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Liquid crystal elastomer (LCE) is a promising candidate for actuation in light-driven soft robot applications. Due to the fact that LCE has complex hysteretic nonlinearities, which are highly dependent on the environment, modeling of actuators made of LCE is a very challenging issue. In this article, a model is proposed to describe the deformation of the LCE actuator accurately and analytically by considering the dynamic phase transition process of LCE molecules. First, an overview of the physical process of LCE's deformation is presented, and the schematic of the LCE actuator, as well as the modeling scheme are then introduced. Next, a thermodynamic analysis of the system's free energy is performed to establish the model for the LCE actuator, which gives the relationship between the system's deformation and the temperature. Here, to describe the complex hysteretic nonlinearity in the model, the dynamic process of the phase transition of LCE molecules is exploited. To effectively identify the model parameters, a two-step parameter identification strategy based on the differential evolution algorithm and nonlinear least-squares method is utilized. Finally, experimental results verify the validity of the proposed model. This modeling provides an approach to describe LCE's deformation with high accuracy and can fully reflect the physical nature of LCE's deformation, especially hysteresis. It can be utilized as a basis for accurate control over LCE actuators in photoresponsive soft robot applications.

This article proposes a targeting posture control approach with dynamic obstacle avoidance of differential-drive wheeled mobile robot (WMR) systems in the presence of unknown skidding, slipping, input disturbances, model uncertainties, and torque saturation. First, a nonlinear model predictive control (NMPC) scheme is presented to generate a feasible trajectory from a starting posture to a targeting posture where dynamic obstacles in the environment and physical constraints of the robots are considered. Second, a robust virtual control law at the kinematic level is introduced for the robots to follow the trajectory. Third, taking the consideration of the unknown skidding, slipping, input disturbances, and model uncertainties in the dynamic model being lumped as a total disturbance, which is estimated by the linear extended state observer (LESO), a disturbance compensation-based saturation controller is designed to make the real velocity of the robot converge to the virtual velocity command. Finally, the effectiveness of the proposed control strategy is verified by simulation results.

The study on the stabilization of planar underactuated manipulators without gravity is well recognized as a major challenge since the system includes a second-order nonholonomic constraint when the passive link is not located at the first link. It is important to solve this difficulty for applications such as systems working in aerospace or underwater. This article presents a position control method based on bidirectional motion planning and intelligent optimization for this kind of system. The control objective is to move the end effector of the manipulator from its initial position to a given target position. The differential evolution algorithm is applied to solve the target angles of all links corresponding to the target position of the end effector. Then, bidirectional motion planning is performed, which consists of forward and backward motions. Each motion is planned by designing a trajectory for every active link based on their initial and target angles. During the forward motion, all active links except the first one are moved to their target angles, and the first active link and the passive link to the intermediate angles. For the backward motion, the first active link is moved to its target angle, the other active links remain at their target angles, and the passive link will be moved to its target angle at the same time. The planned trajectories are chosen based on the time-scaling method and differential evolution algorithm to make sure that the forward and backward planned motions can be connected smoothly. Finally, the trajectory tracking controllers for all active links are designed based on the sliding-mode control method. The proposed control method is verified on planar four-link underactuated manipulators with different passive joints. This strategy has the advantage that it works for planar underactuated manipulators with a second-order nonholonomic constraint whose passive link can be at different positions. Meanwhile, by combining intelligent optimization with bidirectional motion planning, the control process becomes simpler and more effective.

This paper investigates dynamic balance optimization and control of quadruped robots with compliant/flexible joints under perturbing external forces. First, we formulate a constrained dynamic model of compliant/flexible joints for quadruped robots and a reduced-order dynamic model is developed considering the robot interaction with the environment through multiple contacts. A dynamic force distribution approach based on quadratic objective function is proposed for evaluating the optimal contact forces to cope with the external wrench, and fuzzy-based adaptive control of compliant/flexible joints for quadruped robots is proposed to suppress uncertainties in the dynamics of the robot and actuators. The dynamic surface control approaches and fuzzy learning algorithms are combined in the proposed framework. All the signals of the closed-loop system have proven to be uniformly ultimately bounded through Lyapunov synthesis. Simulation experiments were performed for a quadruped robot with compliant/flexible joints. The benefits of its tracking accuracy and robustness indicate that the proposed framework is promising for the robots with payload uncertainties and external disturbances.

In this paper, adaptive control is proposed for hybrid position/force synchronization of master-slave teleoperation systems with time-varying delays in communication channels. Different from previous works on bilateral teleoperation systems, we investigate position/force control of bilateral teleoperation systems subject to time-varying delays and dynamical uncertainties. Using partial feedback linearization, the dynamics of both master and slave are transformed into two subsystems: local master/slave position/force control with unmodeled dynamics and delayed position/force synchronization. An adaptive control is proposed to deal with the dynamical uncertainties and robust against time delays, which guarantees the position/force synchronization trajectories converge to the desired manifolds with prescribed performance. The stability of the closed-loop system and the boundedness of synchronization tracking errors are proved using linear matrix inequalities based on Lyapunov stability synthesis. The position/force tracking error up to an ultimately bounded error is achieved. The proposed adaptive controls are robust against relative motion disturbances, parametric uncertainties, and time delays, and are validated by experimental studies.