Command-filtered Backstepping Research Articles

Practical observer-based path following control of the guide ship with propeller and rudder in the underway replenishment

ABSTRACT A path following control strategy is proposed for the guide ship in the underway replenishment without velocity measurements. Firstly, the detailed models of the ship and external disturbances are established. Secondly, an extended state observer is designed to estimate the linear speeds and yaw rate of the ship based on the information of position, heading, propeller speed and rudder angle. With the estimated linear speeds, the sideslip angle led by the disturbances is obtained and thus can be compensated by the update law of the desired heading. Also, an augmented state in this observer is constructed to approximate the system uncertainties and disturbances. Thirdly, two command filter backstepping controllers companied with the observer are proposed to directly control the propeller and rudder, respectively. For the proposed control system, its stability is proved by using the Lyapunov theory. Finally, the effectiveness of the proposed method is demonstrated by a simulation example.

Adaptive Command-Filtered Fuzzy Nonsingular Terminal Sliding Mode Backstepping Control for Linear Induction Motor

This paper puts forward a projection-based adaptive command filtered fuzzy nonsingular terminal sliding mode backstepping (PACFTB) control method for the speed control of the linear induction motor (LIM) with unknown end effects. Firstly, the technique of fuzzy logic systems (FLS) is investigated to approximate the nonlinear components of the LIM’s mathematical model, which reduces the difficulty and cost of controller design. Then, a constrained command-filtered backstepping controller is designed with a filtering compensator compensating for the inherent error of constrained filter. Moreover, the nonsingular terminal sliding mode control method is combined in the controller design for its advantages of finite-time convergence of the system, and the projection-operator-based adaptive laws are established at the same time. Finally, the stability analysis proves that the boundedness and stability of all signals can be ensured with the proposed PACFTB controller, and the simulation results along with experiment results verify that the proposed control strategy has better control performance than the conventional command filter backstepping and PI controller.

Prescribed Performance Adaptive Neural Compensation Control for Intermittent Actuator Faults by State and Output Feedback.

Due to the existing effects of intermittent jumps of unknown parameters during operation, effectively establishing transient and steady-state tracking performances in control systems with unknown intermittent actuator faults is very important. In this article, two prescribed performance adaptive neural control schemes based on command-filtered backstepping are developed for a class of uncertain strict-feedback nonlinear systems. Under the condition of system states being available for feedback, the state feedback control scheme is investigated. When the system states are not directly measured, a cascade high-gain observer is designed to reconstruct the system states, and in turn, the output feedback control scheme is presented. Since the projection operator and modified Lyapunov function are, respectively, used in the adaptive law design and stability analysis, it is proven that both schemes can not only ensure the boundedness of all closed-loop signals but also confine the tracking errors within prescribed arbitrarily small residual sets for all the time even if there exist the effects of intermittent jumps of unknown parameters. Thus, the prescribed system transient and steady-state performances in the sense of the tracking errors are established. Furthermore, we also prove that the tracking performance under output feedback is able to recover the tracking performance under state feedback as the observer gain decreases. Simulation studies are done to verify the effectiveness of the theoretical discussions.

A Novel Command-Filtered Adaptive Backstepping Control Strategy with Prescribed Performance for Photovoltaic Grid-Connected Systems

With the aim of solving the power fluctuation and bus voltage instability problems caused by external environment variations in the photovoltaic grid-connected system, a prescribed performance-based adaptive backstepping controller is proposed for the system to regulate the bus voltage and the inverter current. First, the mathematical model of the grid-connected inverter is established, in which the uncertain system parameters are estimated via a designed projection-based adaptive law. Then, the command-filtered backstepping sliding mode control method is applied to the system for power regulation. In order to achieve favorable tracking performance, the prescribed performance technique is introduced in the voltage regulation strategy by constraining the compensated voltage tracking error within a certain range from a novel point of view. Finally, the simulation is carried out considering the variations of environmental situations, and the obtained results demonstrate the sound performance of the prescribed performance-based control strategy with respect to the photovoltaic grid-connected system.

