This article addresses the consensus tracking control problem for a class of unparametrizable noncanonical nonlinear multi-agent systems. Most existing consensus tracking control schemes for nonlinear multi-agent systems (MASs) are restricted to canonical-form system models. For noncanonical-form nonlinear MASs, to our best knowledge, there is still no result available so far due to some challenging technical issues encountered in design and analysis. Firstly, existing distributed observers are limited to canonical systems by the assumption that the output matrix of leader is known, rendering them inapplicable to noncanonical nonlinear systems. Moreover, current adaptive distributed observer frameworks only consider first-order differentiability of the system matrix, missing the information of higher-order derivative. Secondly, unlike the traditional Lyapunov method for canonical systems, noncanonical systems require augmented error analysis for stability, and extending this to noncanonical nonlinear MASs is highly challenging. Thirdly, unparametrizable nonlinear functions in the original system complicate control design. To tackle these issues, an approximate Takagi-Sugeno (T-S) fuzzy models are constructed to eliminate non-parametric functions, alongside a novel adaptive distributed observer for estimating leader information. Simulations verify the effectiveness of our control scheme in achieving consensus control tracking.

The piezoelectric actuator will exhibit the problem of unknown control direction under high-speed operating conditions. Hence, the realization of high-speed control for nonlinear systems driven by piezoelectric actuators requires the fixed-time stability to enhance the convergence rate and the Nussbaum technique to handle the problem of unknown control directions. However, these two techniques are incompatible because the required conditional inequalities for each are different. To resolve this incompatibility, we have incorporated the projection operator into the design of the adaptive updating law. On the basis of ensuring that the adaptive estimation parameter is bounded, we have established the conditional inequality for Nussbaum technique. Then, some nonlinear damping terms are incorporated into the stability analysis to counteract a term containing Nussbaum function, thereby reducing the difficulty of the fixed-time stability analysis. Furthermore, the predefined tracking accuracy and fixed-time stability are theoretically proven through the stability analysis. Finally, the efficiency of the proposed control method is validated through semi-physical experiments on a cylindrical piezoelectric actuator.

In this paper, a Zeroing Neural Network (ZNN)-based control framework is proposed for finite-time visual servoing of robotic manipulators, without requiring camera calibration or kinematic modeling. To address the challenge of the unknown robot-camera interaction, a data-driven Jacobian estimator is introduced, enabling real-time mapping without offline training or analytical derivation. A finite-time noise-rejection ZNN (FTNRZNN) controller is developed to ensure robust and fast joint-level control under measurement noise. The continuous-time scheme is further discretized for digital implementation. Rigorous Lyapunov analysis guarantees finite-time convergence. Simulations and real-world experiments validate the effectiveness of the method in both regulation and trajectory tracking, demonstrating strong adaptability to unstructured environments.

The work aims to solve the high-speed high-precision tracking control problem of piezoactuators in the presence of stochastic disturbances. First, a cascade model composed of the Preisach operator and a class of stochastic nonlinear systems is proposed to describe the sophisticated actuator dynamics during high-speed operation, and then a hysteresis decomposition strategy is developed to transform the Preisach model into an appropriate form tractable to control design so that a robust adaptive fuzzy control framework can be constructed successfully to suppress the hysteresis nonlinearities, and to robustify bounded stochastic disturbances. More importantly, based on such a framework, the fixed-time stability (instead of practical fixed-time stability), and the prescribed steady-state tracking performance can be established simultaneously. Besides theoretical analysis, some experimental tests are also conducted to illustrate the effectiveness of the proposed scheme.

An inversion-based control strategy has been shown to be effective in compensating the hysteresis nonlinearities modeled by the Preisach operator. However, when the operator is coupled with the dynamics of uncertain nonlinear systems, there is still no result available for constructing the hysteresis inverse controller. To fill in the gap, in this study, we propose an iterative inverse-based adaptive fuzzy control scheme. Technically, an adaptive hysteresis inverse constructed through an iteration algorithm and updated by a projection-based adaptive law is developed as a feedforward hysteresis compensator, and then, the hysteresis inverse compensation error and plant nonlinearities and uncertainties are handled by a newly designed adaptive fuzzy controller. With our scheme, the closed-loop stability in the sense of signal boundedness, the prescribed steady-state tracking performance, and the convergence of the iteration algorithm can be established. Besides theoretical analysis, the effectiveness of our scheme is also validated by simulation and experimental results.

This paper develops an image-based visual servoing (IBVS) control strategy using model predictive control (MPC) to stabilize a physically constrained mobile robot. In IBVS strategy, ambiguity, and degeneracy problems of the homography and fundamental matrix-based algorithms can be avoided. Moreover, a synthetic error vector incorporating the advantages of IBVS and position-based visual servoing is defined that includes both the robot angle and image coordinates. By using linear system control theory, the kinematics of nonholonomic chained robotic systems can be transformed into a skew-symmetric form, and through introducing an exponential decay phase, the uncontrollable problem can be solved. Then, an MPC strategy is developed and, thereafter, iteratively transformed into a constrained quadratic programming (QP) problem. Subsequently, we utilize a primal-dual neural network (PDNN) to solve this QP problem. By using PDNN optimization, the cost function of MPC effectively converges to the exact optimal values. Finally, experimental studies on the actual robotic systems have been conducted to demonstrate the performance of the proposed approach.

In this paper, a class of nonholonomic chained systems is first converted into two subsystems, and then an explicit exponential decaying term is introduced into the input of the first subsystem to guarantee its controllability. After a state-scaling transformation, a model predictive control (MPC) scheme is proposed for the nonholonomic chained systems. The proposed MPC scheme employs a general projection neural network (GPN) to iteratively solve a quadratic programming (QP) problem over a finite receding horizon. The GPN employed in this paper is proved to be stable in the sense of Lyapunov, and its global convergence to the optimal solution is guaranteed for the reformulated QP. A simulation study is performed to show stable and convergent control performance under the proposed method, irrespective of whether the control input $\boldsymbol {u_{1}}$ vanishes or not.