DiffPD: Differentiable Projective Dynamics

We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit timestepping schemes require tiny timesteps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics. Inspired by Projective Dynamics ( PD ), we present Differentiable Projective Dynamics ( DiffPD ), an efficient differentiable soft-body simulator based on PD with implicit time integration. The key idea in DiffPD is to speed up backpropagation by exploiting the prefactorized Cholesky decomposition in forward PD simulation. In terms of contact handling, DiffPD supports two types of contacts: a penalty-based model describing contact and friction forces and a complementarity-based model enforcing non-penetration conditions and static friction. We evaluate the performance of DiffPD and observe it is 4–19 times faster compared with the standard Newton’s method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control. We also apply DiffPD in a reality-to-simulation ( real-to-sim ) example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.

Design and development of multiphase buck converters for voltage regulator modules

<p>Modern microprocessors in high-power applications require a low input voltage and a high input current, necessitating the use of multiphase buck converters. As per microprocessor computing complexity, the power requirements of the switching converter will also be more important and will be increasing as per load demand. Previous studies introduced some methods to achieve the advantages associated with multiphase regulators. This paper presents an effective closed closed-loop control scheme for multiphase buck converters that reduces ripple and improves transient response. It is suitable for applications that require regulated output voltage with effectively reduced ripple. The analysis began with a simulation of the entire design using the OrCAD tool, followed by the construction of a hardware setup. Experiments on a 200 Khz, 9 V, 12 A, 2-phase buck voltage regulator were conducted and the proposed experiment found to be useful.</p>

Enhancement of dynamic performance of wave energy converter by introducing a flow control

The stabilization of power take-off (PTO) is imperative especially under circumstances of fluctuating input wave energy. In this paper, a flow control valve is introduced to optimize the transient process of the hydraulic PTO, which can contribute to a quicker adjustment and a stronger stability. Under variations of input power and load torque in transient process, an open-loop control method and a closed-loop control method are proposed as the opening law of the above valve, and the hydraulic motor speed, the pressure at the accumulator inlet and the generated power are chosen as indicators to examine the regulation performance. Then, the synergic effect of the flow control valve and the accumulator in the transient process is discussed. The effectiveness of the two presented control methods on the fluctuation suppression is respectively tested and compared in both regular wave and irregular wave situations via simulation. To validate the practical effectiveness of the proposed methods, field experiments are conducted. The results demonstrate that the open-loop control can only improve the damping ability of the hydraulic PTO in the speed raising stage, while the closed-loop control can improve the stability both in the speed raising stage and in the load increasing stage.

Integrated Temperature and Position Sensors in a Shape-Memory Driven Soft Actuator for Closed-Loop Control

Soft actuators are a promising option for the advancing fields of human-machine interaction and dexterous robots in complex environments. Shape memory alloy wire actuators can be integrated into fiber rubber composites for highly deformable structures. For autonomous, closed-loop control of such systems, additional integrated sensors are necessary. In this work, a soft actuator is presented that incorporates fiber-based actuators and sensors to monitor both deformation and temperature. The soft actuator showed considerable deformation around two solid body joints, which was then compared to the sensor signals, and their correlation was analyzed. Both, the actuator as well as the sensor materials were processed by braiding and tailored fiber placement before molding with silicone rubber. Finally, the novel fiber-rubber composite material was used to implement closed-loop control of the actuator with a maximum error of 0.5°.

Vector Control and Modeling of Modular Multilevel High Voltage Converter for Rail Vehicle Traction

Rail transit plays an important role in the social and economic life of China and even all countries in the world, especially some populous countries or regions. The traction drive system of rail vehicle provides three-phase AC with adjustable voltage and frequency for the traction motor, controls the speed and torque of the traction motor, and then controls the operation of the vehicle. The modular multilevel converter has the advantages of low harmonic, good power quality of output waveform, high reliability, no input filtering and power compensation, and is suitable in the field of frequency conversion. In this work, the open-loop scalar control and vector closed-loop control of modular multi-level high-voltage inverter were adopted. It was found that driven by modular multi-level variable frequency vector control system, asynchronous motor not only has less harmonic content of voltage and current waveform, but also its speed regulation characteristics have been improved.

Manipulation of free-floating objects using Faraday flows and deep reinforcement learning

The ability to remotely control a free-floating object through surface flows on a fluid medium can facilitate numerous applications. Current studies on this problem have been limited to uni-directional motion control due to the challenging nature of the control problem. Analytical modelling of the object dynamics is difficult due to the high-dimensionality and mixing of the surface flows while the control problem is hard due to the nonlinear slow dynamics of the fluid medium, underactuation, and chaotic regions. This study presents a methodology for manipulation of free-floating objects using large-scale physical experimentation and recent advances in deep reinforcement learning. We demonstrate our methodology through the open-loop control of a free-floating object in water using a robotic arm. Our learned control policy is relatively quick to obtain, highly data efficient, and easily scalable to a higher-dimensional parameter space and/or experimental scenarios. Our results show the potential of data-driven approaches for solving and analyzing highly complex nonlinear control problems.

Online Gain Adaptation of Whole-Body Control for Legged Robots with Unknown Disturbances

This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie.

A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems

This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady-state performance.