This article investigates the problem of model-free tracking control for a class of continuum manipulators. A new type of robust control strategy is provided to address the problem, which primarily consists of two parts. First, to achieve output tracking with the desired accuracy, an error transformation scheme is constructed. Moreover, because of the robustness of the error transformation scheme against model uncertainties, no knowledge of the manipulator model is required, leading to a model-free control solution. Then, a tuning function is used to modify certain error variables to relax the requirements on the initial conditions of the error transformation scheme. In this way, the global stability of the closed-loop system can be guaranteed. Finally, both simulation and experimental results validate the expected performance of the presented control method.

The proportional-integral (PI) control method is long well-known because of its simplicity as well as effectiveness. Although the PI control method overwhelmingly dominates engineering applications, the issue of developing a general PI control for nonlinear systems remains open. This article considers the PI control problem for a family of unknown nonlinear systems. A structurally simple and computationally inexpensive PI-type control approach with constant gains is provided to solve the problem, which maintains preassigned transient as well as steady-state performance and guarantees the global stability of closed-loop systems. The key to possessing such properties comes down to the transformation and modification of tracking errors. The effectiveness of the presented control approach is assessed via the experiment on a pneumatic artificial muscle, and the results corroborate the theoretical findings.

Autonomous underwater vehicles (AUVs) face significant challenges in trajectory tracking due to nonlinear dynamics, actuator faults, and environmental disturbances. To address these issues, this article proposes a novel fault-tolerant control strategy that ensures fixed-time trajectory tracking with prescribed accuracy for underactuated AUVs. The proposed approach integrates boundary functions with a constraint-handling mechanism, enabling guaranteed tracking performance within a fixed time horizon while satisfying output constraints. Unlike existing approaches, the controller does not rely on accurate system models, parameter estimation, or external observers, and avoids the computation of virtual control derivatives, resulting in reduced computational complexity. Moreover, the control scheme maintains robustness against time-varying actuator faults and environmental disturbances without auxiliary adaptation or learning mechanisms. Simulation results demonstrate the effectiveness and superior performance of the proposed approach compared with existing methods, validating its capability to maintain tracking accuracy and closed-loop stability under adverse operating conditions.

This paper addresses the issue of safe trajectory tracking for robotic manipulators operating in confined spaces that often exhibit asymmetric geometric structures, such as narrow passages or cluttered obstacle fields. We introduce an innovative model-free control strategy aimed at guaranteeing safe trajectory tracking by constraining joint trajectory tracking errors within prescribed safe bounds. To achieve the desired tracking objective, an error transformation mechanism is developed. Notably, the robustness of this mechanism against model uncertainties eliminates the necessity for knowledge of the system model, enabling model-free control. Additionally, we introduce a Monte Carlo-based search strategy that integrates motion planning, which provides a collision-free feasible reference trajectory, with control, which ensures runtime safety. This framework operates autonomously and guarantees safety at both the planning and control layers. The controller’s robustness, along with its autonomous implementation from planning to control, facilitates practical deployment in complex real-world environments and is effectively validated on a Baxter robotic platform.