Neural-Networks-Based Prescribed Tracking for Nonaffine Switched Nonlinear Time-Delay Systems.

This paper proposes a global tracking control method for underactuated ships with input and velocity constraints using the dynamic surface control (DSC) method, where the control structure is formed in a modular way that cascaded kinematic and dynamic linearizations can be achieved similarly as in the backstepping method. First, the first step linearization of the kinematics determines the pseudo (or auxiliary) surge velocity and yaw angle, which are used as the commands for the second-step linearization. Then, in the second-step linearization of dynamics, the actual torque inputs are designed to make the actual surge velocity and yaw angle follow these pseudo commands to achieve the position and yaw angle tracking. By employing the dynamic surface control method in the design of each kinematic and dynamic linearization law, we can obtain a control structure that is much simpler than the previous backstepping-based controllers such that it is beneficial from the practical application viewpoint. In addition, it is possible to track general reference trajectories, i.e., the reference yaw velocity need not be persistently exciting and there is no restriction on the initial yaw tracking error. In particular, global tracking control is achieved even in the presence of input and velocity constraints, unlike the DSC method which introduces the several filters in the backstepping design procedure to avoid the model differentiation and make it easier to be implemented and usually has semiglobal tracking performance. Finally, the stability analysis and numerical simulations are performed to confirm the effectiveness of the proposed scheme.

Adaptive output feedback dynamic surface control of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign

This brief considers the distributed finite-time consensus tracking control problem for nonlinear multi-agent systems with false data injection (FDI) attacks. Firstly, since instability is inevitable when false datas are injected into the researched system, a security control algorithm combining the modified coordinate transformation and the dynamic surface control (DSC) method is developed to eliminate the negative impact of FDI attacks and reduce the burden caused by iterative computations of virtual controls. Besides, there are unknown nonlinearities in the dynamics of each agent, which can not be directly handled by the traditional control design. Thus, the neural-network-based approximation technique is introduced to compensate the unknown nonlinearities. In summary, a novel controller is constructed for each agent, which guarantees the consensus of the outputs of all agents can be achieved and that all the signals of the studied system are bounded within a finite-time period. To prove the correctness of the designed security control algorithm, a simulation example of the multi-agent system composed of multiple single-link robots is presented.

In this paper, an adaptive quantized output feedback event‐triggered (ET) distributed consensus control approach for an uncertain nonstrict nonlinear switched fractional‐order (FO) multi‐agent system (SFOMAS) subject to communication limitation is proposed. By relying on the common Lyapunov function method, the switch signals are arbitrarily, and no information regarding the switching instants is required. The consensus error of each agent has its own asymmetric time‐varying constraints (ATCs). By adopting the common barrier Lyapunov function (BLF), violation of these constraints is prevented. For each agent, different input nonlinearities can be considered in different modes, and the controllers need no information about their types and characteristics. The communication burden between outputs sensors and the controllers is reduced by applying uniform‐logarithmic‐hysteresis quantizers to the outputs signals. By using FO filters, which can be considered as a FO version of the dynamic surface control (DSC) method, the so‐called explosion of complexity is avoided and the discontinuity problem of FO derivatives of virtual controllers due to outputs quantization is resolved. Observers are designed to estimate the unmeasured states. Also, function approximators (FAs) are employed to approximate uncertainties. A novel lemma that correlates observer states to consensus errors and their constraints, FO filters errors, adaptive parameters and backstepping error surfaces, has been proposed to establish the observer stability. To reduce the communication burden between the controllers and actuators, relative threshold ET mechanisms have been applied to control signals. The combination problem of unmeasured states with the mentioned input constraints for SFOMASs is coped with another novel lemma. Utilizing the common Lyapunov functions (CLFs) and the backstepping technique, virtual controllers, adaptive law and control signal for each agent have been designed and boundedness of closed‐loop signals and avoidance of Zeno behavior have been proved. The effectiveness of the proposed control scheme is demonstrated through a simulation study.

Modular neural dynamic surface control for position tracking of permanent magnet synchronous motor subject to unknown uncertainties

This paper introduces a novel control strategy for managing the uncertainties in flexible joint manipulators, incorporating a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Surface Control (ADSC). This strategy innovatively utilizes RBFNN to precisely approximate uncertain system dynamics and integrates a nonlinear damping term to effectively counteract external disturbances, enhancing the overall control accuracy. We have also developed an adaptive law that updates neural network weights and system parameters in real-time, ensuring the system's adaptability to dynamic changes. The application of the Lyapunov method ensures that all signals within the closed-loop system remain semi-globally uniformly bounded, significantly reducing tracking errors. Moreover, we introduce the use of Long Short-Term Memory (LSTM) networks for predictive analysis of state data, which further confirms the robustness and effectiveness of our control method through extensive simulations. The distinctive integration of these technologies and their practical validation through comparative simulations underscore the innovative aspects of our approach in addressing real-world challenges in flexible manipulators.

