This work presents a rat optimization algorithm (ROA), which simulates the social behavior of rats and is a new nature-inspired optimization technique. The ROA consists of three operators that simulate rats searching for prey, chasing and fighting prey, and jumping and hunting prey to deal with optimization issues. The Levy flight strategy is introduced into the ROA to keep the algorithm from running into issues with slow convergence and local optimums. The ROA is tested with four real-world engineering optimization issues and twenty-two benchmark functions. Experiments show that the ROA is particularly effective at solving real-world optimization problems compared to other well-known optimization techniques.

In this work, an improved symmetric integral barrier function-based tracking control is addressed for a robotic manipulator in the presence of position error performance requirements and unknown uncertain terms. First of all, we construct an improved symmetric time-variant integral barrier function to solve the robotic system’s constrained requirements. The novel barrier function is constructed by developing an integral upper limit function that can be used in conjunction with existing performance envelope functions. Then, the equivalent conversion error is taken as the upper limit of the integral function to achieve the specified steady-state as well as transient behaviors of the system. Additionally, a disturbance observer based on the velocity tracking error is introduced to compensate for unknown uncertain terms. In the end, a numeral simulation study on a robot with two degrees is performed to indicate that the present scheme is feasible in handling the performance constraint and uncertain terms.

To address the challenges of lumped model uncertainties and tracking error constraints in biomimetic rehabilitation robot control, this paper proposes a novel high-order integral barrier function to construct an adaptive neural tracking control scheme. Radial basis function neural networks (NNs), inspired by the receptive field mechanism of motor neurons, feature local activation and can accurately approximate the nonlinear dynamics of such bionic rehabilitation devices. Distinct from traditional integral barrier Lyapunov functions, the presented high-order integral barrier function can accommodate both time-varying and time-invariant error constraints, while simplifying the controller derivation and ensuring full differentiability of virtual control laws throughout the backstepping framework. Supported by the derived barrier function theorems, the tracking error of the robot is theoretically proven to stay within predefined safe boundaries and converge exponentially to a compact neighborhood of the origin. Finally, comparative numerical simulations on a biomimetic rehabilitation robot validate the effectiveness of the proposed theorem and constrained adaptive neural control strategy.