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The paper is devoted to the theoretical problem of designing a robust asymptotic tracking control system for a rotational motion of a 2DOF underactuated linear mechanical system with parametric uncertainties. The mathematical formulation of the problem is motivated by the attitude control problem of an earth observation satellite with a solar panel. It is assumed that all the parameters of the plant model are uncertain and the plant single input is additively disturbed by an unknown constant torque. By employing the general regulator theory in the state space setup combined with the concept of the structured singular value, we develop a robustly stabilizing and robustly asymptotically tracking error feedback controller. The rotation of the main rigid body of the mechanical system is to asymptotically track a harmonically changing reference signal. The obtained theoretical results are successfully tested on two numerical examples and computations are performed in Matlab.

In this paper, a robust adaptive control scheme is proposed for the trajectory tracking control of underactuated surface vessels (USVs) subject to unknown dynamics, external disturbances and input saturation. First, a coordinate transformation is introduced to deal with the underactuation problem of the USV. A Gaussian error function and an adaptive neural network (NN) are adopted to approximate the saturation function and the unknown dynamics, respectively. Then, an adaptive robust integral of the sign of the error (RISE) feedback term is introduced in feedback control design to compensate the NN and saturation approximation residual errors and unknown external disturbances. On the basis of the above, a robust adaptive trajectory tracking control law is proposed incorporating a coordinate transformation, Gaussian error function and NN into RISE method. In addition, the adjustable-online adaptive feedback gain reduces the conservativeness of the control design. The theoretical analysis indicates that the designed robust adaptive control law can force USVs to track the desired trajectory while guaranteeing the asymptotic tracking performance. Simulation results verify the effectiveness of the novel robust adaptive trajectory tracking control scheme.

This paper presents a model-free robust nonlinear PD (R-NPD) controller for cable-driven parallel manipulators (CDPMs) in joint space. Generally, in various mechanical manipulators and in particular CDPMs for fast and high-precision tracking, a precise dynamic model is required. However, the dynamic model of the robot is always contaminated with uncertainties such as nonlinear and time-varying parameters as well as external disturbances. For this purpose, in the proposed controller structure, the time-delay estimation (TDE) technique is used to indirectly use the robot dynamics into the control structure without need of its prior knowledge. Furthermore, a nonlinear PD controller is designed in joint space in such a way that the robot can track the reference trajectory quite fast and accurate, without the need for any auxiliary sensors. The stability of the closed-loop system has been examined through Lyapunov direct method, and it has been shown that tracking error remains uniformly ultimately bounded. Finally, to demonstrate the effectiveness of the proposed controller, simulations and experiments have been performed on two different categories of CDPMs, whose results show that the proposed control scheme outperforms modified TDE control method in practice.

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

This paper investigates the L1 gain stability problem of reliable control for positive systems with input saturation under multi‐asynchronous switching. Firstly, by constructing a system state observer and integrating it with an output feedback control strategy, the input variables for the system controller were obtained, and a reliable controller with input saturation was designed. Secondly, to prevent data accumulation, an adaptive event‐triggered control strategy that ensures the non‐negativity requirements of positive systems is introduced between the observer and the system state. This strategy can adjust the tightness of the event‐triggering process, which not only improves control efficiency but also reduces the risk of the Zeno effect. The following describes a switching strategy based on event‐triggered control. Under the guidance of a time‐varying mode‐dependent average dwell‐time switching strategy, the multi‐asynchronous delay problem of sub‐observers and sub‐controllers with respect to subsystems is addressed, leading to a closed‐loop control system based on error feedback. By constructing co‐positive Lyapunov function, sufficient conditions for the positivity of the system under both synchronous‐ and asynchronous‐switching are provided, and the L1 gain stability of the system in both synchronous and asynchronous intervals is verified. Finally, the significance of the proposed method is validated through an example. We investigate the L1‐gain stability problem of reliable saturation control for positive system under multi‐asynchronous switching. Then, we designed an adaptive event‐triggering control strategy and an event‐triggered switching strategy.

This paper discusses the robust trajectory tracking control of an autonomous tractor-trailer in agricultural applications. Firstly, considering the model parameter uncertainties and various disturbances, the kinematic and dynamic models of the autonomous tractor-trailer system are established. Moreover, the coordinate transformation is adopted to convert the trajectory tracking error into a new unconstrained error state space model. On this basis, the prescribed performance control (PPC) technique is designed to ensure the convergence speed and final tracking control accuracy of the tractor-trailer control system. Then, this paper designs a double closed-loop control structure. The posture control level adopts the model predictive control (MPC) method, and the dynamic level adopts the sliding mode control (SMC) method. At the same time, it is worth mentioning that the nonlinear disturbance observer (NDO) is designed to estimate all kinds of system disturbances and compensate for the tracking control system to improve the system’s robustness. Finally, the proposed control strategy is validated through comparative simulations, demonstrating its effectiveness in achieving robust trajectory tracking of the autonomous tractor-trailer system.

