Legged robots have extremely strong adaptability in different terrain environment and their stable walking in the wild is still a current research hotspot. Although they have shown great motion performance in recent years, most of them rely on high-performance sensors, especially force sensors settled on foot. However, it is impractical to select special forces sensors in the field environment, and the performance degradation and noise during the usage will bring fatal control defects. In order to solve this problem, we propose a foot contact force estimation method that does not require force sensors, and applies the estimated contact force to the impedance control of the legged robot to achieve the robot’s active compliance control. The method of force estimation is based on the generalized momentum of the robot. It only needs to provide the position information of the joint and the information of the applied control torque. The designed feedforward and compensation terms effectively improve the speed and accuracy of the force estimation. We improved the response speed of the system to the contact force by improving the structure of the impedance control, and applied the estimated contact force to the impedance control to achieve the stable motion of the quadruped robot. Simulation experiments verify the effectiveness of the proposed force estimation method.

This paper addresses the trajectory tracking problem for constrained high dynamic electrohydraulic actuator in the presence of time-varying parameters, high frequency external load interference, measurement noise and some unmeasurable states. An adaptive robust optimal control scheme is proposed for the electro-hydraulic actuator in legged robot. The framework of our presented scheme is based on a linear time-varying model predictive controller (LTV-MPC) embedded with a multi-scale online estimator (MEKF). With fast- varying and slow- varying time scales, the MEKF part is used not only for measurable states filtering and unmeasurable states estimation, but also for time-varying parameters and external load interference estimation, which will be integrated into the mpc model in real time. The LTV-MPC part is a trajectory tracking controller designed by constrained MPC with an approximate high-precision real-time model and a rapidly solved cost function, which guarantees that the input and output constraints are satisfied during the receding horizon and optimal control process. Finally, with a series of highly dynamic conditions, the comparison experiment results show that the proposed controller has a simple design process, strong adaptive robust performance and trajectory tracking performance, which verifies the effectiveness of the control scheme.

Prosthetic discrete controller relies on finite state machines to switch between a set of predefined task-specific controllers. Therefore the prosthesis can only perform a limited number of discrete locomotion tasks and need hours to tune the parameters for each user. In contrast, the continuous controller treats a gait cycle in a unified way. Thus it is expected to better facilitate normative biomechanics by providing a gait predictive model to contribute a non-switching controller that supports a continuum of tasks. Furthermore, a better method is to train a personalized trajectory prediction model suitable for personal characteristics according to personal walking data. This paper proposes a Gaussian process enhanced Fourier series (GPEFS) method to construct a gait prediction model that represents the human locomotion as a continuous function of phase, speed and slope. Firstly the joint trajectories are transformed into the Fourier coefficient space by least square method. Then the relationship between each Fourier coefficient and task input can be learned by multiple Gaussian process regression (GPRs) model respectively. Compared with directly using GPR to fit the joint trajectory under multi task, our method greatly reduces the computational burden, so as to meet the real-time application scenario. In addition, in Fourier coefficient space, the difference in all tasks between the Fourier coefficient of personal data and the one of statistical data follows the same trend. Therefore, a personalized prediction model is built to predict an individual’s kinematics over a continuous range of slopes and speeds given only one personalized task at level ground and normal speed. The experimental results show that the gait prediction model and the personalized prediction model are feasible and effective.

A robust adaptive admittance control scheme using human-inspired virtual constraints was presented for a robotic knee prosthesis. The controller is able to deal with the wearer’s motion intention as well as the partial unknown parameter values of the prosthesis dynamics. The desired trajectory of the prosthetic knee joint is parameterized by the amputee-driven thigh phase variable and implemented by human-inspired virtual constraints rather than the preprogrammed time-dependent trajectory or human data replaying. A reference admittance model was set up to make the prosthesis be more compliant in the presence of ground reaction forces impact. A composite reaching law is proposed to cope with the chattering phenomenon and the finite-time convergence problem. The controller is designed by the back-stepping method based on Lyapunov. The tracking performance and the stability of the closed-loop system are proven via Lyapunov stability analysis. The feasibility and effectiveness of the proposed control scheme are proved in simulation results.

The body weight support method of exoskeletons is useful for the rehabilitation of patients with lower limb dyskinesia and performance augmentation of elders. Rather than enhancing the human joint strength directly, the body weight support exoskeleton provides assistance by supporting part of the human body weight and thus releasing the burden experienced by human legs. This paper proposes a two-level hierarchical control method aiming to achieve task-free and strong system robustness. A scale force control method, which can be simply described as making the supporting force of the robot several times the magnitude of a human leg, is developed as the high-level controller. The low-level controller is designed as a fuzzy cerebellar model articulation controller (CMAC)-based adaptive control to enhance the system robustness. The proposed method is applied to a squat movement and a single leg stance movement and is simulated in a combined simulation environment. The results show that the exoskeleton can always provide improved support according to the foot reaction force that is controlled by the user and that the system handles model errors and external disturbances well.

Tailless aircraft suffers from limited yaw control power and poor directional stability inherently. To address these issues in the early design process of a tailless configuration with low costs and risks, this paper presents an innovative experimental approach to control law validation and quantitative flying quality evaluation with a dynamically scaled model mounted on a three degree-of-freedom rig in the wind tunnel. The motion equations of the tailless demonstrator on the rig are derived, and then the comparisons of the lateral-directional flight dynamics between the rig constrained model and the free flight model are carried out. Construction of the control augmentation system for yaw and roll motion is accomplished according to the scale modified criteria of flying qualities. Effectiveness of the designed control law is demonstrated with steady pilot-in-the-loop flights at different airspeeds and angles of attack. The achieved closed-loop flying qualities are evaluated by applying multistep maneuvers for low order equivalent system identification. Whereas severe instability is observed in yaw for the open-loop case, the closed-loop flying quality of the Dutch-roll mode can be improved to level 1 at low angle of attack.