An Adaptive Observer-Based Controller Design for Active Damping of a DC Network with a Constant Power Load

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis article explores a nonlinear, adaptive controller aimed at increasing the stability margin of a direct-current (dc), small-scale, electrical network containing an unknown constant power load (CPL). Due to its negative incremental impedance, this load reduces the effective damping of the network, which may lead to voltage oscillations and even to voltage collapse. To overcome this drawback, we consider the incorporation of a controlled dc-dc power converter in parallel with the CPL. The design of the control law for the converter is particularly challenging due to the existence of unmeasured states and unknown parameters. We propose a standard input-output linearization stage, to which a suitably tailored adaptive observer is added. The good performance of the controller is validated through experiments on a small-scale network.Peer ReviewedPostprint (author's final draft

An adaptive observer-based controller design for active damping of a DC network with a constant power load

This article explores a nonlinear, adaptive controller aimed at increasing the stability margin of a direct-current (dc), small-scale, electrical network containing an unknown constant power load (CPL). Due to its negative incremental impedance, this load reduces the effective damping of the network, which may lead to voltage oscillations and even to voltage collapse. To overcome this drawback, we consider the incorporation of a controlled dc-dc power converter in parallel with the CPL. The design of the control law for the converter is particularly challenging due to the existence of unmeasured states and unknown parameters. We propose a standard input-output linearization stage, to which a suitably tailored adaptive observer is added. The good performance of the controller is validated through experiments on a small-scale network

Historical overview of the passification method and its applications to nonlinear and adaptive control problems

The present survey paper provides a historical overview of the method of passification and its applications to nonlinear and adaptive control problems from 1980 to present days

Human-Robot Collaboration for Kinesthetic Teaching

Recent industrial interest in producing smaller volumes of products in shorter time frames, in contrast to mass production in previous decades, motivated the introduction of human–robot collaboration (HRC) in industrial settings, as an attempt to increase flexibility in manufacturing applications by incorporating human intelligence and dexterity to these processes. This thesis presents methods for improving the involvement of human operators in industrial settings where robots are present, with a particular focus on kinesthetic teaching, i.e., manually guiding the robot to define or correct its motion, since it can facilitate non-expert robot programming.To increase flexibility in the manufacturing industry implies a loss of a fixed structure of the industrial environment, which increases the uncertainties in the shared workspace between humans and robots. Two methods have been proposed in this thesis to mitigate such uncertainty. First, null-space motion was used to increase the accuracy of kinesthetic teaching by reducing the joint static friction, or stiction, without altering the execution of the robotic task. This was possible since robots used in HRC, i.e., collaborative robots, are often designed with additional degrees of freedom (DOFs) for a greater dexterity. Second, to perform effective corrections of the motion of the robot through kinesthetic teaching in partially-unknown industrial environments, a fast identification of the source of robot–environment contact is necessary. Fast contact detection and classification methods in literature were evaluated, extended, and modified to use them in kinesthetic teaching applications for an assembly task. For this, collaborative robots that are made compliant with respect to their external forces/torques (as an active safety mechanism) were used, and only embedded sensors of the robot were considered.Moreover, safety is a major concern when robotic motion occurs in an inherently uncertain scenario, especially if humans are present. Therefore, an online variation of the compliant behavior of the robot during its manual guidance by a human operator was proposed to avoid undesired parts of the workspace of the robot. The proposed method used safety control barrier functions (SCBFs) that considered the rigid-body dynamics of the robot, and the method’s stability was guaranteed using a passivity-based energy-storage formulation that includes a strict Lyapunov function.All presented methods were tested experimentally on a real collaborative robot

