Simulation and the monte carlo method

Robust Control Design with MATLAB (second edition) helps the student to learn how to use well-developed advanced robust control design methods in practical cases. To this end, several realistic control design examples from teaching-laboratory experiments, such as a two-wheeled, self-balancing robot, to complex systems like a flexible-link manipulator are given detailed presentation. All of these exercises are conducted using MATLAB Robust Control Toolbox 3, Control System Toolbox and Simulink. By sharing their experiences in industrial cases with minimum recourse to complicated theories and formulae, the authors convey essential ideas and useful insights into robust industrial control systems design using major H-infinity optimization and related methods allowing readers quickly to move on with their own challenges. The hands-on tutorial style of this text rests on an abundance of examples and features for the second edition: rewritten and simplified presentation of theoretical and methodological material including original coverage of linear matrix inequalities; new Part II forming a tutorial on Robust Control Toolbox 3; fresh design problems including the control of a two-rotor dynamic system; and end-of-chapter exercises. Electronic supplements to the written text that can be downloaded from extras.springer.com/isbn include: M-files developed with MATLAB help in understanding the essence of robust control system design portrayed in text-based examples; MDL-files for simulation of open- and closed-loop systems in Simulink; and a solutions manual available free of charge to those adopting Robust Control Design with MATLAB as a textbook for courses. Robust Control Design with MATLAB is for graduate students and practising engineers who want to learn how to deal with robust control design problems without spending a lot of time in researching complex theoretical developments.

Robust control design with MATLAB

fundamental concepts in the design of experiments is available in our digital library an online access to it is set as public so you can get it instantly. Our digital library saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the fundamental concepts in the design of experiments is universally compatible with any devices to read.

Fundamental Concepts In The Design Of Experiments

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Hydraulic Control Systems

The mobile inverted pendulum is developed and tested for an intelligent control experiment of control engineers. Intelligent control algorithms are tested for the control experiment of a low cost mobile inverted pendulum system. Online learning and control using neural network of a wheel-driven mobile inverted pendulum system is presented. Neural network learning algorithm is embedded on a digital signal processing board along with primary proportional-integral-differential controllers to achieve real time control. Without knowing dynamics of the system, uncertainties in system dynamics are compensated by neural network in an online fashion. Digital filters are designed for a gyro sensor to compensate for a phase lag. Experimental studies of balancing the pendulum and tracking the desired trajectory of the cart for one dimensional motion are conducted. Results show the robustness of the proposed controller even when outer impacts as disturbance are present.

Control Experiment of a Wheel-Driven Mobile Inverted Pendulum Using Neural Network

Preface. Introduction. I. FUNDAMENTALS. 1 Fluid Properties. 1.1 Introduction. 1.2 Fluid Mass Density. 1.3 Fluid Bulk Modulus. 1.4 Thermal Fluid Properties. 1.5 Fluid Viscosity. 1.6 Vapor Pressure. 1.7 Chemical Properties. 1.8 Fluid Types and Selection. 1.9 Conclusion. 1.10 References. 1.11 Homework Problems. 2 Fluid Mechanics. 2.1 Introduction. 2.2 Governing Equations. 2.3 Fluid Flow. 2.4 Pressure Losses. 2.5 Pressure Transients. 2.6 Hydraulic Energy and Power. 2.7 Lubrication Theory. 2.8 Conclusion. 2.9 References. 2.10 Homework Problems. 3 Dynamic Systems and Controls. 3.1 Introduction. 3.2 Modeling. 3.3 Linearization. 3.4 Dynamic Behavior. 3.5 State-Space Analysis. 3.6 Block Diagrams and the Laplace Transform. 3.7 Stability. 3.8 Compensation. 3.9 Conclusion. 3.10 References. 3.11 Homework Problems. II HYDRAULIC COMPONENTS. 4 Hydraulic Control Valves. 4.1 Introduction. 4.2 Valve Flow Coefficients. 4.3 Two-Way Spool Valves. 4.4 Three-Way Spool Valves. 4.5 Four-Way Spool Valves. 4.6 Poppet Valves. 4.7 Flapper Nozzle Valves. 4.8 Conclusion. 4.9 References. 4.10 Homework Problems. 5 Hydraulic Pumps. 5.1 Introduction. 5.2 Pump Efficiency. 5.3 Gear Pumps. 5.4 Axial-Piston Swash-Plate Pumps. 5.5 Conclusion. 5.6 References. 5.7 Homework Problems. 6 Hydraulic Actuators. 6.1 Introduction. 6.2 Actuator Types. 6.3 Linear Actuators. 6.4 Rotary Actuators. 6.5 Conclusion. 6.6 References. 6.7 Homework Problems. III HYDRAULIC CONTROL SYSTEMS. 7 Valve-Controlled Hydraulic Systems. 7.1 Introduction. 7.2 Four-Way Valve Control of a Linear Actuator. 7.3 Three-Way Valve Control of a Linear Actuator. 7.4 Four-Way Valve Control of a Rotary Actuator. 7.5 Conclusion. 7.6 References. 7.7 Homework Problems. 8 Pump-Controlled Hydraulic Systems. 8.1 Introduction. 8.2 Fixed-Displacement Pump Control of a Linear Actuator. 8.3 Variable-Displacement Pump Control of a Rotary Actuator. 8.4 Conclusion. 8.5 References. 8.6 Homework Problems. INDEX.

