This paper proposes a new adaptive time- delay control (ATDC) scheme for cable-driven manipulat- ors. The proposed ATDC scheme contains three elements, i.e., time-delay estimation (TDE), injected dynamics and adaptive laws. The TDE utilizes intentionally time-delayed signals to estimate the unknown system dynamics and brings a fascinating model-free nature. The injected dyna- mics employ a fractional-order nonsingular terminal sliding mode (FONTSM) manifold and a fast-TSM-type reaching law, thus it can ensure fast dynamical response and high tracking accuracy in both sliding mode and reaching phases. The adaptive laws are utilized to obtain good robustness and effective suppression of the chatters simultaneously. Moreover, the continuous and chatter-free adaptive gains are used to enhance the control performan- ce under time-varying disturbances. The tracking error is proved to be uniformly ultimately bounded (UUB) using Lyapunov approach. Finally, the superiorities of the propo- sed ATDC scheme are demonstrated by three experiments using a cable-driven manipulator.

For high control performance of cable-driven manipulators, we design a new adaptive time-delay control (ATDC) using enhanced nonsingular fast terminal sliding mode (NFTSM). The proposed ATDC uses time-delay estimation (TDE) to acquire the lumped dynamics in a simple way and founds a practical model-free structure. Then, a new enhanced NFTSM surface is developed to ensure fast convergence and high control accuracy. To acquire good comprehensive performance under lumped uncertainties, in this article we propose a novel adaptive algorithm for the control gain, which can regulate itself based on the control errors timely and accurately. Benefitting from the TDE and the proposed enhanced NFTSM surface and adaptive control gain, our proposed ATDC is model-free, fast response, and accurate. Theoretical analysis concerning system stability, and control precision and convergence speed are given based on Lyapunov theory. Finally, the advantages of our ATDC over existing methods are verified with comparative experiments.

The sense of touch plays a critical role in traditional open surgeries since it could provide tactile feedback to surgeons and is also used to acquire intrinsic properties of tissues through palpation. However, it is partially or completely lost in most existing robot-assisted minimally invasive surgeries. To solve this problem, a miniature tactile sensor with diameter less than 8 mm suitable for catheter robot-based tissue hardness palpation is presented in this paper. The stringent size constraint of minimally invasive surgery (MIS) is met by a unique spiral shape as well as a vertically configured piezoelectric transducer. The spiral shape also helps it achieve a low operating frequency suitable for testing biological tissues. The relationship between electrical impedance of the sensor and mechanical impedance of a load is derived based on the transduction matrix model, which forms the basis of the unique simultaneous actuation and sensing (SAS) technique. As a result, hardness of the load could be sensed from the sensor’s electrical impedance by extracting the resonant frequency, with simple instrumentation. The proposed sensor and SAS technique are verified numerically on a finite element model and experimentally on a prototype. After properly choosing the vibration mode and operating frequency range, the sensor is able to perform hardness sensing in a wide range of 0–1.7 MPa. In addition, both simulation and experiment results indicate that the sensor has high sensitivity and low variance in the low-hardness region, and relatively lower sensitivity and higher variance in the high-hardness region, suggesting that the sensor can be used in two different sensing modes (quantitative measurement and qualitative classification) in the two regions, respectively. An ex vivo experiment confirms that the sensor could detect the presence, shape and location of an embedded lump from spatial distribution of tissue hardness acquired through grid-based palpation, followed by an improved k -means clustering algorithm. Compared with traditional hardness sensors, this tactile sensor is developed with a unique spiral shape which reduces the operating frequency for enhancing the interaction with biological tissues while keeps the overall size of the sensor as small as possible. And the proposed unique simultaneous actuation and electrical impedance sensing mode helps simplify the instrumentation, making it easier to integrate the sensor into MIS equipment.

In this paper, a piezoelectric tactile sensor for detecting tissue stiffness in robot-assisted minimally invasive surgery (RMIS) is proposed. It can detect the stiffness not only when the probe is normal to the tissue surface, but also when there is a contact angle between the probe and normal direction. It solves the problem that existing sensors can only detect in the normal direction to ensure accuracy when the degree of freedom (DOF) of surgical instruments is limited. The proposed senor can distinguish samples with different stiffness and recognize lump from normal tissue effectively when the contact angle varies within [0°, 45°]. These are achieved by establishing a new detection model and sensor optimization. It deduces the influence of contact angle on stiffness detection by sensor parameters design and optimization. The detection performance of the sensor is confirmed by simulation and experiment. Five samples with different stiffness (including lump and normal samples with close stiffness) are used. Through blind recognition test in simulation, the recognition rate is 100% when the contact angle is randomly selected within 30°, 94.1% within 45°, which is 38.7% higher than the unoptimized sensor. Through blind classification test and automatic k-means clustering in experiment, the correct rate is 92% when the contact angle is randomly selected within 45°. We can get the proposed sensor can easily recognize samples with different stiffness with high accuracy which has broad application prospects in the medical field.