Nonlinear Excitation Control of Diesel Generator: A Command Filter Backstepping Approach

This article proposes an alternate approach following command-filtered backstepping (CFBS) principle to control the terminal voltage and stabilize the speed of a diesel generator through the excitation system. This controller eliminates the errors introduced due to differentiation of input signal and is, therefore, better compared to the conventional backstepping control technique. The global stability conditions of the overall closed-loop system are established using Lyapunov criteria. The parameters of the controller are optimized using a comprehensive learning particle swarm optimization technique. The performance of the proposed controller is compared with two other controllers, which include a classical proportional integral derivative (PID) and ΔΣ-based PID controllers. The performance comparison using percentage overshoot and settling time demonstrate that the proposed controller is superior and could effectively control the terminal voltage and stabilize the speed under various fault conditions.

Neural network-based finite-time adaptive tracking control of nonstrict-feedback nonlinear systems with actuator failures

In this paper, the finite-time adaptive fault-tolerant tracking control for nonstrict-feedback nonlinear systems (NSFNSs) is investigated. To avoid the problem of “complexity explosion”, a novel finite-time command filter is introduced to generate command signals and their derivatives. The fractional order error compensation mechanism (ECM) is designed to quickly compensate the effect of filter error. By combining the approximation abilities of neural networks and command filtered backstepping (CFB) approach, a finite-time adaptive control strategy is established. It guarantees that the output tracking error approaches to a sufficiently small region of the original point within finite-time, and all signals of the closed-loop system are finite-time semi-globally uniformly ultimately bounded (SGUUB). Finally, two simulation examples are supplied to verify the effectiveness of the proposed control method.

Finite-Time Adaptive Fuzzy Control for MIMO Nonlinear Systems With Input Saturation via Improved Command-Filtered Backstepping

In this article, the problem of finite-time adaptive fuzzy tracking control for multi-input and multi-output (MIMO) nonlinear systems with input saturation is investigated. The new finite-time command filter is introduced for generating command signals and their derivatives to work out the matter of “explosion of complexity,” and the modified fractional power-based error compensation mechanism (ECM) serves as removing the effect of filter error. Then, the finite-time adaptive control scheme is established via the backstepping recursive design technique. It guarantees all the signals of the closed-loop system (CLS) are finite-time bounded while the output tracking errors are regulated to a sufficiently small neighborhood of the origin in finite time. Finally, the effectiveness of the proposed finite-time control scheme is verified by a numerical comparison example.

Neural Network-Based Finite-Time Command Filtering Control for Switched Nonlinear Systems With Backlash-Like Hysteresis.

This brief is concerned with the finite-time tracking control problem for switched nonlinear systems with arbitrary switching and hysteresis input. The neural networks are utilized to cope with the unknown nonlinear functions. To present the finite-time adaptive neural control strategy, a new criterion of practical finite-time stability is first developed. Compared with the traditional command filter technique, the main advantage is that the improved error compensation signals are designed to remove the filtered error and the Levant differentiators are introduced to approximate the derivative of the virtual control signal. The finite-time adaptive neural controller is proposed via the new command filter backstepping technique, and the tracking error converges to a small neighborhood of the origin in finite time. Finally, the simulation results are provided to testify the validity of the proposed method.

Command-Filter-Based Finite-Time Adaptive Control for Nonlinear Systems With Quantized Input

This article considers the finite-time adaptive control problem of nonlinear systems with quantized input signal. Compared with existing results, the quantized parameters are unknown and the bound of the disturbance is not required. By utilizing the command filter backstepping method, an adaptive switching-type controller is designed and a novel switching mechanism is also proposed. By regulating the controller parameters online, practical finite-time stability can be guaranteed for the closed-loop system. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.

Finite-Time Tracking Control for Nonlinear Systems via Adaptive Neural Output Feedback and Command Filtered Backstepping.

This article is concerned with the tracking control problem for uncertain high-order nonlinear systems in the presence of input saturation. A finite-time control strategy combined with neural state observer and command filtered backstepping is proposed. The neural network models the unknown nonlinear dynamics, the finite-time command filter (FTCF) guarantees the approximation of its output to the derivative of virtual control signal in finite time at the backstepping procedure, and the fraction power-based error compensation system compensates for the filtering errors between FTCF and virtual signal. In addition, the input saturation problem is dealt with by introducing the auxiliary system. Overall, it is shown that the designed controller drives the output tracking error to the desired neighborhood of the origin at a finite time and all the signals in the closed-loop system are bounded at a finite time. Two simulation examples are given to demonstrate the control effectiveness.

