A novel trajectory-based online controller design approach to fault accommodation in NREL’s 5MW wind turbine systems

This paper presents a real-time mechanism to tolerate faults occurring in a wind turbine （WT） system. This system is composed of a FAST coded simulator designed by the U.S. National Renewable Energy Laboratory. The demonstrated mechanism lies under the taxonomy of active fault-tolerant control （FTC） systems, namely online redesign based approach. In the proposed approach, we do not use any a priori information about the model of the turbine in real-time. In fact, we use online measurements generated by the WT. Based on the given control specifications, and the observed measurement an occurred fault is accommodated by reconfiguring the online controller such that the WT generates rated power even under faulty conditions. Second, no explicit fault diagnosis （FD） module is used in this approach. As a result, issues of model uncertainty, false alarms, etc. associated with an integrated FD and controller reconfiguration approach to FTC systems are not experienced here.

Online Learning Control for Harmonics Reduction Based on Current Controlled Voltage Source Power Inverters

Nonlinear loads in the power distribution system cause non-sinusoidal currents and voltages with harmonic components.Shunt active filters(SAF) with current controlled voltage source inverters(CCVSI) are usually used to obtain balanced and sinusoidal source currents by injecting compensation currents.However,CCVSI with traditional controllers have a limited transient and steady state performance.In this paper,we propose an adaptive dynamic programming(ADP) controller with online learning capability to improve transient response and harmonics.The proposed controller works alongside existing proportional integral(PI) controllers to efficiently track the reference currents in the d-q domain.It can generate adaptive control actions to compensate the PI controller.The proposed system was simulated under different nonlinear(three-phase full wave rectifier) load conditions.The performance of the proposed approach was compared with the traditional approach.We have also included the simulation results without connecting the traditional PI control based power inverter for reference comparison.The online learning based ADP controller not only reduced average total harmonic distortion by 18.41%,but also outperformed traditional PI controllers during transients.

Learn to swim:Online motion control of an underactuated robotic eel based on deep reinforcement learning

As a typical underwater robot,the robotic fish has been a hot topic of research in terms of the swimming control method and the reduction of energy consumption.Body flexibility and passive designs have been proven to be effective approaches for improving the swimming performance of robotic fishes.However,the addition of passive structures or motions of these approaches makes it more difficult to control the movement of robotic fish.In this paper,we proposed a deep reinforcement learning-based method for online learning control of a robotic eel with multiple passive structures.First,we designed a robotic eel with two wire-driven segments and two complaint bodies made of elastic material.Second,a simulation model of the robotic eel was built and the validity of the model was tested.Following that,the neural network was trained in simulation and deployed directly on the robotic eel,which was controlled without an underlying control model or strategy and directly online by the neural network.Finally,extensive experiments verified the effectiveness of our control method,which offers a valuable and potential solution for robots difficult to model and control.

Online optimal control of nonlinear discrete-time systems using approximate dynamic programming

In this paper,the optimal control of a class of general affine nonlinear discrete-time(DT) systems is undertaken by solving the Hamilton Jacobi-Bellman(HJB) equation online and forward in time.The proposed approach,referred normally as adaptive or approximate dynamic programming(ADP),uses online approximators(OLAs) to solve the infinite horizon optimal regulation and tracking control problems for affine nonlinear DT systems in the presence of unknown internal dynamics.Both the regulation and tracking controllers are designed using OLAs to obtain the optimal feedback control signal and its associated cost function.Additionally,the tracking controller design entails a feedforward portion that is derived and approximated using an additional OLA for steady state conditions.Novel update laws for tuning the unknown parameters of the OLAs online are derived.Lyapunov techniques are used to show that all signals are uniformly ultimately bounded and that the approximated control signals approach the optimal control inputs with small bounded error.In the absence of OLA reconstruction errors,an optimal control is demonstrated.Simulation results verify that all OLA parameter estimates remain bounded,and the proposed OLA-based optimal control scheme tunes itself to reduce the cost HJB equation.

Combining the People Power and the Process Power to Achieve Six Sigma Process Capability——A Case Study

Achieving Six-Sigma process capability starts with l istening to the Voice of the Customers, and it becomes a reality by combining th e People Power and the Process Power of the organisation. This paper presents a Six-Sigma implementation case study carried out in a magnet manufacturing compa ny, which produces bearing magnets to be used in energy meters. If the thickness of the produced bearing magnets is between 2.35 mm and 2.50 mm, they will be ac cepted by the customers. All the time the company could not produce the bearing magnets within the specified thickness range, as their process distribution was flat with 2.20 mm as lower control limit and 2.60 mm as upper control limit. This resulted in a huge loss in the form of non-conformities, loss of time and goodwill. The process capability of the company then was around 0.40. Organisat ion restructuring was carried out to reap the benefit of the People Power of the organisation. Statistically designed experiments (Taguchi Method based Design o f Experiments), Online quality control tools (Statistical Process Control To ols) were effectively used to complete the DMAIC (Define, Measure, Analyse, Impr ove and Control) cycle to reap the benefit of the Process Power of the organisat ion. Presently the company enjoys a process capability of 1.75, a way towards Si x-Sigma Process Capability.

On-line controlling system for nitrogen and phosphorus removal of municipal wastewater in a sequencing batch reactor (SBR)

The objectives of this study were to establish an on-line controlling system for nitrogen and phosphorus removal synchronously of municipal wastewater in a sequencing batch reactor(SBR).The SBR for municipal wastewater treatment was operated in sequences:filling,anaerobic,oxic,anoxic,oxic,settling and discharge.The reactor was equipped with on-line monitoring sensors for dissolved oxygen(DO),oxidation-reduction potential(ORP)and pH.The variation of DO,ORP and pH is relevant to each phase of biological process for nitrogen and phosphorus removal in this SBR.The characteristic points of DO,ORP and pH can be used to judge and control the stages of process that include:phosphate release by the turning points of ORP and pH;nitrification by the ammonia valley of pH and ammonia elbows of DO and ORP;denitrification by the nitrate knee of ORP and nitrate apex of pH;phosphate uptake by the turning point of pH;and residual organic carbon oxida-tion by the carbon elbows of DO and ORP.The controlling system can operate automatically for nitrogen and phosphorus efficiently removal.