This article presents a novel composite neural learning fault-tolerant algorithm to implement the path-following activity of underactuated vehicles with event-triggered input. With the input event-triggered mechanism, the dominant superiority is to reduce the communication burden in the channel from the controller to actuators. In the proposed scheme, the system uncertainties are dealt with in the fusion of the neural networks (NNs) and the dynamic surface control (DSC) method. The serial-parallel estimation model (SPEM) is constructed to estimate the error dynamics, where the derived prediction error could improve the compensation effect of the NNs. As for the gain uncertainties and the unknown actuator faults, four adaptive parameters are designed to stabilize the related perturbation and not be affected by the triggering instants. Based on the direct Lyapunov theorem, considerable efforts have been made to guarantee the semiglobal uniformly ultimately bounded (SGUUB) stability of the closed-loop system. Finally, comparison and practical experiments are illustrated to verify the superiority of the proposed algorithm.

Switched-observer-based adaptive DSC design of nonstrict-feedback switched stochastic nonlinear time-delay systems

Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

This article focuses on an adaptive dynamic surface tracking control issue of nonlinear multiagent systems (MASs) with unmodeled dynamics and input quantization under predefined accuracy. Radial basis function neural networks (RBFNNs) are employed to estimate unknown nonlinear items. A dynamic signal is established to handle the trouble introduced by the unmodeled dynamics. Moreover, the predefined precision control is realized with the aid of two key functions. Unlike the existing works on nonlinear MASs with unmodeled dynamics, to avoid the issue of "explosion of complexity," the dynamic surface control (DSC) method is applied with the nonlinear filter. By using the designed controller, the consensus errors can gather to a precision assigned a priori. Finally, the simulation results are given to demonstrate the effectiveness of the proposed strategy.

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multi-output (MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO non-affine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem, and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible controller singularity problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control (DSC) method is applied to solve the problem of explosion of complexity in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.

Robust time-varying formation control for underactuated autonomous underwater vehicles with disturbances under input saturation

With the demand for energy efficiency in electrohydraulic servo systems (EHSS), the separate meter-in and separate meter-out (SMISMO) control system draws massive attention. In this paper, the SMISMO control system is decoupled completely into two subsystems by the proposed indirect adaptive robust dynamic surface control (IARDSC) method. Indirect adaptive robust control (IARC) is proposed to address the internal parameter uncertainties and external disturbances. Dynamic surface control (DSC) is utilized in the design procedure of IARC to deal with the inherent ‘explosion of terms’ problem. The proposed IARDSC simplifies the design procedure and decreases the computational cost of the controller. Besides, a faster parameter estimation scheme is proposed to adapt to the parameter change for a better estimation performance. Finally, experimental results show that the proposed IARDSC can achieve a good parameter estimation and trajectory tracking performance. Meanwhile, two energy saving techniques are discussed.

The finite‐time command filter tracking control for a class of nonstrictly feedback nonlinear systems with unmodeled dynamics and full‐state constraints is investigated in this paper. The hyperbolic tangent function is used as a nonlinear mapping technique to solve the obstacle of the full‐state constraints. A new adaptive finite time control method is proposed through command filtering reverse engineering, and the shortcomings of the dynamic surface control (DSC) method are overcome by the error compensation mechanism. Dynamic signal is designed to handle dynamical uncertain terms. Normalization signal is designed to handle input unmodeled dynamics. Unknown nonlinear functions are approximated by radial basis function neural networks. Based on the Lyapunov stability theory, it is proved that all signals in the closed‐loop system are semi‐globally consistent and finally bounded and the output tracking error converges in finite time. Two numerical examples are utilized to verify the effectiveness of the proposed control approach.

In this paper, an adaptive dynamic surface integral sliding mode fault-tolerant controller is designed for the multimachine power system with static var compensator (SVC) to overcome the problem of actuator failure. The main features of the proposed method are as follows: (1) By combining the dynamic surface control (DSC) method with integral sliding mode (ISM), the tracking errors of the system converge to the neighborhood of zero within a finite time, and the convergence speed, tracking accuracy, and anti-interference ability of the system are also significantly improved. (2) By introducing the failure factors, an adaptive fault-tolerant controller is designed to ensure the stability of the entire system after partial failure of the actuator. (3) By estimating the norm of the ideal weight vector of the radial basis function neural networks (RBFNNs), the computational burden of the controller is reduced. Finally, the simulation results show the effectiveness of the proposed control scheme.