This paper investigates the adaptive control problem for systems with time-varying parameters using the so-called congelation of variables method. First, two scalar examples to illustrate how to deal with time-varying parameters in the feedback path and in the input path, respectively, are discussed. The control problem for an n- dimensional lower triangular system via state feedback is then discussed to show how to combine the congelation of variables method with adaptive backstepping techniques. To achieve output regulation problem via output feedback, problem which cannot be solved directly due to the coupling between the input and the time-varying perturbation, the ISS of the inverse dynamics, referred to as strong minimum-phaseness, is exploited. This allows converting such coupling into the coupling between the output and the time-varying perturbation. A set of filters, resulting in ISS state estimation error dynamics, are designed to cope with the unmeasured state variables. Finally, a controller is designed based on a small-gain-like analysis that takes all subsystems into account. Simulation results show that the proposed controller achieves asymptotic output regulation and outperforms the classical adaptive controller, in the presence of time-varying parameters that are neither known nor asymptotically constant.

A novel adaptive method to achieve both path following and formation moving along desired orbits in the presence of a spatio-temporal flowfield is presented. The flowfield is a spatio-temporal general flow with unknown time-varying parameters. The so-called \emph{congelation of variables} method is used to estimate the time-varying flow parameters, which do not have any restrictions on the rate of their variation. The asymptotic properties of the resulting adaptive system are studied in detail. Simulation results demonstrate the effectiveness of the proposed method.

Colonoscopy is a medical procedure used to examine the inside of the colon for abnormalities, such as polyps or cancer. Traditionally, this is done by manually inserting a long, flexible tube called a colonoscope into the colon. However, this method can cause pain, discomfort, and even the risk of perforation. To address these shortcomings, advancements in technology are needed to develop safer, more intelligent colonoscopes. This article presents the design, control, and evaluation of a self-growing soft robotic colonoscope, leveraging the evertion principle. The device features a tube with an 18 mm diameter, constructed from stretchable fabric, which grows 1.6 m at the tip under pressurization. A pneumatically driven, elastomer-based manipulator enables omni-directional steering over 180° at the tip. An airtight base houses motors and spools that control the material and regulate growth speed. The robot operates in two modes: teleoperation via joysticks and autonomous navigation using sensor inputs, such as a tip-mounted camera. Thorough in-vitro experiments are conducted to assess the system’s functionality and performance. Results illustrate that the robot can achieve locomotion in confined spaces such as a colon phantom, while exerting contact forces averaging less than 0.3 N. Our soft robot shows potential for improving the safety and autonomy of colonoscopies, while reducing discomfort to patients.

This article investigates the model-based control in configurations space of a soft continuum manipulator for minimally invasive surgery. The main control challenges for these types of systems are the presence of model uncertainties and nonlinearities. To this end, a sliding-mode controller, a Lyapunov redesign controller, and an adaptive controller have been designed and compared by means of simulations and experiments on a prototype. The results indicate that the adaptive controller yields better accuracy but a slower transient. Conversely, the sliding-mode controller and Lyapunov redesign yield a faster response but can result in chattering or steady-state errors.

This letter introduces a novel hybrid manipulator composed of two pneumatically-actuated soft segments and a tendon-driven continuum segment, connected in series. The soft segments enhance the mobility of the tendon-driven manipulator, enabling functionalities such as triangulation while maintaining low tool stiffness, making it suitable for endoluminal surgery in the lower gastrointestinal tract. A key advantage of this design is the implementation of a model-based controller and an observer-based contact force estimation method, which estimates external forces based on the configuration and actuation of the soft segments. This approach is experimentally compared to a tendon tension sensing methodology, known as intrinsic force sensing. The experiments include typical lifting and pushing tasks, along with realistic operations on ex-vivo porcine intestinal tissue. The results demonstrate the feasibility of the proposed approach in surgical applications and its advantages over intrinsic force sensing, achieving an average 15.8% reduction in normalized RMSE among all tasks.