Most of the signal processing that we will study in this course involves local operations on a signal, namely transforming the signal by applying linear combinations of values in the neighborhood of each sample point. You are familiar with such operations from Calculus, namely, taking derivatives and you are also familiar with this from optics namely blurring a signal. We will be looking at sampled signals only. Let's start with a few basic examples. Local difference Suppose we have a 1D image and we take the local difference of intensities, DI(x) = 1 2 (I(x + 1) − I(x − 1)) which give a discrete approximation to a partial derivative. (We compute this for each x in the image.) What is the effect of such a transformation? One key idea is that such a derivative would be useful for marking positions where the intensity changes. Such a change is called an edge. It is important to detect edges in images because they often mark locations at which object properties change. These can include changes in illumination along a surface due to a shadow boundary, or a material (pigment) change, or a change in depth as when one object ends and another begins. The computational problem of finding intensity edges in images is called edge detection. We could look for positions at which DI(x) has a large negative or positive value. Large positive values indicate an edge that goes from low to high intensity, and large negative values indicate an edge that goes from high to low intensity. Example Suppose the image consists of a single (slightly sloped) edge:

http://ieeexplore.ieee.org/iel5/5/31307/01456462.pdf

Linear systems

Evolution of Design Method Against Random Waves Statistical Properties and Spectral of Sea Waves Transformation and Deformation of Random Sea Waves Design of Breakwaters Design of Coastal Dikes and Seawalls Probabilistic Design of Harbor Facilities Harbor Tranquility and Vessel Mooring Hydraulic Model Tests with Random Waves Theoretical Description of Random Sea Waves Statistical Theory of Irregular Waves Techniques of Random Wave Analysis 2D Computation of Wave Transformation with Random Breaking and Nearshore Currents Statistical Analysis of Extreme Waves Prediction and Control of Beach Deformation Processes.

Random seas and design of maritime structures

This paper proposes an innovative tuning approach for fuzzy control systems (CSs) with a reduced parametric sensitivity using the Grey Wolf Optimizer (GWO) algorithm. The CSs consist of servo system processes controlled by Takagi–Sugeno–Kang proportional-integral fuzzy controllers (TSK PI-FCs). The process models have second-order dynamics with an integral component, variable parameters, a saturation, and dead-zone static nonlinearity. The sensitivity analysis employs output sensitivity functions of the sensitivity models defined with respect to the parametric variations of the processes. The GWO algorithm is used in solving the optimization problems, where the objective functions include the output sensitivity functions. GWO's motivation is based on its low-computational cost. The tuning approach is validated in an experimental case study of a position control for a laboratory nonlinear servo system, and TSK PI-FCs with a reduced process small time constant sensitivity are offered.

Grey Wolf Optimizer Algorithm-Based Tuning of Fuzzy Control Systems With Reduced Parametric Sensitivity

Pressure-swing distillation (PSD) is widely used as an efficient method for separating pressure-sensitive azeotropic mixtures in industrial processes. Remarkably, PSD can achieve pure products without introducing a third component compared with extractive distillation and azeotropic distillation. Heat integration into PSD can save energy and reduce operating costs, thus relieving the continuous growth of energy consumption in the distillation industry. This review paper describes the development of this widely used distillation technique, including all of the main aspects related to thermodynamic analysis, Quantitative structure property relationship (QSPR), process design, process intensification, and dynamic control. Based on the foundation of research, further development of PSD is proposed for separating multi-component azeotropic mixtures and exploring the process design and dynamic control from QSPR, aiming at promoting the industrial application of this environmentally friendly and well-known separation technique from multi-scale analysis.

Insight into pressure-swing distillation from azeotropic phenomenon to dynamic control

1.Introduction 2.Fundamental Modeling and Control of Unmanned Small-Scale and Mi-niature Helicopters 3.Autonomous Control of a Mini Quadrotor Vehicle Using LQG Controllers 4.Modeling and Control of an Autonomous Quad-Tilt-Wing (QTW) UAV 5.Linearlization and Identification of Helicopter Model for Hierarchical Control Design 6.Analysis of the Autorotation Maneuver in Small-Scale Helicopters and Application for Emergency Landing 7.Autonomous Acrobatic Flight based on Feedforward Sequence Control for Small Unmanned Helicopter 8.Mathematical Modeling and Nonlinear Control of VTOL Aerial Vehicles 9.Formation Flight Control of Multiple Autonomous Helicopters Using Predictive Control 10.Guidance and Navigation Systems for Small Aerial Robots 11.Design and Implementation of a Low-Cost Attitude Quaternion Sensor 12.Vision-Based Navigation and Visual Servoing of Mini Flying Machines 13.Autonomous Indoor Flight and Precise Auto-Landing Using Infrared and Ultrasonic Sensors

Autonomous flying robots : unmanned aerial vehicles and micro aerial vehicles

PID-type fuzzy logic controller tuning based on particle swarm optimization

This paper try to give a new stabilization condition of continuous Takagi-Sugeno fuzzy models. Using non-quadratic Lyapunov function, the new condition of stabilization are used in terms of linear matrix inequalities LMIs. To verify the robustness of this new condition, a numeric example is used.

