Neural networks-based fixed-time control for a robot with uncertainties and input deadzone

Trajectory tracking control for ships with fixed-time prescribed performance considering input saturation and dead zone

To enable underactuated ships to achieve trajectory tracking under unknown external disturbances, model uncertainties, and actuator saturation and dead zone, a fixed-time prescribed performance trajectory tracking control method is designed. Firstly, the position tracking errors are constrained by designing the barrier Lyapunov function, and the prescribed performance function is set as the constraint boundary to address the issue of fixed constraint boundaries in traditional methods. Secondly, RBF neural networks are employed to estimate the model uncertainties, and adaptive laws are used to estimate the upper bound of the composite disturbances. Finally, the controller is designed by incorporating fixed-time convergence theory and further using fixed-time sliding mode surface in order to overcome the shortcomings of traditional control algorithms in terms of slow response and the use of finite-time convergence with respect to the initial state. Through Lyapunov stability analysis, it is proven that all signals in the closed-loop system are bounded, and the velocity tracking errors can achieve global fixed-time convergence. Simulation results demonstrate that the proposed control scheme enables underactuated ship to achieve trajectory tracking even in the presence of input saturation and dead zone. Statistical results show that the performance indicators of the proposed controller are significantly smaller than those of the first group in the comparative experiments, with a shorter settling time. Moreover, compared to traditional saturation handling methods, the input curves of the proposed controller are smoother and more aligned with practical engineering requirements.

Neuro-Adaptive-Based Fixed-Time Composite Learning Control for Manipulators With Given Transient Performance

This article investigates an adaptive neural network (NN) control technique with fixed-time tracking capabilities, employing composite learning, for manipulators under constrained position error. The first step involves integrating the composite learning method into the NN to address the dynamic uncertainties that inevitably arise in manipulators. A composite adaptive updating law of NN weights is formulated, requiring adherence solely to the relaxed interval excitation (IE) conditions. In addition, for the output error, instead of knowing the initial conditions, this article integrates the error transfer function and asymmetric barrier function to achieve the specific performance for position error in both steady and transient states. Furthermore, the fixed-time control methodology and Lyapunov stability criterion are synergistically employed in order to guarantee the convergence of all signals in the manipulators to a compact neighborhood around the origin within a fixed-time. Finally, numerical simulation and experiments with the Baxter robot results both determine the capability of the NN composite learning technique and fixed-time control strategy.

Tracking control via time-varying feedback for an uncertain robotic system with both output constraint and dead-zone input

This paper is devoted to the tracking control for an uncertain robotic system with both output constraint and dead-zone input. Remarkably, the distinctive characters of the system are reflected by system uncertainties and output constraint. First, more serious uncertainties are involved since unknown nonlinear dynamic matrices, external disturbance and the dead-zone input (see unknown slopes and break points therein) are simultaneously considered, but those of the related literature are not. Second, weaker conditions on the output constraint are allowed since the constraint functions considered are only first but not more order continuously differentiable while any their time derivatives are not necessarily available for feedback. This leads to the incapability of the traditional control schemes on this topic. To solve the control problem, a novel control framework is proposed based on time-varying feedback which overcomes the serious system uncertainties while relaxes the conditions on output constraints. Specifically, a state transformation with a time-varying gain is first introduced to derive a new system. Then, by using the traditional backstepping method with the introduction of the time-varying gain in the estimations of some uncertain terms, a time-varying feedback controller is explicitly designed, which ensures that all the states of the resulting closed-loop system are bounded while system output asymptotically tracks the reference signal without any violation of the output constraint. Finally, simulation results for two practical examples are provided to validate the effectiveness of the proposed theoretical results, and moreover, a comparison with PID method is given to show the superiority of the proposed method on tracking accuracy and robustness.

Finite-time compensation control with dead-zone estimation for a rehabilitative walker considering internal disturbance forces

This study discusses a finite-time compensation tracking control method for a rehabilitative training walker. The dynamic model with input dead zone was constructed to describe the walker, and a finite-time disturbance forces observation method was proposed based on the impact mechanism on tracking performance. This approach is novel in that the disturbance forces were observed in reverse through their effects on tracking performance, thus successfully obtaining the disturbance forces of the walker. To ensure the practical finite-time stability of the system, the nonlinear finite-time compensation tracking controller with stochastic configuration networks (SCN) dead-zone estimation was built for the rehabilitative walker. Simulation results and comparative analyses confirmed that the proposed compensation control method effectively restrains dead zone and internal disturbance forces.

