船舶航向的模糊控制matlab仿真

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Fuzzy Model Reference Learning Control (FMRLC) System for a Tanker Ship %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % % By: Kevin Passino % Version: 4/26/01 % % Notes: This program has evolved over time and uses programming % ideas of Andrew Kwong, Jeff Layne, and Brian Klinehoffer. % % This program simulates an FMRLC 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). The FMRLC % adjusts the fuzzy controller to try to get tanker ship heading (psi) % to track the output of a "reference model" (psi_m) that has as an % input the reference input heading (psi_r). We simulate the tanker % as a continuous time system that is controlled by an FMRLC that % is implemented on a digital computer with a sampling interval of T=1 sec. % % This program can be used to illustrate: % - How to code an FMRLC (for two inputs and one output, % for the controller and "fuzzy inverse model"). % - How to tune the input and output gains of an FMRLC. % - How changes in plant conditions ("ballast" and "full" and speed) % can affect performance and how the FMRLC can adapt to improve % performance when there are such changes. % - How the effects of sensor noise (heading sensor noise) and plant % disturbances (wind hitting the side of the ship) can be reduced % over the case of non-adaptive fuzzy control. % - The shape of the nonlinearity synthesized by the FMRLC % by plotting the input-output map of the fuzzy controller at the % end of the simulation (and providing the output membership function % centers). % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% clear % Clear all variables in memory % Initialize ship parameters ell=350; % Length of the ship (in meters) abar=1; % Parameters for nonlinearity bbar=1; % Define the reference model (we use a first order transfer function % k_r/(s+a_r)): a_r=1/150; k_r=1/150; % 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 % "Good" tuned values: g1=2/pi;,g2=250;,g0=8*pi/18; % 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 (basiscally we are using a type of % saturated index adding). 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. % The gain gf is a gain that can be set to one to initialize % the rule base with an initial guess at the fuzzy controller to be % synthesized or to zero to initialize the rule base to all zeros % (i.e., all centers at zero so the rules all say to put zero % into the plant). gf=1; 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]*gf*g0; % Next, we define some parameters for the fuzzy inverse model gye=2/pi;,gyc=10; % Scaling gains for the error and change in error for % the inverse model % These are tuned to improve the performance of the FMRLC gp=0.4; % Scaling gain for the output of inverse model. If you let gp=0 then % you turn off the learning mechanism and hence the FMRLC reduces to % a direct fuzzy controller that is not tuned. Hence, from this % program you can also see how to code a non-adaptive fuzzy controller. % If gp is large then generally large updates will be made to the % fuzzy controller. You should think of gp as an "adaptation gain." numye=11; % Number of input membership functions for the ye % universe of discourse numyc=11; % Number of input membership functions for the yc % universe of discourse wye=0.2*(1/gye); % Sets the width of the membership functions for % ye from center to extremes wyc=0.2*(1/gyc); % Sets the width of the membership functions for % yc from center to extremes invbase=0.4*gp; % Sets the base of the output membership functions % for the inverse model % Place centers of inverse model membership functions % For error input for learning mechanism cye=[-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1]*(1/gye); % For change in error input for learning mechanism cyc=[-1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1]*(1/gyc); % The next matrix contains the rule-base matrix for the fuzzy % inverse model. Notice that for simplicity we choose it to have % the same structure as the rule base for the fuzzy controller. % While this will work for the control of the tanker % for many nonlinear systems a different structure % will be needed for the rule base. Again, the entries are % the centers of the output membership functions, but now for % the fuzzy inverse model. inverrules=[1 1 1 1 1 1 0.8 0.6 0.4 0.2 0; 1 1 1 1 1 0.8 0.6 0.4 0.2 0 -0.2; 1 1 1 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4; 1 1 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6; 1 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8; 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1; 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0