Adaptive fuzzy fault-tolerant control using Nussbaum-type function with state-dependent actuator failures

Robust control of a compliant manipulator with reduced dynamics and sliding perturbation observer

Compliant manipulators equipped with Variable Stiffness Actuation (VSA) possess the ability to safely interact with their environment and adapt to complex tasks. However, the tracking performance of such manipulators is significantly influenced by their intrinsic compliance and varying stiffness. This paper presents a robust control strategy for compliant manipulators equipped with Discrete Variable Stiffness Actuation (DVSA), aimed at enhancing tracking performance under both fixed and varying stiffness conditions. By compensating for nonlinear dynamics, joint coupling, frictions, stiffness variability and uncertainties through a Sliding Perturbation Observer (SPO), the proposed approach ensures robust and consistent performance, significantly outperforming traditional controllers. In fixed stiffness scenarios, the proposed controller achieves a reduction in energy consumption of up to 68.1% at the lowest stiffness and 75.5% at the highest stiffness, demonstrating superior efficiency. For abrupt stiffness transitions (lowest-highest-lowest), the proposed controller reduces energy consumption by up to 70.9% compared to traditional controllers. Additionally, it maintains smooth control input and precise tracking, avoiding the chattering and inefficiencies that traditional controllers typically exhibit. The SPO effectively estimates all system states and perturbation, compensating for nonlinearities, disturbances due to variable stiffness, and friction, enabling consistent trajectory tracking with minimal control effort. The proposed controller's ability to adapt to varying stiffness levels without explicit parameter tuning highlights its robustness and practical viability. Simulation and experimental results validate its superior performance in reducing tracking error and energy consumption, particularly in scenarios where traditional controllers struggle to manage varying stiffness effectively.

Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method

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.

Command Filtered Neuroadaptive Fault-Tolerant Control for Nonlinear Systems With Input Saturation and Unknown Control Direction

This article studies the tracking control of a class of nonlinear systems with input saturation, subject to nonaffine faults and unknown control direction. A fault-tolerant command filtered control (CFC) method based on adaptive neural networks (NNs) is proposed for this kind of nonlinear system. First, the combination of CFC and error compensation overcomes the "explosion of complexity" issue and alleviates the impact of filter errors. Then, a set of radial basis function NNs is constructed to approximate the unknown nonlinear items containing the nonaffine fault function. Additionally, the issue of unknown control direction in the system is effectively resolved by using Nussbaum gain technology. It is proven that the designed controller can ensure that all signals in the closed-loop system are bounded and convergent, and the upper bound of the absolute value of system tracking error is given. Finally, three comparative simulation results are illustrated to show the effectiveness of the proposed method.