DSC-backstepping based robust adaptive fuzzy control for a class of strict-feedback nonlinear systems

This brief presents a dynamic memory event-triggered mechanism based adaptive control strategy for a class of strict-feedback nonlinear systems. Firstly, the dynamic memory event-triggered mechanism (DMETM) is established, rigorous proof demonstrates that the triggering intervals of the proposed DMETM are larger than that of the memoryless dynamic event-triggered mechanism. Furthermore, the adaptive control strategy is designed via the dynamic surface control, by which the “explosion of complexity” in the backstepping design process is avoided. Additionally, the proposed DMETM based adaptive dynamic surface controller can guarantee the closed loop system to be semi-globally uniformly ultimately bounded (SGUUB). Finally, the simulation results illustrate the validity of the proposed control strategy.

This paper proposes a new method based on reinforcement learning to design robust adaptive tracking control laws with optimality for multi-wheeled mobile robots synchronization in communication graph without requiring knowledge of drift tracking terms in node dynamics. Wheeled mobile robots are controlled by integrated kinematic and dynamic laws. Actor critic structures in the control scheme for every node is proposed such as only single NN is used to reduce computational cost and storage resources, but parameters of critic and actors are updated synchronously. Novel tuning laws for the NNs are designed not only to learn online adaptive solutions of cooperative Hamilton-Jacobi-Isaacs (HJI) equation on purpose of approximating optimal cooperative tracking performance index functions and robust direct adaptive tracking control inputs as well as worst case disturbances but also to guarantee closed-loop stability in real-time. The convergence and stability of the overall system are proven by Lyapunov techniques. The simulation results on multi-wheeled mobile robots systems verify the effectiveness of the proposed controller.

Attitude tracking control for variable structure near space vehicles based on switched nonlinear systems

In this paper, robust adaptive control problem is discussed for a class of output feedback nonlinear switched systems with unmodeled dynamics and time-varying constraints. Based on the common Lyapunov function (CLF), an adaptive output feedback dynamic surface control (DSC) scheme is developed. Radial basis function neural networks are utilized to approximate unknown continuous functions, and K-filters and a dynamic signal are designed to estimate the unmeasured states and deal with the dynamic uncertainty. An asymmetric barrier Lyapunov function (ABLF) is introduced to handle output constraint. It is proved that the designed controller can guarantee the semi- global uniform ultimate boundedness (SGUUB) for all the signals in the closed-loop system under arbitrary switchings. The simulation results are provided to demonstrate the effectiveness of the proposed method.

This paper is concerned with the problem of adaptive neural output-feedback control for a class of nonlinear strict-feedback systems. In this research, radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions and a state observer is constructed to estimate the immeasurable state variables. An adaptive neural output feedback control scheme is proposed via backstepping technique. The proposed design method can guarantee semi-global uniform ultimate boundedness (SGUUB) of all signals in the closed-loop system, and ensures the observer errors converge to a small neighborhood. At last, a simulation example is included to show the effectiveness of the proposed method.

In this paper, we investigate the trajectory tracking control problem of surface ships subject to unknown parameters, unknown time-varying disturbances, and input saturation. The input saturation is approximated using a Gaussian error function. The compounded uncertain term caused by the unknown parameters and disturbances is transformed into a linear parametric form with a single parameter called virtual parameter. Then, a novel robust adaptive ship trajectory tracking control law is designed using the adaptive vector-backstepping design method, where the adaptive law online provides the estimation of the virtual parameter. Further, a finite-time control scheme is developed injecting a power function vector of the position and velocity errors into the proposed control scheme such that the control performance is improved. Both our control schemes are simple to compute and easy to implement in engineering applications. Theoretical analysis indicates that the designed control laws can force the surface ship to track the desired trajectory while guaranteeing the global uniform ultimate boundedness of all the signals in the closed-loop trajectory tracking control system of surface ships. Simulation results and comparison verify the effectiveness of our novel robust adaptive trajectory tracking control schemes.

Adaptive robust servo constraint tracking control for an underactuated quadrotor UAV with mismatched uncertainties

In this paper, the problem of adaptive neural control is considered for a class of strict-feedback nonlinear systems with unmodeled dynamics, dynamic disturbances and unknown input saturation. During the controller design, radial basis functions(RBF) neural networks are applied to model the unknown nonlinearities, and an adaptive neural control scheme is developed via backstepping, which guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square. A simulation example is provided to show the effectiveness of the proposed control scheme.

In this paper, a novel adaptive NN control scheme is proposed for a class of uncertain single-input and single- output(SISO) nonlinear time-delay systems with the lower triangular form. RBF NNs are used to approximate unknown nonlinear functions, then the adaptive NN tracking controller is constructed by combining Lyapunov-Krasovskii functionals and the dynamic surface control(DSC) technique along with the minimal-learning-parameters(MLP) algorithm. The proposed controller guarantees uniform ultimate boundedness of all the signals in the closed-loop system, while the tracking error converges to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters is reduced dramatically to one, both problems of “explosion of complexity” and “curse of dimension” are solved simultaneously.