Distributed Adaptive Neural Consensus Tracking Control for Multiple Euler-Lagrange Systems with Unknown Control Directions

This paper investigates the distributed adaptive neural consensus tracking control for multiple Euler-Lagrange systems with parameter uncertainties and unknown control directions. Motivated by the Nussbaum-type function and command-filtered backstepping technique, the error compensations and neural network approximation-based adaptive laws are established, which can not only overcome the computation complexity problem of backstepping but also make the consensus tracking errors reach to the desired region although the control directions and system nonlinear dynamics are both unknown. Numerical example is given to show the proposed algorithm is effective at last.

A Comparative Study of Different Control Structures for Flight Control With New Results

This paper presents several different control structures that facilitate flight control and does a comparison between them. Specifically, this paper considers command-filtered backstepping, nonlinear dynamic inversion (NDI), and a new decoupled approach that decouples the rotational and translational dynamics by estimating the higher order derivatives of the angle of attack and sideslip angle. The latter is also augmented by exploiting a feedback of the control deficiency resulting in improved performance. A series of simulations is performed to gage the performance of different controllers, showing the performance in the case of sensor noise, when performing aggressive maneuvers, when exposed to wind disturbances, as well as when there are model imperfections. The main finding is that all control structures work well for flight control, but the new decoupled method is able to improve the performance. A major reason for the improvement is that the decoupling method alleviates the tuning of the control gains, thus allowing for faster response through suitable gains.

Adaptive Neural Output Feedback Compensation Control for Intermittent Actuator Faults Using Command-Filtered Backstepping.

Effectively compensating unknown intermittent actuator faults in uncertain decentralized nonlinear systems is a very difficult problem, and very few results have been obtained. In this article, to address this issue, an adaptive neural output feedback compensation control scheme based on command-filtered backstepping is developed. First, we design a bank of observers to estimate the system states and utilize neural networks with random hidden nodes to approximate the unknown functions of these observers. Second, a smooth projection algorithm is used to online update estimated parameters in the controllers such that the possible ceaseless increase in the estimated parameters caused by intermittent actuator faults can be eliminated. Due to the presence of intermittent jumps of unknown parameters, a modified Lyapunov function is developed to analyze the system stability. It is proved that the boundedness of all closed-loop system signals is ensured and the ultimate bound of the tracking error depends on design parameters, adjustable jumping amplitude of Lyapunov function, and minimum fault time interval. Third, by analyzing the system transient performance, the peaking phenomenon at the starting instant of the system operation can be removed, and a root mean square type of bound is established to illustrate that the transient tracking error performance is tunable by design parameters. Finally, simulations studies are done to illustrate the effectiveness of the theoretical results.

Command-Filtered Backstepping Integral Sliding Mode Control with Prescribed Performance for Ship Roll Stabilization

In this paper, a novel, robust fin controller based on the backstepping control strategy and sliding mode control is proposed to handle the problem of ship roll stabilization. First, the mathematical model of the fin control system is established, including the modeling errors and the external disturbances generated by sea waves. In order to address the side effects caused by differential expansion, a command-filter is implemented within the backstepping controller design. By introducing a new performance function and a corresponding error transformation, the compensated tracking error can be bounded to achieve the desired prescribed dynamic and steady-state responses. The sliding mode disturbance rejection control with prescribed performance is realized by combining the disturbance observer. Simulations are presented to demonstrate the effectiveness of the proposed control scheme.

A new control design for a morphing UAV based on disturbance observer and command filtered backstepping techniques

Morphing unmanned aerial vehicle (UAV) can manipulate its shape for excellent flight performance under different conditions. The most research of the morphing UAV focuses on modeling. However, the issues including nonlinear characteristics, strong couplings, and mismatched disturbances are inevitable, which can lead to a great challenge in controller design. In this paper, a composite anti-disturbance controller is developed for morphing UAV to achieve enhanced flight performance under multiple sources of disturbances. In the inner loop, a nonlinear disturbance observer (DO) is constructed to estimate the inertial forces and moment; while in the outer loop, the command filtered backstepping (CFBS) method is adopted to guarantee the stability of the closed-loop system. The system outputs can promptly track reference signals in the morphing process of the UAV. The novelty is that the disturbance estimations are added into the control laws to compensate the mismatched disturbances. When comparing to the previous methods, the control scheme presented in this study can significantly improve the performance of anti-disturbance. Finally, the effectiveness of the proposed method is illustrated by numerical simulations.