/pdf/new-condition-of-stabilization-for-continuous-takagi-sugeno-4ocpi9b5c9.pdf

New condition of stabilization for continuous Takagi-Sugeno fuzzy system based on fuzzy Lyapunov function

In this paper, a new robust fixed-structure ℋ∞ controller design based on the Particle Swarm Optimization (PSO) technique is proposed The optimization-based structured synthesis problem is formulated and solved by a constrained PSO algorithm In the proposed approach, the ℋ∞ controller’s structure is selectable PI and PID controller structures are especially adopted The case study of an electrical DC drive benchmark is adopted to illustrate the efficiency and viability of the proposed control approach A comparison to another similar evolutionary algorithm, such as Genetic Algorithm Optimization (GAO), shows the superiority of the PSO-based method to solve the formulated optimization problem Simulations and experimental results show the advantages of simple structure, lower order and robustness of the proposed controller

Particle swarm optimization-based fixed-structure ℋ ∞ control design

This paper investigates an Adaptive Fuzzy Gains-Scheduling Integral Sliding Mode Controller (AFGS-ISMC) design approach to deal with the attitude and altitude stabilization problem of an Unmanned Aerial Vehicles (UAV) precisely of a quadrotor. The Integral Sliding Mode Control (ISMC) seems to be an adequate control tool to remedy this problem. The selection of the controller parameters is done most of the time using repetitive trials-errors based methods. This method is not completely reliable and becomes a time-consuming and difficult task. Here we propose the tuning and selection of all ISMC gains adaptively according to a fuzzy supervisor. The sliding surface and its differential are declared as Fuzzy Logic Supervisor (FLS) inputs and the integral sliding mode control gains as the FLS outputs. The proposed fuzzy-based supervision mechanisms modify all ISMC gains to be time-varying and further enhance the performance and robustness of the obtained adaptive nonlinear controllers against uncertainties and external disturbances. The proposed adaptive fuzzy technique increases the effectiveness of the ISMC structure compared to the classical SMC strategy and excludes the dull and repetitive trials-errors process for its design and tuning. Various simulations have been carried out and followed by comparison and discussion of the results in order to prove the superiority of the suggested fuzzy gains-scheduled ISMC approach for the quadrotor attitude and altitude flight stabilization.

/pdf/fuzzy-gains-scheduling-of-an-integral-sliding-mode-p3ssj7402t.pdf

Fuzzy Gains-Scheduling of an Integral Sliding Mode Controller for a Quadrotor Unmanned Aerial Vehicle

The latest forecasts on the upcoming effects of climate change are leading to a change in the worldwide power production model, with governments promoting clean and renewable energies, as is the case of tidal energy. Nevertheless, it is still necessary to improve the efficiency and lower the costs of the involved processes in order to achieve a Levelized Cost of Energy (LCoE) that allows these devices to be commercially competitive. In this context, this paper presents a novel complementary control strategy aimed to maximize the output power of a Tidal Stream Turbine (TST) composed of a hydrodynamic turbine, a Doubly-Fed Induction Generator (DFIG) and a back-to-back power converter. In particular, a global control scheme that supervises the switching between the two operation modes is developed and implemented. When the tidal speed is low enough, the plant operates in variable speed mode, where the system is regulated so that the turbo-generator module works in maximum power extraction mode for each given tidal velocity. For this purpose, the proposed back-to-back converter makes use of the field-oriented control in both the rotor side and grid side converters, so that a maximum power point tracking-based rotational speed control is applied in the Rotor Side Converter (RSC) to obtain the maximum power output. Analogously, when the system operates in power limitation mode, a pitch angle control is used to limit the power captured in the case of high tidal speeds. Both control schemes are then coordinated within a novel complementary control strategy. The results show an excellent performance of the system, affording maximum power extraction regardless of the tidal stream input.

https://www.mdpi.com/1996-1073/10/7/862/pdf

Joseph Haggège

Papers

PID-type fuzzy logic controller tuning based on particle swarm optimization

New condition of stabilization for continuous Takagi-Sugeno fuzzy system based on fuzzy Lyapunov function

Particle swarm optimization-based fixed-structure ℋ ∞ control design

Fuzzy Gains-Scheduling of an Integral Sliding Mode Controller for a Quadrotor Unmanned Aerial Vehicle

Complementary Power Control for Doubly Fed Induction Generator-Based Tidal Stream Turbine Generation Plants