In this paper, a neural network-based (NN-based) integral sliding mode control approach is presented for a piezoelectric ultrasonic motor. The precision motion performance of the motor can be adversely affected in the presence of nonlinearities including friction and disturbance, as well as parameters uncertainties. Integral sliding mode control is effective in dealing with uncertainties and disturbances. To achieve better performance on tracking desired motion trajectories, a neural network structure with modified jump basis functions are used to model and compensate the discontinuous friction in the motor control systems. This structure can approximate friction with high accuracy but require few NN nodes. Stability of the proposed control strategy is analyzed. The simulation studies are provided to demonstrate the precise tracking performance of the proposed control scheme.

In consideration of the requirements of the polarization alignment in optical switch assembly and the overloading protection for the optical fiber in contact, a novel 2-DOF constant force microgripper is developed with the functional modules of grasping and rotating under overloading protection. Based on the negative stiffness effect, the coantant force mechanisms take more advantages in microassembly than traditional grippers with integrated force sensors in terms of the simplification of control algorithms and the miniaturization of overall size. To avoid the surface damage by the overloaded grasping and the rubbing operation, a parallel constant force microgripper with a grasping module and a rotating module is designed to avoid sliding friction of optical fiber during assembly. Then, the analytical models are constructed to derive the static and kinematic performances. Finally, the analytical results are validated. It is observed that i) the grasping force is approximately constant at 544.69 mN in a range between 835 μm and 1336 μm; ii) the constant force rotating stroke reaches 6.70°.

Despite plenty of motion planning strategies have been proposed for bipedal locomotion, enhancing the walking robustness in real-world environments is still an open question. This paper focuses on robust body and leg trajectories synthesis through integrating constrained optimization with imitation learning. Specifically, we first propose a Quadratically Constrained Quadratic Programming (QCQP) algorithm to make use of the ankle strategy and stepping strategy. Based on the Linear Inverted Pendulum (LIP) model, body motion can be determined by the modulated Center of Pressure (CoP) position and step parameters (including step location and step duration). After that, we exploit an imitation learning approach Kernelized Movement Primitives (KMP) to plan robot leg motions, which allows for adapting the learned motion patterns to new situations (e.g., passing through various desired points) in a straightforward manner. Several LIP simulations and whole-body dynamic simulations demonstrate that higher walking robustness can be achieved using our framework.

When experiencing disturbances during locomotion, human beings use several strategies to maintain balance, e.g. changing posture, modulating step frequency and location. However, when it comes to the gait generation for humanoid robots, modifying step time or body posture in real time introduces nonlinearities in the walking dynamics, thus increases the complexity of the planning. In this paper, we propose a two-layer hierarchical optimization framework to address this issue and provide the humanoids with the abilities of step time and step location adjustment, Center of Mass (CoM) height variation and angular momentum adaptation. In the first layer, times and locations of consecutive two steps are modulated online based on the current CoM state using the Linear Inverted Pendulum Model. By introducing new optimization variables to substitute the hyperbolic functions of step time, the derivatives of the objective function and feasibility constraints are analytically derived, thus reduces the computational cost. Then, taking the generated horizontal CoM trajectory, step times and step locations as inputs, CoM height and angular momentum changes are optimized by the second layer nonlinear model predictive control. This whole procedure will be repeated until the termination condition is met. The improved recovery capability under external disturbances is validated in simulation studies.

To compensate the nonlinear effects of a nanopositioning stage and its model uncertainties, this paper presents a composite controller by integrating disturbance observer (DOB) and model prediction control (MPC). The nonlinearity and dynamic cross-coupling effects are treated as unknown disturbances to the system, and a DOB is used to partially estimate and compensate the disturbance to improved performance. A MPC is commonly used to track a reference signal, which minimizes the steady-state tracking error as well as increases the closed-loop bandwidth. Inspired by existing MPC schemes to improve the positioning performance of the piezo-actuated stage, a two-input two-output (TITO) form of the MPC controller is designed in this work with the estimated residual error being used to modify the reference in real time for better performance. The novelty of the proposed TITO MPC lies not only the compensation for nonlinear effects but also the rejection of cross-coupling dynamics effects in a 2-DOF nanopositioning system. The effectiveness of the proposed controller is validated through experimental investigations on a commercial nanopositioner platform. Experimental results show that the proposed method can improve the tracking performance of the piezo-actuated stage, compared to the traditional MPC.

The modulation of step location and duration plays an important role in realizing robust bipedal walking. This paper formulates it as a nonlinear programming problem (NLP) and proposes a novel optimization approach to adjust step location and duration in real time. Based on state feedback, the Linear Inverted Pendulum dynamics is exploited to determine the optimal step parameters. Different from previous works, this work presents three main characteristics: i) the hyperbolic functions of step duration rather than the step duration itself are chosen to be optimization variables; ii) the approach can be switched from baseline two-steps-prediction optimization to one-step-prediction optimization through merely adding several equality constraints in problem formulation; iii) the approach can deal with relative step location tracking (velocity tracking) or absolute step location tracking (position tracking) via changing the reference step parameters. As a result, the first characteristic enables the NLP to be solved in a computational-efficient manner and the latter two endow the approach with versatility under different control modes. The effectiveness has been demonstrated by simulation experiments.

Patient transportation in hospitals faces many challenges, including the limited manpower, work-related injuries, and low efficiency of current bed pushing methods. This paper presents a new motorized robotic bed mover with omnidirectional mobility to address this problem. This device is composed of an omnidirectional mobility unit, a force sensing-based human-machine interface, and control hardware with batteries and electronics. The proposed bed mover can be attached to the bottom of a manual hospital stretcher, transforming it into a powered omnidirectional bed (OmniBed) that can be used only by one person. The function of the OmniBed is compared with that of a conventional powered bed, which only provides forward assistance with a fifth powered wheel. We perform a pilot study with 14 subjects to evaluate the performance of this OmniBed and benefits for hospital application. The experimental results show that the OmniBed can half the manpower while decreasing back muscle activities, revealing the potential health benefits for older staffs. The OmniBed also shows the promising signs of high precision and handling in small spaces with its one-step "parallel-parking" ability. This device is more ergonomic, more effective, and safer than the conventional powered bed.

Compliant actuator is widely accepted for physical human-robot interaction due to its safety aspect, dynamic performance improvements and energy saving abilities. In this paper, based on the variable ratio lever mechanism, a new kind of Serial Variable Stiffness Actuator (SVSA) is proposed by using an Archimedean Spiral Relocation Mechanism (ASRM) to change the position of the pivot, implementing large range of adjustable stiffness. The ASRM introduced here makes the SVSA design has continuous stiffness adjustment ability and simply mechanical structure. Within the Variable Stiffness Mechanism (VSM), two linear springs are assembled antagonistically on a spring shaft. Their displacements are perpendicular to the output link to transmit the spring force more efficiently. Stiffness modeling and analysis of the SVSA are carried out to cover large deflection angle. The physical implementation of the SVSA shows that the output stiffness of the VSM is changed from 1.72 to 150.56 Nm/rad using a linear spring with stiffness 1882 N/m, working range covered from 0 to 360°. Control experiments also proved the wide range of stiffness adjustment ability of the SVSA.