Genetic Algorithm Based Control System Design of a Self-Excited Induction Generator

This paper presents an application of the genetic algorithm (GA) for optimizing controller gains of the Self-Excited Induction Generator (SEIG) driven by the Wind Energy Conversion Scheme (WECS). The proposed genetic algorithm is introduced to adapt the integral gains of the conventional controllers of the active and reactive control loop of the system under study, where GA calculates the optimum value for the gains of the variables based on the best dynamic performance and a domain search of the integral gains. The proposed genetic algorithm is used to regulate the terminal voltage or reactive power control, by adjusting the self excitation, and to control the mechanical input power or active power control by adapting the blade angle of WECS, in order to adjust the stator frequency. The GA is used for optimizing these gains, for an active and reactive power loop, by solving the related optimization problem. The simulation results show a better dynamic performance using the GA than using the conventional PI controller for active and reactive control

Evolutionary design of a full-envelope full-authority flight control system for an unstable high-performance aircraft

The use of an evolutionary algorithm in the framework of H1 control theory is being considered as a means for synthesizing controller gains that minimize a weighted combination of the infinite norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements) at the same time. The case study deals with a complete full-authority longitudinal control system for an unstable high-performance jet aircraft featuring (i) a stability and control augmentation system and (ii) autopilot functions (speed and altitude hold). Constraints on closed-loop response are enforced, that representing typical requirements on airplane handling qualities, that makes the control law synthesis process more demanding. Gain scheduling is required, in order to obtain satisfactory performance over the whole flight envelope, so that the synthesis is performed at different reference trim conditions, for several values of the dynamic pressure, used as the scheduling parameter. Nonetheless, the dynamic behaviour of the aircraft may exhibit significant variations when flying at different altitudes, even for the same value of the dynamic pressure, so that a trade-off is required between different feasible controllers synthesized at different altitudes for a given equivalent airspeed. A multiobjective search is thus considered for the determination of the best suited solution to be introduced in the scheduling of the control law. The obtained results are then tested on a longitudinal non-linear model of the aircraft

Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations

Safe Controller Optimization for Quadrotors with Gaussian Processes

One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters. Typically, a model of the system is used to obtain an initial controller, but ultimately the controller parameters must be tuned manually on the real system to achieve the best performance. To avoid this manual tuning step, methods from machine learning, such as Bayesian optimization, have been used. However, as these methods evaluate different controller parameters on the real system, safety-critical system failures may happen. In this paper, we overcome this problem by applying, for the first time, a recently developed safe optimization algorithm, SafeOpt, to the problem of automatic controller parameter tuning. Given an initial, low-performance controller, SafeOpt automatically optimizes the parameters of a control law while guaranteeing safety. It models the underlying performance measure as a Gaussian process and only explores new controller parameters whose performance lies above a safe performance threshold with high probability. Experimental results on a quadrotor vehicle indicate that the proposed method enables fast, automatic, and safe optimization of controller parameters without human intervention.Comment: IEEE International Conference on Robotics and Automation, 2016. 6 pages, 4 figures. A video of the experiments can be found at http://tiny.cc/icra16_video . A Python implementation of the algorithm is available at https://github.com/befelix/SafeOp

Optimization of an Intelligent Controller for an Unmanned Underwater Vehicle

 Underwater environment poses a difficult challenge for autonomous underwater navigation. A standard problem of underwater vehicles is to maintain it position at a certain depth in order to perform desired operations. An effective controller is required for this purpose and hence the design of a depth controller for an unmanned underwater vehicle is described in this paper. The control algorithm is simulated by using the marine guidance navigation and control simulator. The project shows a radial basis function metamodel can be used to tune the scaling factors of a fuzzy logic controller. By using offline optimization approach, a comparison between genetic algorithm and metamodeling has been done to minimize the integral square error between the set point and the measured depth of the underwater vehicle. The results showed that it is possible to obtain a reasonably good error using metamodeling approach in much a shorter time compared to the genetic algorithm approach

Prototyping a new car semi-active suspension by variational feedback controller

New suspension systems electronically controlled are presented and mounted on board of a real car. The system consists of variable semi-active magneto-rheological dampers that are controlled through an electronic unit that is designed on the basis of a new optimal theoretical control, named VFC-Variational Feedback Controller. The system has been mounted on board of a BMW Series 1 car, and a set of experimental tests have been conducted in real driving conditions. The VFC reveals, because of its design strategy, to be able to enhance simultaneously both the comfort performance as well as the handling capability of the car. Preliminary comparisons with several industrially control methods adopted in the automotive field, among them skyhook and groundhook, show excellent results

Tuning of Nonlinear PID Controller for TRMS Using Evolutionary Computation Methods

In this paper, the Twin rotor MIMO system (TRMS) is tuned by Nonlinear PID controller using Evolutionary Computation methods. The proposed Nonlinear PID controller, used to tune TRMS, improves the system performance with additional degrees of freedom. Evolutionary Computation methods such as Differential Search Algorithm (DSA), real coded Genetic Algorithm (RGA) with simulated binary crossover (SBX) and Particle Swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are used to determine the optimal parameters of the proposed controller by minimizing Integral Square Error (ISE) for rotor response of TRMS. SIMULINK MATLAB software is used for simulating the system. The statistical performance of the controller is analysed among twenty independent trials by taking best, worst, mean and standard deviations of ISE. Simulation results reveal that TRMS system tuned by nonlinear PID controller using Particle Swarm optimization (PSO) is better than the other methods