A model-free terminal sliding mode control for robots: Achieving fixed-time prescribed performance and convergence

This paper introduces a new control strategy for robot manipulators, specifically designed to tackle the challenges associated with traditional model-based sliding mode (SM) controller design. These challenges include the need for accurately computed system models, knowledge of disturbance upper bounds, fixed-time convergence, prescribed performance, and the generation of chattering. To overcome these obstacles, we propose the incorporation of a neural network (NN) that effectively addresses these issues by removing the constraint of a precise system model. Additionally, we introduce a novel fixed-time prescribed performance control (PPC) to enhance response performance and position-tracking accuracy, while effectively limiting overshoot and maintaining steady-state error within the predefined range. To expedite the convergence of the SM surface to its equilibrium point, we introduce a faster terminal sliding mode (TSM) surface and a novel fixed-time reaching control algorithm (RCA) with adaptable factors. By integrating these approaches, we develop a novel control strategy that successfully achieves the desired goals for robot manipulators. The effectiveness and stability of the proposed approach are validated through extensive simulations on a 3-DOF SAMSUNG FARA-AT2 robot manipulator, utilizing both Lyapunov criteria and performance evaluations. The results demonstrate improved convergence rate and tracking accuracy, reduced chattering, and enhanced controller robustness.

Robust Adaptive Fixed-Time Sliding-Mode Control for Uncertain Robotic Systems With Input Saturation

In this article, a robust adaptive fixed-time sliding-mode control method is proposed for robotic systems with parameter uncertainties and input saturation. First, a model-based fixed-time controller is designed under the premise that the system parameters are known. Moreover, the unknown dynamics of robotic systems and the boundary of compounded disturbance are synthesized into a compounded uncertainty. Then, the Gaussian radial basis function neural networks (NNs) are selected to approximate the compounded uncertainty. In addition, the nonsingular fast terminal sliding-mode (NFTSM) control is incorporated into the proposed fixed-time control framework to enhance the robustness and convergence speed of unknown robotic systems. Finally, a comparative simulation based on a rigid manipulator shows the superiority and efficacy of the designed methods.

UDE-based task space tracking control of uncertain robot manipulator with input saturation and output constraint

This paper investigates the trajectory tracking problem of uncertain robot manipulators with input saturation and output constraints. Uncertainty and disturbance estimator (UDE) is used to tackle the model uncertainties and external disturbances. Different from most existing methods, UDE only needs the bandwidth of the unknown plant model for design, which makes it easy to be implemented. Nonlinear state-dependent function is employed to cope with output constraints and a second order auxiliary system is constructed to solve the input saturation. Finally, an UDE-based tracking controller is proposed based on the backstepping method. With the proposed control scheme, the input saturation and the output constraints are not violated, and all signals in the closed-loop system are bounded. The comparative simulation results of a two-link robot manipulator are utilized to validate the effectiveness and superiority of the proposed control method.

Disturbance observer-based robust fixed-time integrated trajectory tracking control for space manipulator

This article investigates the fixed-time trajectory tracking control of a free-flying rigid space manipulator perturbed by model uncertainties and external disturbances. A novel robust fixed-time integrated controller is developed by integrating a nominal fixed-time proportional–differential-like controller with a fixed-time disturbance observer. It is strictly proved that the proposed controller can ensure the position and velocity tracking errors regulate to zero in fixed time even subject to lumped disturbance. Benefiting from the feedforward compensation, the proposed controller has the strong robustness and excellent disturbance attenuation capability. The effectiveness and advantages of the proposed control approach are validated through simulations and comparisons.

Adaptive sliding tracking control for nonlinear uncertain robotic systems with unknown actuator nonlinearities

This study is concerned with the tracking control problem for nonlinear uncertain robotic systems in the presence of unknown actuator nonlinearities. A novel adaptive sliding controller is designed based on a robust disturbance observer without any prior knowledge of actuator nonlinearities and system dynamics. The proposed control strategy can guarantee that the tracking error eventually converges to an arbitrarily small neighborhood of zero. Simulation results are included to demonstrate the effectiveness and superiority of the proposed strategy.