Purpose Capsule endoscopy offers a minimally invasive approach to gastrointestinal (GI) diagnostics but is limited by passive propulsion, which restricts controllability and risks suboptimal visualisation in complex anatomical regions. This study aims to develop and validate advanced control strategies for the CAPSUBOT, a vibro-impact robotic active capsule endoscope, enabling precise navigation and sustained motion within the GI tract. Methods A two-degree-of-freedom (2-DOF) navigation system is implemented to control the relative motion of the inner mass with respect to the outer mass of the capsule. The capsule dynamics are mathematically modelled and formulated in the state-space domain. Linear Quadratic Regulator (LQR) and various Sliding Mode Controllers (SMC), including first-order SMC, second-order SMC, Integral SMC (ISMC), and Terminal SMC (TSMC), are designed and simulated using MATLAB/Simulink. Frictional forces and interaction effects are incorporated to emulate realistic operational conditions. The controllers’ performance in tracking desired trajectories is systematically evaluated, and the potential for optimisation of LQR weighting matrices using computational techniques such as artificial bee colony, genetic algorithm, particle swarm optimisation, and reinforcement learning is discussed. Results Simulation results demonstrate that both LQR and SMC controllers can effectively guide the CAPSUBOT along commanded trajectories. First-order SMC, ISMC, and TSMC exhibit particularly strong performance in achieving steady-state tracking of the inner mass position and velocity, whereas LQR offers improved trajectory tracking under nominal conditions. The SMC controllers show robustness against disturbances and system uncertainties, supporting their use for sustained in vivo motion. Some time delays are observed in the SMC responses, highlighting areas for further improvement. Conclusion This work establishes a framework for actively controlling a vibro-impact robotic capsule endoscope using LQR and SMC strategies, providing unified principles for the interaction of inner and outer capsule masses under feedback-controlled actuation. While the controllers demonstrate feasibility and robustness, limitations remain: SMC time delays and computational complexity in real-time implementation warrant further investigation. Future work will focus on integrating artificial intelligence-based reinforcement learning, neural networks, and fuzzy logic to develop faster, adaptive controllers. Overall, the study contributes both practical guidance for CAPSUBOT navigation and generalized control principles that may inform the design of other active capsule endoscopy systems.
Abdollahi et al. (Fri,) studied this question.
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