Lipeng Yuan

For a class of high-order uncertain systems with actuator faults and constrained performance requirements, this paper proposes a low-complexity preset-time fault-tolerant control method with flexible self-adjusting prescribed performance. Unlike most existing fault-tolerant control methods, this study introduces a new coordinate transformation by combining the arctangent function and a preset-time performance function, and, together with the tangent function, eliminates the controller design’s dependence on information such as system nonlinearities and derivatives of virtual control laws. The proposed method not only replaces conventional techniques such as function approximation, command filtering, and data-driven approaches but also ensures that the system output tracks the reference signal with preset time and accuracy. Furthermore, by organically combining multiple functions, this study constructs a unified framework. Within this framework, users can simultaneously achieve various prescribed performance behaviors and self-adjusting performance for constraint boundaries through simple parameter selection, without altering the controller structure. Simulation results verify the feasibility of the proposed method.

To address the issues of insertion deflection caused by the inherent properties of standard puncture needles in clinical prostate particle implantation, the overemphasis of existing control algorithms on single puncture positioning performance at the expense of continuous puncture reliability, and the lack of precise support for soft tissue environmental stiffness, this study proposes a reliability control algorithm for puncture deflection and continuous positioning for prostate particle implantation. First, an interaction model between the puncture needle and glandular soft tissue is constructed, the deflection mechanism is analyzed, and the core role of soft tissue stiffness parameters in deflection regulation is clarified. Second, by integrating Hooke’s law with measurements from force and displacement sensors, the least squares method is adopted to achieve online estimation of human prostate soft tissue environmental stiffness, providing real‐time stiffness parameters for deflection control. Finally, a closed‐loop control model incorporating data acquisition, deviation identification and instruction feedback is constructed, the needle posture is adjusted via spatial transformation of the puncture robotic arm, a particle filter algorithm is adopted to evaluate positioning reliability in real time, and the spatial adjustment of the needle tip is derived to ensure continuous puncture accuracy. Comparative experiments on prostate bionic soft tissues demonstrate that the proposed online stiffness estimation method takes approximately 3 s and can rapidly and synchronously track dynamic stiffness variations during puncture, and that the deflection and continuous puncture control method is effective and reliable, achieving a target positioning overlap rate of 95.6%. This algorithm ensures the therapeutic efficacy of prostate particle implantation and provides a phased theoretical reference for research on precision control systems for puncture robots.

Addressing the dynamic stability issue of quadruped robots with a relatively high leg-to-body mass ratio on various structured terrains, this paper proposes a hybrid control strategy. Initially, the gait scheduling framework is constructed using Hopf oscillators within the Central Pattern Generator (CPG) to emulate the bio-inspired characteristics of quadruped robot locomotion. Subsequently, terrain estimation and gait planning are employed to enable the robot to traverse diverse structured terrains. During the stance phase of quadruped robot locomotion, a hierarchical Quadratic Programming (QP) optimization control is implemented. In the swing phase, a force-based impedance control is adopted to enhance the compliance and smoothness of the robot’s leg-lifting motion. Furthermore, by leveraging Lyapunov stability theory, the stability of the control system in the swing phase is rigorously analysed and proven, thereby ensuring the effectiveness and reliability of the control strategy from a theoretical standpoint. For posture control, dynamic stability is achieved by adjusting the linear and angular accelerations of the robot’s centre of mass. The proposed control strategy was validated through the MATLAB/Simulink simulation platform. The results demonstrate that, compared with the standard QP optimization control, the proposed strategy can more accurately and stably control the quadruped robot’s locomotion on flat ground. Moreover, the strategy enables the robot to achieve stable walking on various structured terrains.

To address the challenges of dynamic stability and robust control for quadruped robots on sloping terrain, this paper proposes a bionic dual-modal robust control (DMRC) strategy. This approach synergistically allocates robust non-singular fast terminal sliding mode control to the swing phase and decoupled virtual model control (VMC) to the support phase, thereby eliminating the need for global Jacobian recalculation. The framework incorporates four novel bio-inspired terrain adaptation modules: (i) a pitch-adaptive stride adjustment strategy, (ii) an active ground-contact and downward-exploration strategy, (iii) a slope-adaptive leg posture adjustment strategy, and (iv) a gravity feedforward anti-slip virtual force strategy. A Lyapunov-based finite-time convergence proof is provided for the swing phase under parametric uncertainties and high-frequency disturbances. MATLAB/Simulink simulations demonstrate that on undisturbed flat terrain, the proposed DMRC strategy achieves attitude accuracy within ±1° with a significantly reduced displacement-tracking error. Under compound disturbances comprising 20% parametric uncertainties, stochastic torque disturbances, and multi-source sensor noise, the robot re-establishes equilibrium within one to two gait cycles. When the same compound disturbances are applied on a 0.3158 rad slope, the DMRC framework still enables successful terrain negotiation, with the system states remaining bounded and stable. Steady-state motion is recovered within one to two gait cycles after the disturbances cease. The proposed approach substantially outperforms both conventional VMC and hybrid force/position control schemes, confirming its superiority and robustness in unstructured environments.

