03_fc_tanker.rar_ship_ship control_ship fuzzy

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Fuzzy Control System for a Tanker Ship %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % By: Kevin Passino % Version: 4/15/99 % % Notes: This program has evolved over time and uses programming % ideas of Andrew Kwong, Scott Brown, and Brian Klinehoffer. % % This program simulates a fuzzy control system for a tanker % ship. It has a fuzzy controller with two inputs, the error % in the ship heading (e) and the change in that error (c). The output % of the fuzzy controller is the rudder input (delta). We want the % tanker ship heading (psi) to track the reference input heading % (psi_r). We simulate the tanker as a continuous time system % that is controlled by a fuzzy controller that is implemented on % a digital computer with a sampling interval of T. % % This program can be used to illustrate: % - How to code a fuzzy controller (for two inputs and one output, % illustrating some approaches to simplify the computations, for % triangular membership functions, and either center-of-gravity or % center-average defuzzification). % - How to tune the input and output gains of a fuzzy controller. % - How changes in plant conditions ("ballast" and "full") % can affect performance. % - How sensor noise (heading sensor noise), plant disturbances % (wind hitting the side of the ship), and plant operating % conditions (ship speed) can affect performance. % - How improper choice of the scaling gains can result in % oscillations (limit cycles). % - How an improper choice of the scaling gains (or rule base) can % result in an unstable system. % - The shape of the nonlinearity implemented by the fuzzy controller % by plotting the input-output map of the fuzzy controller. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear % Clear all variables in memory % Initialize ship parameters % (can test two conditions, "ballast" or "full"): ell=350; % Length of the ship (in meters) u=5; % Nominal speed (in meters/sec) %u=3; % A lower speed where the ship is more difficult to control abar=1; % Parameters for nonlinearity bbar=1; % The parameters for the tanker under "ballast" conditions % (a heavy ship) are: K_0=5.88; tau_10=-16.91; tau_20=0.45; tau_30=1.43; % The parameters for the tanker under "full" conditions (a ship % that weighs less than one under "ballast" conditions) are: %K_0=0.83; %tau_10=-2.88; %tau_20=0.38; %tau_30=1.07; % Some other parameters are: K=K_0*(u/ell); tau_1=tau_10*(ell/u); tau_2=tau_20*(ell/u); tau_3=tau_30*(ell/u); % Initialize parameters for the fuzzy controller nume=11; % Number of input membership functions for the e % universe of discourse (can change this but must also % change some variables below if you make such a change) numc=11; % Number of input membership functions for the c % universe of discourse (can change this but must also % change some variables below if you make such a change) % Next, we define the scaling gains for tuning membership functions for % universes of discourse for e, change in e (what we call c) and % delta. These are g1, g2, and g0, respectively % These can be tuned to try to improve the performance. % First guess: g1=1/pi;,g2=100;,g0=8*pi/18; % Chosen since: % g1: The heading error is at most 180 deg (pi rad) % g2: Just a guess - that ship heading will change at most % by 0.01 rad/sec (0.57 deg/sec) % g0: Since the rudder is constrained to move between +-80 deg % Tuning: g1=1/pi;,g2=200;,g0=8*pi/18; % Try to reduce the overshoot g1=2/pi;,g2=250;,g0=8*pi/18; % Try to speed up the response a bit but to do this % have to raise g2 a bit to avoid overshoot. Take these % as "good" tuned values. %g1=2/pi;,g2=0.000001;,g0=2000*pi/18; % Values tuned to get an oscillation (limit % cycle) for COG, ballast, % and nominal speed with no sensor % noise or rudder disturbance): % g1: Leave as before % g2: Essentially turn off the derivative gain % since this help induce an oscillation % g0: Make this big to force the limit cycle % In this case simulate for 16,000 sec. %g1=2/pi;,g2=250;,g0=-8*pi/18; % Values tuned to get an instability % g0: Make this negative so that when there % is an error the rudder will drive the % heading in the direction to increase the error % Next, define some parameters for the membership functions we=0.2*(1/g1); % we is half the width of the triangular input membership % function bases (note that if you change g0, the base width % will correspondingly change so that we always end % up with uniformly distributed input membership functions) % Note that if you change nume you will need to adjust the % "0.2" factor if you want membership functions that % overlap in the same way. wc=0.2*(1/g2); % Similar to we but for the c universe of discourse base=0.4*g0; % Base width of output membership fuctions of the fuzzy % controller % Place centers of membership functions of the fuzzy controller: % Centers of input membership functions for the e universe of % discourse of fuzzy controller (a vector of centers) ce=[-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1]*(1/g1); % Centers of input membership functions for the c universe of % discourse of fuzzy controller (a vector of centers) cc=[-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1]*(1/g2); % This next matrix specifies the rules of the fuzzy controller. % The entries are the centers of the output membership functions. % This choice represents just one guess on how to synthesize % the fuzzy controller. Notice the regularity % of the pattern of rules. Notice that it is scaled by g0, the % output scaling factor, since it is a normalized rule base. % The rule base can be tuned to try to improve performance. rules=[1 1 1 1 1 1 0.8 0.6 0.3 0.1 0; 1 1 1 1 1 0.8 0.6 0.3 0.1 0 -0.1; 1 1 1 1 0.8 0.6 0.3 0.1 0 -0.1 -0.3; 1 1 1 0.8 0.6 0.3 0.1 0 -0.1 -0.3 -0.6; 1 1 0.8 0.6 0.3 0.1 0 -0.1 -0.3 -0.6 -0.8; 1 0.8 0.6 0.3 0.1 0 -0.1 -0.3 -0.6 -0.8 -1; 0.8 0.6 0.3 0.1 0 -0.1 -0.3 -0.6 -0.8 -1 -1; 0.6 0.3 0.1 0 -0.1 -0.3 -0.6 -0.8 -1 -1 -1; 0.3 0.1 0 -0.1 -0.3 -0.6 -0.8 -1 -1 -1 -1; 0.1 0 -0.1 -0.3 -0.6 -0.8 -1 -1 -1 -1 -1; 0 -0.1 -0.3 -0.6 -0.8 -1 -1 -1 -1 -1 -1]*g0; % Now, you can proceed to do the simulation or simply view the nonlinear % surface generated by the fuzzy controller. flag1=input('\n Do you want to simulate the \n fuzzy control system \n for the tanker? \n (type 1 for yes and 0 for no) '); if flag1==1, % Next, we initialize the simulation: t=0; % Reset time to zero index=1; % This is time's index (not time, its index). tstop=4000; % Stopping time for the simulation (in seconds) step=1; % Integration step size T=10; % The controller is implemented in discrete time and % this is the sampling time for the controller. % Note that the integration step size and the sampling % time are not the same. In this way we seek to simulate % the continuous time system via the Runge-Kutta method and % the discrete time fuzzy controller as if it were % implemented by a digital computer. Hence, we sample % the plant output every T seconds and at that time % output a new value of the controller output. counter=10; % This counter will be used to count the number of integration % steps that have been taken in the current sampling interval. % Set it to 10 to begin so that it will compute a fuzzy controller % output at the first step.