Multi-terminal dc grid overall control with modular multilevel converters

This paper presents a control philosophy for multiterminal DC grids, which are embedded in the main AC grid. DC transmission lines maintain higher power flow at longer distances compared with AC lines. The voltage losses are also much lower. DC power transmission is good option for Russian north. Arctic seashore regions of Russia don't have well developed electrical infrastructure therefore power line lengths are significant there. Considering above it is possible to use DC grids for supply mining enterprises in Arctic regions (offshore drilling platforms for example). Three different control layers are presented in an hierarchical way: local, primary and secondary. This whole control strategy is verified in a scaled three-nodes DC grid. In one of these nodes, a modular multilevel converter (MMC) is implemented (five sub-modules per arm). A novel model-based optimization method to control AC and circulating currents is discussed. In the remaining nodes, three-level voltage source converters (VSC) are installed. For their local controllers, a new variant for classical PI controllers are used, which allow to adapt the values of the PI parameters with respect to the measured variables. Concerning the primary control, droop control technique has been chosen. Regarding secondary level, a new power flow technique is suggested. Unbalance conditions are also verified in order to show the robustness of the whole control strategy

An Edge Architecture Oriented Model Predictive Control Scheme for an Autonomous UAV Mission

In this article the implementation of a controller and specifically of a Model Predictive Controller (MPC) on an Edge Computing device, for controlling the trajectory of an Unmanned Aerial Vehicle (UAV) model, is examined. MPC requires more computation power in comparison to other controllers, such as PID or LQR, since it use cost functions, optimization methods and iteratively predicts the output of the system and the control commands for some determined steps in the future (prediction horizon). Thus, the computation power required depends on the prediction horizon, the complexity of the cost functions and the optimization. The more steps determined for the horizon the more efficient the controller can be, but also more computation power is required. Since sometimes robots are not capable of managing all the computing process locally, it is important to offload some of the computing process from the robot to the cloud. But then some disadvantages may occur, such as latency and safety issues. Cloud computing may offer "infinity" computation power but the whole system suffers in latency. A solution to this is the use of Edge Computing, which will reduce time delays since the Edge device is much closer to the source of data. Moreover, by using the Edge we can offload the demanding controller from the UAV and set a longer prediction horizon and try to get a more efficient controller.Comment: 7 pages, 13 figures, isie 202

Interval Observer-based Feedback Control for Rehabilitation in Tremor

International audienceTremor, one of the most common health disorders , is defined as a chronic movement disorder. To reduce tremor in patients, the design of stabilizing techniques is critical. For this purpose, we consider an uncertain continuous-time linear time-varying oscillator model of tremor in this article. We design a state feedback control for deep brain stimulation technique by considering the practical case in which only sets of admissible values are given for the nominal values of the stimulation amplitude, the time-varying tremor's angular frequency and the tremor measurement noise. First, we estimate state signal bounds that include the true unknown value of the state. Next, we design a stabilizing control input based on the estimated bounds. The stability of the controlled system is verified using linear matrix inequalities (LMIs). We demonstrate the methodology's performance in numerical simulations

Evaluation of the region-specific risks of accidental radioactive releases from the European Spallation Source

The European Spallation Source (ESS) is a neutron research facility under construction in southern Sweden. The facility will produce a wide range ofradionuclides that could be released into the environment. Some radionuclides are of particular concern such as the rare earth gadolinium-148. In this article, the local environment was investigated in terms of food production and rare earth element concentration in soil. The collected data will later be used to model thetransfer of radioactive contaminations from the ESS

Parameterized Convex Minorant for Objective Function Approximation in Amortized Optimization

Parameterized convex minorant (PCM) method is proposed for the approximation of the objective function in amortized optimization. In the proposed method, the objective function approximator is expressed by the sum of a PCM and a nonnegative gap function, where the objective function approximator is bounded from below by the PCM convex in the optimization variable. The proposed objective function approximator is a universal approximator for continuous functions, and the global minimizer of the PCM attains the global minimum of the objective function approximator. Therefore, the global minimizer of the objective function approximator can be obtained by a single convex optimization. As a realization of the proposed method, extended parameterized log-sum-exp network is proposed by utilizing a parameterized log-sum-exp network as the PCM. Numerical simulation is performed for parameterized non-convex objective function approximation and for learning-based nonlinear model predictive control to demonstrate the performance and characteristics of the proposed method. The simulation results support that the proposed method can be used to learn objective functions and to find a global minimizer reliably and quickly by using convex optimization algorithms.Comment: 12 pages, 4 figure