Traditional control methods for high‐order nonlinear systems often exhibit slow convergence and poor robustness. To address these challenges, this paper proposes a fixed‐time convergence non‐singular disturbance rejection control strategy tailored for high‐order nonlinear systems. First, by integrating power functions and exponential functions, a novel fixed‐time fast‐convergence Lyapunov function is constructed based on finite‐time control theory. This function guarantees a fixed upper bound for the system's convergence time, independent of the system's initial states. Compared to conventional finite‐time Lyapunov functions, the proposed approach achieves significantly faster convergence rates. Subsequently, this function is combined with a backstepping control strategy, and a nonlinear disturbance observer is employed to accurately estimate the combined effects of unknown disturbances and modeling errors. This method enhances the controller's response speed, improves disturbance rejection capabilities, and reduces steady‐state errors. Finally, the proposed control strategy is applied to an inverted pendulum system and a hydraulic servo system. Experimental results demonstrate that the proposed strategy offers significant performance advantages.

Aiming at the problems of low accuracy and poor robustness in gait recognition of lower extremity exoskeleton robots in human-computer interaction, a depth residual contraction network recognition method based on the fusion of surface electrosemg (sEMG) and inertial measurement unit (IMU) signals was proposed. Firstly, a new energy kernel feature extraction method was used to extract sEMG signals. Based on the sEMG oscillator model, the sEMG energy kernel phase diagram was converted to gray level map by matrix counting method. Secondly, the IMU signal is denoised and processed graphically. Then, deep residual contraction network (DRSN) was used to recognize sEMG and IMU signals in lower limbs. Finally, experimental hardware was deployed in the wearer's lower limbs, and the algorithm was used to conduct offline and online recognition experiments of three common gaits. Different comparative experiments show that the attention mechanism of DRSN network can significantly improve the classification effect, and the recognition accuracy is improved by 10%-20% compared with single source signal and other feature extraction methods, and finally the recognition accuracy reaches more than 90% through online experiments. The multi-feature fusion network based on energy kernel feature extraction is time-efficient, high-accuracy and robust, and has real-world application value in the field of exoskeleton robotics.

To achieve obstacle-avoiding puncture in breast interventional surgery, a robotics system based on three-fingered breast target-point manipulation is proposed and designed. Firstly, based on the minimum number of control points required for three-dimensional breast deformation control and the bionic structure of the human hand, the structure and control scheme of the robotics system based on breast target-point manipulation are proposed. Additionally, the workspace of the robotics system is analyzed. Then, an optimal control point selection method based on the minimum resultant force principle is proposed to achieve precise manipulation of the breast target point. Concurrently, a breast soft tissue manipulation framework incorporating a Model Reference Adaptive Control (MRAC) system is developed to enhance operational accuracy. A dynamic model of breast soft tissue is developed by using the manipulative force–displacement data obtained during the process of manipulating breast soft tissue with mechanical fingers to realize the manipulative force control of breast tissue. Finally, through simulation and experiments on breast target-point manipulation tasks, the results show that this robotic system can achieve spatial control of breast positioning at arbitrary points. Meanwhile, the robotic system proposed in this study demonstrates high-precision control with an accuracy of approximately 1.158 mm (standard deviation: 0.119 mm), fulfilling the requirements for clinical interventional surgery in target point manipulation.

Addressing the dynamic stability issue of quadruped robots with a relatively high proportion of leg mass in various structured terrains, this paper proposes a hybrid control strategy. Initially, the gait scheduling framework is constructed using Hopf oscillators within the Central Pattern Generator (CPG) to emulate the bio-inspired characteristics of quadruped robot locomotion. Subsequently, terrain estimation and gait planning are employed to enable the robot to traverse diverse structured terrains. During the stance phase of quadruped robot locomotion, a hierarchical quadratic programming (QP) optimization control is implemented. In the swing phase, a force-based impedance control is adopted to enhance the compliance and smoothness of the robot's leg-lifting motion. Furthermore, by leveraging Lyapunov stability theory, the stability of the control system in the swing phase is rigorously analyzed and proven, thereby ensuring the effectiveness and reliability of the control strategy from a theoretical standpoint. For posture control, dynamic stability is achieved by adjusting the linear and angular accelerations of the robot's center of mass. The proposed control strategy was validated through the MATLAB/Simulink simulation platform. The results demonstrate that, compared with the standard QP optimization control, the proposed strategy can more accurately and stably control the quadruped robot's locomotion on flat ground. Moreover, the strategy enables the robot to achieve stable walking on various structured terrains.