This paper considers the adaptive neuro-fuzzy control scheme to solve the output tracking problem for a class of strict-feedback nonlinear systems. Both asymmetric output constraints and input saturation are considered. An asymmetric barrier Lyapunov function with time-varying prescribed performance is presented to tackle the output-tracking error constraints. A high-gain observer is employed to relax the requirement of the Lipschitz continuity about the nonlinear dynamics. To avoid the "explosion of complexity", the dynamic surface control (DSC) technique is employed to filter the virtual control signal of each subsystem. To deal with the actuator saturation, an additional auxiliary dynamical system is designed. It is theoretically investigated that the parameter estimation and output tracking error are semi-global uniformly ultimately bounded. Two simulation examples are conducted to verify the presented adaptive fuzzy controller design.

Around the cooperative path-following control for the underactuated surface vessel (USV) and the unmanned aerial vehicle (UAV), a logic virtual ship-logic virtual aircraft (LVS-LVA) guidance principle is developed to generate the reference heading signals for the USV-UAV system by using the “virtual ship” and the “virtual aircraft”, which is critical to establish an effective correlation between the USV and the UAV. Taking the steerable variables (the main engine speed and the rudder angle of the USV, and the rotor angular velocities of the UAV) as the control input, a robust adaptive neural cooperative control algorithm was designed by employing the dynamic surface control (DSC), radial basic function neural networks (RBF-NNs) and the event-triggered technique. In the proposed algorithm, the reference roll angle and pitch angle for the UAV can be calculated from the position control loop by virtue of the nonlinear decouple technique. In addition, the system uncertainties were approximated through the RBF-NNs and the transmission burden from the controller to the actuators was reduced for merits of the event-triggered technique. Thus, the derived control law is superior in terms of the concise form, low transmission burden and robustness. Furthermore, the tracking errors of the USV-UAV cooperative control system can converge to a small compact set through adjusting the designed control parameters appropriately, and it can be also guaranteed that all the signals are the semi-global uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed algorithm has been verified via numerical simulations in the presence of the time-varying disturbances.

In this paper, considering the simultaneous influence of multi-source disturbances, system modeling uncertainties and input–output constraints, an adaptive robust attitude tracking control scheme is proposed for near space vehicles (NSVs) which is expressed as a stochastic nonlinear system. A multi-dimensional Taylor polynomial network (MTPN) is utilized to handle the system uncertainties, and the nonlinear disturbance observer (NDO) based on MTPN is designed to estimate the external disturbances. Furthermore, by constructing the auxiliary system to tackle the input saturation and introducing the Tan-type barrier Lyapunov function (TBLF) to solve the output constraint, the constrained control strategy can be obtained. Combining with backstepping control (BC) technique and stochastic control method, an adaptive robust stochastic control scheme is developed based on NDO, MTPN, and auxiliary system, and the closed-loop system stability in the sense of probability is analyzed based on stochastic Lyapunov stability theory. Finally, numerical simulations further demonstrate the feasibility of the proposed tracking control scheme.

In this paper, a new fuzzy adaptive control approach is developed for a class of strict-feedback nonlinear systems with unknown time-delay. Using fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is introduced for state estimation. Under the backstepping design, fuzzy adaptive output feedback control is constructed recursively. In addition, the number of on-line adaptive parameters is not more than the order of the considered systems. It is proved that the proposed backstepping design approach guarantees the semi-global boundedness property for all the signals. Finally, Simulation results are provided to show the effectiveness of the proposed approach.

This paper presents a robust proportional derivative adaptive nonsingular finite-time synergetic tracking control (PDAFS) for a parallel Delta robot system. First, a finite-time synergetic controller combined with a proportional derivative (PD) control is constructed based on an object-oriented model to fulfill the robust tracking control of the robot. Then, an adaptive radial basis function approximation neural network (RBF) is designed to compensate for the effects of uncertainty parameters and external disturbances. Second, a second-order sliding mode (SOSM) differentiator is implemented to reduce the chattering noises due to the low-resolution encoders. Third, the stability theorems of the proposed control scheme are provided, where the Lyapunov stability theory is used to prove the theorems. Then, simulations of the helix trajectory tracking and the pick-and-place task are demonstrated on the Delta robot to validate the advantages of the proposed control scheme. Based on the advances, an implementing control system of the proposed controller is performed to improve the Delta robot’s performance in the experiments.

In this work, the tracking control problem for a class of nonlinear strict-feedback systems with input nonlinearity is addressed. In response to the influence of input nonlinearity, an auxiliary control system is constructed to compensate for it. To process unknown nonlinear dynamics, radial basis function neural networks (RBFNNs) are introduced to approximate them, and some adaptive updating control laws are designed to estimate unknown parameters. Furthermore, during the dynamic surface control (DSC) design process, first-order low-pass filters are introduced to solve the complexity explosion problems caused by repeated differentiation. After that, an NN-based adaptive dynamic surface tracking controller is proposed to achieve the tracking control. By applying the proposed controller, it can be guaranteed that not only the output of the system can track the desired trajectory, but also that the tracking error can converge to a small neighborhood of zero, while all signals of the closed-loop system are bounded. Finally, the effectiveness of the proposed controller is verified through two examples.