM. Firstly, a backstepping scheme is posed for controlling the tracing motion of the LPMSM drive system. The proposed backstepping control system, which is applied in the mover position of the LPMSM drive system, possesses better dynamic control performance and robustness to uncertainties for the tracing trajectories. Because of the LPMSM with nonlinear and time-varying dynamic characteristics, an adaptive amended Elman neural network uncertainty observer (AAENNUO) is posed to estimate the required lumped uncertainty. According to the Lyapunov stability theorem, on-line parameter training methodology of the amended Elman neural network (AENN) can be derived by use of adaptive law. The error estimated law is proposed to compensate for the observed error induced by the AENN with adaptive law. Furthermore, to help improve convergence and to obtain better learning performance, the mended particle swarm optimization (PSO) algorithm is utilized for adjusting the varied learning rate of the weights in the AENN. At last, these experimental results, which show better performance, are verified by the proposed control system.

Adaptive TOPSIS fuzzy CMAC back-stepping control system design for nonlinear systems

This paper aims to propose a more efficient control algorithm for nonlinear systems. A novel adaptive Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) fuzzy cerebellar model articulation controller (FCMAC) back-stepping control system is developed. The proposed adaptive TOPSIS FCMAC (ATFCMAC) incorporates a multi-criteria decision analysis with a fuzzy CMAC structure to determine the optimal threshold values for selecting suitable firing nodes, improving the computational efficiency, reducing the number of firing rules, and achieving good performance for nonlinear systems. A back-stepping technique is employed for the control system design. The proposed control system comprises an ATFCMAC and a robust compensator; the ATFCMAC is used to approximate an ideal controller and the robust compensator is used to reduce the influence of residual approximation error between the ideal controller and the ATFCMAC. The parameters of the proposed ATFCMAC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so that the stability of the control system is guaranteed. The simulation and experimental results for a Duffing–Holmes chaotic system and a magnetic ball levitation system are used to verify the effectiveness of the proposed control scheme.

Adaptive Backstepping Control of Six‐Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

AbstractAn adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high‐performance six‐phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field‐oriented six‐phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six‐phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six‐phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32‐bit floating‐point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.

Nonlinear adaptive backstepping control for variable-speed wind energy conversion system-based permanent magnet synchronous generator

In this paper, a robust nonlinear adaptive controller based on backstepping control strategy is adopted for a variable-speed wind energy conversion system with permanent magnet synchronous generator (PMSG). Firstly, field-oriented control law based on PI controllers has been introduced. Nevertheless, it presents a noticeable tracking error; thus, the system control is improved by using a backstepping controller. This method provides a simple and fast tracking. However, it is sensitive to parameter uncertainties and load torque disturbances. Therefore, an adaptive backstepping control system is then designed to compensate the parameter uncertainties of the system. Simulation results validate the effectiveness of the proposed control law.

Review of Course Keeping Control System for Unmanned Surface Vehicle

This paper presents a review of research work done on various aspects of control system approaches of unmanned surface vehicle (USV) in order to improve the course keeping performance. Various methods have been used to produce a course keeping control system for manoeuvring system of USV. However, the review reveals that the adaptive backstepping control system is a powerful tool for the design of controllers for nonlinear systems or transformable to form a tight feedback parameter. It is very suitable for the automated control system of USV in relative motion that involves the disturbances from waves and wind. Fuzzy logic control also had been suggested as an alternative approach for complex systems with uncertain dynamics and those with nonlinearities. This method does not rely on the mathematical models, but the heuristic approach. Further studies may be conducted to combine the control method approach mentioned above to develop a real time system with robust control laws to the motions of a USV in waves, usually at a specific speed, including station keeping or heading in sinusoidal and irregular waves.

Intelligent Control Using the Wavelet Fuzzy CMAC Backstepping Control System for Two-Axis Linear Piezoelectric Ceramic Motor Drive Systems

This study aims to propose a more efficient control algorithm to achieve precision trajectory tracking control for a two-axis linear piezoelectric ceramic motor (LPCM). Since the inherent nonlinear nature and cross-coupling effect of a two-axis LPCM, its accurate model is difficult to obtain; thus, an intelligent adaptive wavelet fuzzy cerebellar model articulation controller backstepping (AWFCB) control system is designed to achieve high precision trajectory tracking control for a two-axis LPCM drive system. A novel wavelet fuzzy cerebellar model articulation controller (CMAC) is proposed in this paper; in some special cases, it can be reduced to a fuzzy system, a fuzzy neural network, a wavelet fuzzy neural network, or a conventional CMAC. The developed wavelet fuzzy CMAC incorporates the wavelet decomposition property and a fuzzy CMAC fast learning ability; thus, it is used for the LPCM control. In the AWFCB control system, a wavelet fuzzy CMAC is used to imitate an ideal backstepping controller, and a smooth compensator is designed to eliminate the residual of the approximation error between the wavelet fuzzy CMAC and the ideal backstepping controller. In order to guarantee the convergence of the tracking error, analytical methods using the Lyapunov function are utilized to derive the adaptation laws to tune the parameters of the control system online. Thus, the stability of the two-axis LPCM control system can be guaranteed. Finally, the experimental results show the precision of the trajectory tracking using AWFCB control. Compared with PID control and adaptive fuzzy sliding-mode control, the AWFCB control can achieve tracking error reduction of about 80%~99% and 48%~97%, respectively.

