Contact-Implicit Trajectory Optimization Based on a Variable Smooth Contact Model and Successive Convexification

In this paper, we propose a contact-implicit trajectory optimization (CITO) method based on a variable smooth contact model (VSCM) and successive convexification (SCvx). The VSCM facilitates the convergence of gradient-based optimization without compromising physical fidelity. On the other hand, the proposed SCvx-based approach combines the advantages of direct and shooting methods for CITO. For evaluations, we consider non-prehensile manipulation tasks. The proposed method is compared to a version based on iterative linear quadratic regulator (iLQR) on a planar example. The results demonstrate that both methods can find physically-consistent motions that complete the tasks without a meaningful initial guess owing to the VSCM. The proposed SCvx-based method outperforms the iLQR-based method in terms of convergence, computation time, and the quality of motions found. Finally, the proposed SCvx-based method is tested on a standard robot platform and shown to perform efficiently for a real-world application.Comment: Accepted for publication in ICRA 201

Plan-Guided Reinforcement Learning for Whole-Body Manipulation

Synthesizing complex whole-body manipulation behaviors has fundamental challenges due to the rapidly growing combinatorics inherent to contact interaction planning. While model-based methods have shown promising results in solving long-horizon manipulation tasks, they often work under strict assumptions, such as known model parameters, oracular observation of the environment state, and simplified dynamics, resulting in plans that cannot easily transfer to hardware. Learning-based approaches, such as imitation learning (IL) and reinforcement learning (RL), have been shown to be robust when operating over in-distribution states; however, they need heavy human supervision. Specifically, model-free RL requires a tedious reward-shaping process. IL methods, on the other hand, rely on human demonstrations that involve advanced teleoperation methods. In this work, we propose a plan-guided reinforcement learning (PGRL) framework to combine the advantages of model-based planning and reinforcement learning. Our method requires minimal human supervision because it relies on plans generated by model-based planners to guide the exploration in RL. In exchange, RL derives a more robust policy thanks to domain randomization. We test this approach on a whole-body manipulation task on Punyo, an upper-body humanoid robot with compliant, air-filled arm coverings, to pivot and lift a large box. Our preliminary results indicate that the proposed methodology is promising to address challenges that remain difficult for either model- or learning-based strategies alone.Comment: 4 pages, 4 figure

A Comparative Analysis of Contact Models in Trajectory Optimization for Manipulation

In this paper, we analyze the effects of contact models on contact-implicit trajectory optimization for manipulation. We consider three different approaches: (1) a contact model that is based on complementarity constraints, (2) a smooth contact model, and our proposed method (3) a variable smooth contact model. We compare these models in simulation in terms of physical accuracy, quality of motions, and computation time. In each case, the optimization process is initialized by setting all torque variables to zero, namely, without a meaningful initial guess. For simulations, we consider a pushing task with varying complexity for a 7 degrees-of-freedom robot arm. Our results demonstrate that the optimization based on the proposed variable smooth contact model provides a good trade-off between the physical fidelity and quality of motions at the cost of increased computation time.Comment: 6 pages, 7 figures, 4 tables, IROS 2018 camera-ready versio

Transient performance of a vertical axis wind turbine

A coupled CFD/rotor dynamics modeling approach is presented for the analysis of realistic transient behavior of a height-normalized, three-straight-bladed VAWT subject to inertial effects of the rotor and generator load which is manipulated by a feedback control under standardized wind gusts. The model employs the k-ε turbulence model to approximate unsteady Reynolds-averaged Navier-Stokes equations and is validated with data from field measurements. As distinct from related studies, here, the angular velocity is calculated from the rotor's equation of motion; thus, the dynamic response of the rotor is taken into account. Results include the following: First, the rotor's inertia filters large amplitude oscillations in the wind torque owing to the first-order dynamics. Second, the generator and wind torques differ especially during wind transients subject to the conservation of angular momentum of the rotor. Third, oscillations of the power coefficient exceed the Betz limit temporarily due to the energy storage in the rotor, which acts as a temporary buffer that stores the kinetic energy like a flywheel in short durations. Last, average of transient power coefficients peaks at a smaller tip-speed ratio for wind gusts than steady winds

Hardware-in-the-loop simulations and control design for a small vertical axis wind turbine

Control design plays an important role in wind energy conversion systems in achieving high efficiency and performance. In this study, hardware-in-the-loop (HIL) simulations are carried out to design a maximum power point tracking (MPPT) algorithm for small vertical axis wind turbines (VAWTs). Wind torque is calculated and applied to an electrical motor that drives the generator in the HIL simulator, which mimics the dynamics of the rotor. To deal with disturbance torques in the HIL system, a virtual plant is introduced to obtain an error between the speeds in the HIL system and virtual plant. This error is used by a proportional-integral (PI) controller to generate a disturbance torque compensation signal. The MPPT algorithm is tested in the HIL simulator under various wind conditions, and the results are compared with numerical simulations. The HIL simulator successfully mimics the dynamics of the VAWT under various wind conditions and provides a realistic framework for control designs

Küçük dikey eksenli rüzgâr türbini için basit kontrol tasarımı (Simple control design for a small vertical axis wind turbine)

Bu makalede, küçük dikey eksenli rüzgâr türbinin elde ettiği enerjiyi maksimize edecek basit bir kontrolör tasarlanmıştır. Bu önerilen kontrol algoritmasının amacı mevcut sistemlere kıyasla daha basit bir yapıda olmasıdır. Algoritma kontrol işlemini sisteme uygulanan yük katsayısını önceden belirlenen değer aralıklarında müdahalede bulunarak yapabilmektedir. Bunu yapmak için önceden enerjiyi maksimize eden bir optimizasyon yöntemiyle belirlenmiş olan sınır değerlerinden faydalanmaktadır. Bu makalede, değişik simülasyonlar sonucu elde edilen enerjiyi maksimize ederken, basitleştirilmiş bir dikey eksenli rüzgâr türbini modeli kullanılmıştır

Model predictive control for energy maximization of small vertical axis wind turbines

In this paper, a model predictive control (MPC) approach is presented to maximize the energy generated by a small vertical axis wind turbine (VAWT) subject to current and voltage constraints of electrical and power electronic components. Our method manipulates a load coefficient and optimizes the control trajectory over a prediction horizon such that a cost function that measures the deviation from the maximum available energy and the violation of current and voltage constraints is minimized. Simplified models for the VAWT and a permanent magnet generator have been used. A number of simulations have been carried out to demonstrate the performance of the proposed method at step and oscillatory wind conditions. Furthermore, impacts of the constraints on energy generation have been investigated. Moreover, the performance of the MPC has been compared with a typical maximum power point tracking algorithm in order to show that maximizing the instantaneous power does not mean maximizing the energy; and simulation results have shown that the MPC outperforms the maximum power point tracking algorithm in terms of generated energy by allowing deviations from the maximum power instantaneously for future gains in energy generation