Hydraulic control systems

In this paper, the linear and nonlinear mathematical force models are presented for an end-milling process. The nonlinear force model is compared with the updated linear force model. The mechanistic identification of the cutting force coefficients for the linear and nonlinear force models of the end milling process is presented with experimental validation. The cutting force coefficients are determined for an end-milling process using two methods, the average force method and the optimization technique method. The average force method uses equations given by Altintas and Budak [1, 2, 3] for calculating the cutting coefficients of the linear force model. However, the equations for the nonlinear model are derived and used in this paper. The second method uses the optimization technique for identifying the cutting coefficients in the milling process by forming objective functions using the optimization technique to minimize the error between the experimental and the analytical forces. Moreover, this method produced a good force model that approximates the experimental force results, which compared with the average force method for both models.

Identification of cutting force coefficients for the linear and nonlinear force models in end milling process using average forces and optimization technique methods

The heavy equipment industry is building more and more equipment with electro-hydraulic control systems The existing industry practices for the design of control systems in construction machines primarily rely on classical designs coupled with ad-hoc synthesis procedures Such practices produce desirable results, but lack a systematic procedure to account for invariably present plant uncertainties in the design process as well as coupled dynamics of the multi-input multi-output (MIMO) configuration In this paper, two H(infinity) based robust control designs are presented for an automatic bucket leveling mechanism of a wheel loader In one case, the controller is designed for the base plant model In another case, the controller is designed for the plant with a feedback linearization control law applied yielding improved stability robustness A MIMO nonlinear model for an electro-hydraulically actuated wheel loader linkage is considered The robustness of the controller designs are validated by using analysis and by simulation using a complete nonlinear model of the wheel loader linkage and hydraulic system

Robust control design for a wheel loader using H∞ and feedback linearization based methods

A fluid control system may include a pump, a tank, and an actuator. A valve assembly may be configured to control fluid communication between the actuator, the tank, and the pump. An energy recovery circuit, including a pressure transformer, may be fluidly coupled to the actuator in parallel with the valve assembly.

Hydraulic control system with energy recovery

The existing industry practices for the design of control systems in construction machines primarily rely on classical designs coupled with ad-hoc synthesis procedures. Such practices lack a systematic procedure to account for invariably present plant uncertainties in the design process as well as coupled dynamics of the multi-input multi-output (MIMO) configuration. In this paper, an H/sub /spl infin// based robust control design combined with feedback linearization is presented for an automatic bucket leveling mechanism of a wheel loader. With the feedback linearization control law applied, stability robustness is improved. A MIMO nonlinear model for an electro-hydraulically actuated wheel loader is considered. The robustness of the controller designs are validated by using analysis and by simulation using a complete nonlinear model of the wheel loader system.

http://www.nt.ntnu.no/users/skoge/prost/proceedings/acc05/PDFs/Papers/0783_FrB12_5.pdf

Roger Fales

Papers

Hydraulic control systems

Identification of cutting force coefficients for the linear and nonlinear force models in end milling process using average forces and optimization technique methods

Robust control design for a wheel loader using H∞ and feedback linearization based methods

Hydraulic control system with energy recovery

Robust control design for a wheel loader using mixed sensitivity h-infinity and feedback linearization based methods