The Adaptive Backstepping Control of Permanent Magnet Synchronous Motor Supplied by Current Source Inverter

The sensorless control system of permanent magnet synchronous motor (PMSM) fed by current source inverter is presented in this paper. The adaptive backstepping control system and the backstepping speed observer are presented. The control systems are based on multiscalar variables. The control variables are dc-link voltage and the output current vector pulsation. The control system was named the voltage control system because of the fact that the first control variable is voltage in dc-link. The machine inverter is treated as a commutator. It works with constant modulation index. The proposed control system is robust on mechanical and electromagnetical parameters of the motor. The adaptive estimator of the stator resistance is introduced. Such an approach to control of the PMSM motor gives better performance. It is robust to dynamic uncertainties and does not require knowledge of the system parameters. The mathematical equation was introduced. Simple method limitation of control variables is explained. The reference variables must be limited to a constant or various value if the motor system has to be used in the industrial applications. The simulation and experimental test are shown.

TSK Fuzzy CMAC-Based Robust Adaptive Backstepping Control for Uncertain Nonlinear Systems

A Takagi-Suegeno-Kang (TSK) fuzzy cerebellar-model-articulation-controller-based robust adaptive backstepping (TFCRAB) control system is proposed for the uncertain nonlinear systems. This TFCRAB control system is composed of a novel TSK fuzzy cerebellar model articulation controller (TFC) and a robust compensator. The proposed TFC is a generalization of a TSK fuzzy system, a fuzzy neural network, and a conventional cerebellar-model-articulation-controller. It is used as the principal tracking controller to mimic an ideal backstepping controller (IBC). The parameters of TFC are tuned online by the derived adaptation laws based on the Lyapunov stability theorem. The robust compensator is designed to dispel the approximation error between the TFC and the IBC so that the asymptotic stability of the closed-loop system can be guaranteed. Finally, the proposed control system is applied to control a Duffing-Holmes chaotic system and a voice coil motor. From the simulation and experimental results, it is verified that the proposed TFCRAB control scheme can achieve favorable tracking performance and that even the system models of the controlled systems are unknown.

Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems

A novel self-organizing wavelet cerebellar model articulation controller (CMAC) is proposed. This self-organizing wavelet CMAC (SOWC) can be viewed as a generalization of a self-organizing neural network and of a conventional CMAC, and it has better generalizing, faster learning and faster recall than a self-organizing neural network and a conventional CMAC. The proposed SOWC has the advantages of structure learning and parameter learning simultaneously. The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance. Then a SOWC backstepping (SOWCB) control system is proposed for the nonlinear chaotic systems. This SOWCB control system is composed of a SOWC and a fuzzy compensator. The SOWC is used to mimic an ideal backstepping controller and the fuzzy compensator is designed to dispel the residual of approximation errors between the ideal backstepping controller and the SOWC. Moreover, the parameters of the SAWCB control system are on-line tuned by the derived adaptive laws in the Lyapunov sense, so that the stability of the feedback control system can be guaranteed. Finally, two application examples, a Duffing–Holmes chaotic system and a gyro chaotic system, are used to demonstrate the effectiveness of the proposed control method. The simulation results show that the proposed SAWCB control system can achieve favorable control performance and has better tracking performance than a fuzzy neural network control system and a conventional adaptive CMAC.

Robust Position Control of Electro-Hydraulic Actuator Systems Using the Adaptive Back-Stepping Control Scheme

In general, the position control of electro-hydraulic actuator (EHA) systems is difficult because of system uncertainties such as Coulomb friction, viscous friction, and pump leakage coefficient. Even if the exact values of the friction and pump leakage coefficient may be obtained through experiment, the identification procedure is very complicated and requires much effort. In addition, the identified values may not guarantee the reliability of systems because of the variation of the operating condition. Therefore, in this paper, an adaptive back-stepping control (ABSC) scheme is proposed to overcome the problem of system uncertainties effectively and to improve the tracking performance of EHA systems. In order to implement the proposed control scheme, the system uncertainties in EHA systems are considered as only one term. In addition, in order to obtain the virtual controls for stabilizing the closed-loop system, the update rule for the system uncertainty term is induced by the Lyapunov control function (LCF). To verify the performance and robustness of the proposed control system, computer simulation of the proposed control system is executed first and the proposed control scheme is implemented for an EHA system by experiment. From the computer simulation and experimental results, it was found that the ABSC system produces the desired tracking performance and has robustness to the system uncertainties of EHA systems.

A robust control of electro hydrostatic actuator using the adaptive back-stepping scheme and fuzzy neural networks

In general, the position control of electro hydrostatic actuator(EHA) systems is difficult because of the large variation of the effective bulk modulus of the working fluid, which is due to the absence of a heat exchanger like a reservoir tank, the friction between the cylinder and piston, and the external disturbance force. Moreover, it is difficult to identify the values of the effective bulk modulus and friction. In this paper, the variation of the effective bulk modulus, friction, and external disturbance are considered as uncertainties of EHA systems. To solve the problems due to these system uncertainties, an adaptive back-stepping control scheme with fuzzy neural networks(FNNs) is proposed. The effectiveness of the adaptive back-stepping control(ABSC) system with FNNs was compared with those of the standard back-stepping control(BSC) system and the ABSC system through computer simulation.

CMAC-based adaptive backstepping synchronization of uncertain chaotic systems

This study proposes an adaptive backstepping control system for synchronizing uncertain chaotic system by using cerebellar model articulation controller (CMAC). CMAC is a nonlinear network with simple computation, good generalization capability and fast learning property. The proposed CMAC-based adaptive backstepping control (CABC) system uses backstepping method and adaptive cerebellar model articulation controller (ACMAC) for synchronizing uncertain chaotic system. Finally, simulation results for the Genesio system are presented to illustrate the effectiveness of the proposed control system.