Neuroadaptive containment control of nonlinear multiagent systems with input saturations

SummaryThis paper investigates the output containment tracking problem of nonlinear multiagent systems with mismatched uncertain dynamics and input saturations. A neural network–based distributed adaptive command filtered backstepping (CFB) scheme is given, which can guarantee that the containment tracking errors reach to the desired neighborhood of origin and all signals in the closed‐loop system are bounded. Note that error compensation system and virtual control laws established in CFB only use local information, so the given scheme is completely distributed. Moreover, the applied sliding mode differentiator (SMD) can make the outputs of SMD fast approximate the virtual signal and its derivative at each step of backstepping, which can further improve the control quality. Finally, a simulation example is given to show the effectiveness of the proposed scheme.

Photovoltaics literature survey (no. 149)

Photovoltaics literature survey (no. 149)

Command Filtered Model-Free Robust Control for Aircrafts With Actuator Dynamics

In this paper, a command filtered model-free robust (CFMFR) controller is proposed to regulate attitude angles of an aircraft with parameter uncertainties and disturbances to track the given reference signals. For the first subsystem of the controlled plant, the incremental nonlinear dynamic inversion (INDI) is applied to design incremental virtual control law. While the time-delayed control (TDC) method is used to design the control law by integrating sliding-mode technique considering the nominal value of the control effectiveness matrix is unknown. The adaption laws of the control gain are constructed by Lyapunov control theorem. Not only the actuator dynamics of the control surfaces are considered, but also the noises, biases, and time delays of the measurements of the control surface deflection angles are taken into account. Therefore, the command-filtered backstepping is utilized to compensate for the actuator dynamics and filtered errors, and the modified stable linear filter is developed to handle the measurement errors. The stability of the whole closed-loop system, including the compensated signals which are the outputs of the stable linear filters, is analyzed by using Lyapunov theory. The numerical simulation results demonstrate effectiveness of the proposed CFMFR control approach with updating matrix.

A novel adaptive command-filtered backstepping sliding mode control for PV grid-connected system with energy storage

In order to solve the problems of power fluctuation in the photovoltaic (PV) grid-connected system and the nonlinearity in the model of inverters, a projection-based adaptive backstepping sliding mode controller with command-filter is designed in the system to adjust the DC-link voltage and the AC side current in the PV grid-connected system. Firstly, the mathematical model of the inverter in PV system is established, then backstepping control method is applied to control it, and the command filter is added to the controller to eliminate the differential expansion of the backstepping controller. Furthermore, the adaptive law based on Lyapunov stability theory is designed to estimate the uncertain parameters in the grid-connected inverter. A projection algorithm is introduced into the adaptive controller due to the demand of guaranteeing the bounded estimated value. Additionally, a sliding mode controller is increased to improve its robustness in this system. Considering the influence of irradiation and temperature changes, a battery energy storage system (BESS) is applied on the DC side to suppress the fluctuation of the output power of the PV system. Finally, the simulation results demonstrate that the presented strategy can control precisely the grid-connected inverter.

Learning from neural control for non-affine systems with full state constraints using command filtering

ABSTRACT This paper focusses on learning from command filtering-based adaptive neural control for non-affine nonlinear systems with full state constraints. This paper utilises a novel transformed function to convert the origin full-state constrained problem into an equivalent unconstrained one. By combining command-filtered backstepping, a novel adaptive neural control scheme is proposed to guarantee that all closed-loop signals are uniformly ultimately bounded and all states stay in the predefined bounded regions. Subsequently, by verifying the partial persistent excitation condition of the radial basis function neural network, neural weight estimates are proven to converge to the ideal weights and the convergent weights are stored in the constant values. Using the stored knowledge, a neural learning control scheme is developed for similar control tasks, which can improve control performances without the violation of full state constraints. Simulation studies are performed to illustrate the validity of the proposed scheme.