Controllers for Marine Engineering Systems - Essay Example

Distinguish Between PID, Robust and Optimal Controllers for Marine Engineering Systems. Additionally, Discuss Situations Where One Type of Controller May be More Appropriate Than the Other The technology of process instrumentation and control undergoes rapid changes in its application over the years. This is more obvious in the present age of information technology and computer science. Computer-based control and instrumentation systems are highly reliable, accurate and precise. We could see this process control applications in almost all engineering products including car steering system and ship autopilot, and in space control system as well. Automatic control systems were originated two thousand years back. The first feedback control system was the ancient water clock of Ktesibios in Alexandria in third century B.C. Time was calculated with that device by regulating the water level in a vessel and ascertaining the quantum of its flow out of it. (Australian Maritime College, department of maritime time engineering lecture notes on instrumentation and process control. (http://academic.amc.edu.au/hnguyen/IPC/IPC01-06.pdf, viewed on 23rd August , 2007) Development of instrumentation and control systems were very slowly till the Second World War. It progressed and accelerated with tremendous speed after the war. Initial progress was in single loop control systems. These contain a single feedback channel and this technology was elaborated since then for acquiring multi-loop systems 1 that contain two or more feedback modes. (http://academic.amc.edu.au/hnguyen/IPC/IPC01-06.pdf, page.1, viewed on 23rd August 2007). In recent years marine vehicles are designed in swift progression. There are unmanned underwater vehicles, surface ships and high-speed crafts with integrated bridges. These marine vehicles and their engines are controlled with the aid of computer science. But designing and producing a computer-based automatic control system were challenging in marine control engineering till recently. Several types of control are used now. They are control applications in marine and offshore systems like CAMS (Control Applications in Marine Systems) and MCMC (Maneuvering and Control of Marine Crafts), maneuvering, control and ship positioning systems, robust and reliable control systems, optimization methods in marine systems and modeling, underwater vehicles and robotics, offshore systems, traffic guidance and control systems, fault tolerant control, detection and isolation in marine systems, engine and machinery control systems, machinery surveillance, condition monitoring and quality control systems, networking and IT for marine control. (http://academic.amc.edu.au/hnguyen/IPC/IPC01-06.pdf, page 1, viewed on 23rd August 2007). The history of automatic process control reveals that the PID controller heralded all the mechanical devices used in the marine engineering. Earlier these mechanical controllers used a lever, spring and a mass. Compressed air activated the system and 2 such pneumatic controllers were treated as the industry standard. Times have changed then and likewise the techniques. Proportional Integral Derivative, abbreviated to PID controller is a generic control loop feedback mechanism. It is extensively applied in industrial control systems. This controller rectifies errors occurred between a measured process variable and a set-point by calculating and outputting corrective action which could adjust the process. (http://en.wikipedia.org/wiki/PID_controller, viewed on 23rd August, 2007). The calculation is based on three elements viz., the Proportional, the Integral and Derivative values. The first one considers the reaction to the present error, while the second regulates the reaction on the sum of recent errors and the third determines the reaction to the rate of error change. The sum of these three is taken as output to a control element. By adjusting the proportional, the integral and the derivative values, the PID can give control action for specific requirements. The effectiveness of the controller is ascertained as the viability to respond to an error and stage to which the controller overshoots the set-point and the degree of system oscillation. An improved PID tuning method can be used for marine diesel engine governors that face problems under extreme propeller load fluctuation because of changes in weather or sea conditions. A low level second-order transfer function of the marine propulsion plant is used for PI and PID speed regulators for loop shaping requirements. PID controllers for full loaded main engine speed regulations are used in propulsion power plants of big containership. (http://cat.inist.fr/aModele=afficheN&cpsidt=16296250, viewed on 22nd August 2007) 3 However, in some applications PID controllers will not bring in the required output. For instance, if PID controller is used alone, the performance will be poor when the PID loop gains are reduced in order to thwart the overshooting of the control system. The performance can be made up by combining the functions of PID controller with feed-forward control output. For an increased system performance the data derived from the system state can be added to the PID output. In motion control systems for accelerating a mechanical load under control, more torque is applied from the prime mover. When the load is accelerated a corresponding torque is applied from the prime mover without bothering about the feedback value. In this circumstance the PID loop considers the feedback data to increase or decrease the combined output for lowering difference between the process setpoint and the feedback value. The characteristic such as being linear is another drawback of a PID controller. Its performance in non-linear systems is variable. Now-a-days PID controllers are applied in the softwares in programmable logic controllers (PLCs). Software applications are more beneficial being cost effective, flexible and reliable with regard to PID algorithm. Nevertheless, taking account of their long history, simplicity, well established theory and simple setup and maintenance requirements, PID controllers are still the sought after controller for most of the applications. Modern industrial facilities do not go for tune loops using the manual calculation. Instead, PID tuning and loop optimization software are used to obtain stable results. 