Soft robots have the characteristics of good adaptability to an environment. But at present, most soft robots only complete a single specific task, and as a result, their ability to adapt to complex environments is limited. In order to solve the weak ability of soft robots to adapt to complex and diverse environments, this paper presents a new bionic omnidirectional bending actuator (BOBA) inspired by leeches, caterpillars, and other mollusks. BOBA has less radial expansion, fast response, good flexibility, and omnidirectional bending. Using modular methods, it can be quickly assembled into a soft robot with a variety of motion modes. Experiments show the multimodal soft robot can climb vertical walls, transition from one surface to another, climb stairs, leap obstacles, cross narrow pipes, and move with heavy objects. This greatly improves the ability of soft robots to adapt to complex and diverse environments.

Aiming to address the lack of effective parameter-tuning strategies for model-free predictive controllers, this letter proposes an online self-learning approach using a tiny neural network. First, an incremental model-free predictive control scheme is introduced that incorporates a tiny neural network module. By analyzing the physical relationship between motor voltage and current, the input and output values for neural network learning are determined. Subsequently, an online learning strategy is proposed, leveraging the ultra-local model principle from physics, model-free control theory principles, and the neural network structure itself. Experimental results demonstrate that the proposed method has a short computation time, fully supports real-time operation in the motor current loop, and can autonomously optimize controller parameters when the initial settings of the ultra-local model-free predictive control are not ideal.

Gradient-based flux observers are promising for encoderless control of surface-permanent magnet synchronous motors (SPMSMs) for their computational efficiency and adaptability, yet suffer from noise sensitivity and gain tuning. To this end, this article proposes a robust optimized flux observer combining the huber loss function (HLF) and root mean square propagation (RMSProp) algorithm to overcome these limitations. First, a robust nonlinear flux observer is developed with the HLF to enhance the steady state performance of the encoderless control by leveraging its smoothness and noise suppression capabilities. Second, the RMSProp algorithm is employed to optimize the gradient search process, dynamically adjusting the step size in real-time based on historical gradient information to ensure improved dynamic response. Additionally, the Lyapunov stability criterion is utilized to analyze the stability of the proposed controller and prove the bounds of its observation error. Finally, experimental results confirm that the proposed control strategy achieves high-precision position estimation. Angular estimation errors under no-load and load conditions are reduced to below −0.09 rad and −0.08 rad, respectively, compared to −0.22 rad and −0.15 rad with conventional controller, due to flexible gain tuning and strong noise robustness.

Smooth and robust speed regulation is important for permanent magnet synchronous motor (PMSM) drives. This article proposes a novel PMSM speed control design methodology that explicitly incorporates target performance specifications into the controller synthesis, achieving nonovershoot and oscillation-free speed response. Particularly, compared with the traditional prescribed performance control (PPC), the proposed method is based on a refined tube-type performance function, which achieves unilateral convergence and has a simple structure. It has faster convergence speed and nonovershoot characteristics. Furthermore, the idea of the monotonous tube performance function is extended to the case of abrupt reference variation, in which the original PPC may lead to singularity problems and controller crashes. Besides, the inherent static error and oscillation problem of the PPC method is alleviated with an integral term combined with an antiwindup scheme. The controller effectively improves the system’s robustness and ensures the same predefined nonovershoot and oscillation-free performance under different load and speed variations. Additionally, rigorous theoretical analysis is studied via the Lyapunov stability theory. Comparative experiments are conducted on a PMSM platform. Despite diverse load conditions and references, the proposed controller delivers a smooth, overshoot-free, and robust speed response.

Encoderless algorithms for surface-mounted permanent magnet synchronous motors addresses the installation limitations of encoders in harsh or confined environments. However, existing encoderless control methods suffer from challenges in dynamic response speed and parameter sensitivity issues. To solve these issues, a modified reaching law for nonlinear flux observer with improved response speed is proposed in this study. First, a logarithmic function-based reaching law is designed for the nonlinear angle observer to achieve faster response speed. The Lyapunov function is developed to prove the stability of this reaching law. Second, an accurate mathematical model of the motor is designed and a dataset is constructed based on the deviation between actual values and reference values. Adjustable weight factors are used in this model to fit factors, such as inductance saturation and unmodeled disturbances. In addition, the sparse Bayesian learning algorithm is employed to analyze the features of historical data within the dataset, which helps reduce the impact of parameter variations on angle observation accuracy. The experimental results indicate that the proposed algorithm recovers from speed reversal in only 53.2 ms, significantly faster compared to 177.2 ms in conventional methods.

Model predictive control (MPC) has become an attractive solution for doubly fed induction motors (DFIMs) due to its fast dynamic response and multi-variable constraint handling capability. However, the performance of conventional MPC relies on the accuracy of the system model. To further enhance the control performance and adaptability, this paper proposes a fuzzy logic-based model predictive control (FL-MPC) strategy. The proposed method continuously monitors the current tracking errors and their rates of change, utilizing a fuzzy inference system to dynamically optimize the weight distribution within the predictive model. This enables the controller to autonomously adjust its behavior for optimal performance across a wide range of operating conditions. Both simulation and experimental results demonstrate that, compared to the conventional MPC, the proposed FL-MPC strategy achieves superior dynamic response.