Electric Power Steering (EPS) systems enhance driving comfort and safety. However, their performance often degrades under varying operating conditions due to external disturbances and modeling uncertainties. Traditional control methods, which typically rely on fixed parameters or neglect disturbance dynamics, struggle to maintain robustness and adaptability across diverse scenarios. This article presents an improved control strategy integrating Active Disturbance Rejection Control (ADRC) with advanced soft computing techniques to address these challenges. The proposed method introduces two key innovations: optimizing the tracking differentiator’s speed factor using a genetic algorithm and dynamically tuning state feedback control parameters through a fuzzy inference system. This hybrid approach enhances the disturbance rejection capability of ADRC and significantly improves system adaptability and tracking accuracy. Simulation results validate the effectiveness of the proposed controller, demonstrating low tracking errors (1.875% at low speed and 1.373% at high speed) and disturbance estimation accuracy exceeding 90%. Compared to conventional controllers, the proposed method exhibits superior robustness, reduced steady-state error, and improved performance across a wide range of operating conditions. These results confirm the potential of integrating ADRC with intelligent optimization techniques for advanced control in automotive mechatronic systems.

In this work, a robust finite-time anti-swing controller is proposed for the quadrotor slung-load system subject to external disturbances. To stabilize the swing angles of the payload, an energy function incorporating both kinetic energy and potential energy is constructed such that the swing angles gradually converge to zero. Since the swing angle information is integrated into the energy function, the global convergence of the quadrotor slung-load system is improved. Meanwhile, a novel compensation function observer is designed to suppress unknown disturbances, thereby enhancing the robustness of the closed-loop system. Compared with the traditional extended state observer method, the developed compensation function observer achieves a faster convergence rate and higher observation accuracy with a smaller observer gain. To further improve the fast convergence performance of the entire closed-loop system, the finite-time control technique is adopted to design the robust anti-swing flight control scheme based on the Lyapunov stability theory and backstepping approach. This control scheme guarantees that all error signals of the quadrotor slung-load system are uniformly ultimately bounded. Finally, simulation verification is performed and comparison results are provided to illustrate the superior effectiveness of the presented control algorithm.

This article proposes a robust optimal output-feedback control scheme for solving the optimal tracking problem of a piezoelectric motion system, where the model of the piezoelectric motion system is represented by the linear dynamics with both asymmetric input hysteresis nonlinearities and various bounded disturbances. Different from the existing hysteresis model, the neural network is incorporated in the Prandtl-Ishlinskii model to depict the asymmetric feature of the input hysteresis nonlinearity, a novel fast composite adaptive identification method is proposed to obtain the parameters of the asymmetric input hysteresis model. Based on the identified asymmetric hysteresis model, its inverse is constructed to compensate the asymmetric hysteresis nonlinearities of the piezoelectric motion system, and the boundedness of the inverse compensation error is firstly analyzed. In particular, a robust optimal output-feedback controller with a finite-time extend state observer is designed to achieve the optimal tracking of the piezoelectric motion system in the presence of the unknown states and disturbances. Both the convergence of the hysteresis model parameters and the stability of the closed-loop system are analyzed. Finally, the excellent modeling and identification accuracy of the asymmetric input hysteresis nonlinearities and the satisfactory tracking performance of the proposed control scheme are demonstrated by real-time experiments on a piezoelectric motion stage.

In this paper, an interacting multiple model (IMM) Kalman filter is used to estimate non-measurable states in a linear switched positive discrete-time system (SPDS). The presence of process noise or measurement noise in a system is a common phenomenon. In positive systems, the problem of noise generally is ignored due to the possibility of eliminating the positive nature of the system. To solve the above problem, the powerful IMM-Kalman filter tool can be used along with the equilibrium point transfer in a targeted manner. Also, the effect of finite bounded disturbance is attenuated by the H∞ method. To guarantee the stability and positivity of the finite-time system with the asynchronous switching regime, a robust output feedback controller is designed. The original novelty of the method is designing a switched positive system in which the effect of disturbance and noise simultaneously have been reduced on it, and moving the equilibrium point of the system helps to improve efficiency and maintains its positivity. Then, the proposed method is implemented on the pest population dynamic as a stochastic model and compared by the root mean square error (RMSE) criterion with an existing output feedback method. The smallness of the RMSE criterion values for the three levels of disturbance in the presence of noise confirms the robustness and stability of the presented method.