4 These software packages gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes. For example, in most motion control systems, in order to accelerate a mechanical load under control, more force or torque is required from the prime mover, motor or actuator. If a velocity loop PID controller is being used to control the speed of the load and command the force or torque being applied by the prime mover, then it is beneficial to take the sudden acceleration desired for the load and measure that value accurately and add it to the output of the PID velocity loop controller. This means whenever the load is accelerated a proportional force is issued from the prime mover regardless of the feedback value. The PID loop will then use the feedback data to apply an increase or decrease of the combined output for reducing the resultant difference between the process setpoint and the feedback value. The combined feed-forward open loop controller and closed loop PID controller can provide a responsive, stable and sensitive control systems. Robust control theory is used to measure the changes of a control system with the system parameters. This technique is adopted to make effective embedded systems. It is beneficial in the case of finding an alternative to changes in the system. It is stable and user friendly. (http://www.ece.cmu.edu/koopman/des_s99/control_theory/#robust, viewed on 23rd August, 2007) Based on the information of plant uncertainties, disturbances, and control-input constraints, the robust control problem can be formulated as a p-synthesis problem with three perturbation blocks. 5 A robust controller can be distinguished by its properties failing to change much if applied to a system other than the mathematical one used for its synthesis. In other words robust controller is the control of unknown plants with unknown dynamics under unknown disturbances. (http://www.ece.cmu.edu/koopman/des_s99/control_theory/#robust, viewed on 23rd August 2007) The major flaw of robust control system is its uncertainty. When robust controller is subjected to this uncertainty the control can give control system requirements. As such robust control theory is treated as a negative one instead of categorizing it as a typical base method. Some of its performance will be lost while meeting the required result. This theory is executed when there is a ship control problem, whereby the plant uncertainties take form due to the changes in speed of the ship and external disturbances. Velocity gain scheduling is used in this case of uncertainties for the variations in the ship model. (http://ieeexplore.ieee.org/xpl/freeabs_all.jsparnumber=831244, viewed on 23rd August 2007). The Robust Control Toolbox identifies lower level performance and formulate controllers of less sensitivity to parameter variations and errors in the models. A controller is said to be robust if it keeps the desired performance without considering plant-modeling errors. It contains algorithms for quantifying the uncertainty in the model and will analyze its effect on control system performance. It will also find robust controllers with reliable performance standard on the real plant, thereby reducing the sophistication of plant and controller models. 6 The Robust Control Toolbox offers a systematic approach in the robust designing and maintaining of variable feedback control systems. It consists of modeling and quantifying plant uncertainty, analysis of robustness and controller and plant model reduction. A robust controller is conceived to track a desired angle trajectory in spite of an unknown cross flow velocity. When the cross flow velocity and link length are aware an adaptive controller can be made for asymptotic tracking. For the drag loading an approximate linear parameterization is used. Also the numerical simulations used determine the effectiveness of the designed controllers for the desired trajectories. (http://ieeexplore.ieee.org/xpl/freeabs_all.jsparnumber=980684, viewed on 23rd August, 2007). The important features of a ship control system are maneuvering, robustness to plant uncertainties, environmental disturbances, and fuel saving for steering. In the last several decades PID and LQ optimal controllers were used in ship control. However, the robustness issues caused by ship model uncertainties and wave turbulence have not been subjected to proper attention. Here, we can investigate ship dynamics uncertainties and wind-generated wave disturbances and build uncertainty models for them. Based on these models a robust control problem is formulated to face the robustness issues. The latest control techniques have optimized the control systems. But optimal control algorithms are not always viable to changes in the control system. A nonlinear robust control method applying neural networks is shaped for stabilizing ship roll. In order to reduce the conservation of control law restoring forces 7 and damping are applied. As stability analysis is conceived on the Barbashin and Krasovkii theorem, mean valued theorem and Lyapunov stability theory, the relevance of the off-line training is discarded. (http://manuales.elo.utfsm.cl/conferences/cdc/cdc99/pdffiles/papers/0395.pdf, viewed on 23rd August, 2007) Optimal control is used for getting a control law to a given system so that the required optimality criterion is determined. An optimal control can be identified as a group of differential equations detailing the control variable approach that lessen the cost functional. The optimal control is obtained by using Pontryagin's maximum principle or by evaluating the Hamilton-Jacobi-Bellman equation. (http://en.wikipedia.org/wiki/Optimal_control, viewed on 23rd August, 2007) Cost functional control is a mathematical expression which gives the travel time as a function of the speed, geometrical considerations etc. of the system. The problem with optimal control is that it needs some means to minimize the fuel consumption so as to complete a given action within the required time frame and the same time not exceeding the given amount limit. Optimal control has another problem which is reducing the total monetary cost of completing the work within the assumed monetary value for time and fuel. In our modern times optimal control solutions are executed digitally. The classical controllers were effective when we consider optimal controllers. For the evaluation of the control methods for reducing the resistance of a ship, now, digital 8 simulation techniques are used. (http://www.ifremer.fr/avano/notices/00022/810.htm, viewed on 23rd August, 2007) Instrumentation and control systems were developed slowly till the Second World War. The development progressed and accelerated with tremendous speed. Initial progress was in single loop control systems. These contain a single feedback channel and this technology was elaborated since then for acquiring multi-loop systems that contain two or more feedback modes. (http://academic.amc.edu.au/hnguyen/IPC/IPC01-06.pdf, viewed on 23rd August, 2007) 9 REFERENCE 1. Internet sources :(http://academic.amc.edu.au/hnguyen/IPC/IPC01-06.pdf, page.1, viewed on 23rd August 2007). 2. (http://en.wikipedia.org/wiki/PID_controller, viewed on 23rd August, 2007). 3. (http://cat.inist.fr/aModele=afficheN&cpsidt=16296250, viewed on 22nd August 2007) 4. (http://www.ece.cmu.edu/koopman/des_s99/control_theory/#robust, viewed on 23rd August, 2007) 5. (http://ieeexplore.ieee.org/xpl/freeabs_all.jsparnumber=831244, viewed on 23rd August 2007). 6. (http://ieeexplore.ieee.org/xpl/freeabs_all.jsparnumber=980684, viewed on 23rd August, 2007). 7. (http://manuales.elo.utfsm.cl/conferences/cdc/cdc99/pdffiles/papers/0395.pdf, viewed on 23rd August, 2007) 8. (http://en.wikipedia.org/wiki/Optimal_control, viewed on 23rd August, 2007) 9. http://www.ifremer.fr/avano/notices/00022/810.htm, viewed on 23rd August, 2007) Read More