The file *Demo_008.m* is found in IQClabâ€™s folder *demos*; it performs an LPV controller synthesis with the function *fLPVsyn*. The example is taken from [16]. This paper describes the application of the LPV control design algorithm to a parametrically varying flight control problem. The LPV model together with the performance objectives are found in this reference.

The equations of motion are given by:

Here:

- is the angle-of-attack (rad)
- is the tail fin deflection (rad)
- is the pitch rate (rad/s)
- is the Mach number
- is the normal acceleration per

The remaining constants are given by

The synthesis objective is to track the commanded acceleration manoeuvres , while satisfying some performance specifications. The control input is the tail fin deflection , and the measurements are the error in nominal acceleration and the pitch rate:

We choose the reference for the normal acceleration as the exogenous performance outputs as:

with the performance weight

in order to enforce disturbance rejection at low frequencies and to penalized control actuation at high frequencies.

To derive the LPV model, we conveniently choose the Mach number, , and the angle of attack, , as scheduling variables. Then it is not difficult to formulate an LFT structure of the equations of motion with normalized scheduling block

where and .

The synthesis was run with the following code:

- Length of the basis function: 3
- Solution check: ‘on’
- Enforce strictness of the LMIs:

% Load missile open-loop plant load missile % Define uncertainties del_a = ureal('del_a',0,'Range',[-1,1]); del_M = ureal('del_M',0,'Range',[-1,1]); Del_a = pi/18*(1 + del_a); Del_M = 3 + del_M; Del_T = blkdiag(Del_a*eye(2),Del_M*eye(5)); % Extract open-loop interconnection for normalized uncertainties P = lft(Del_T,ssbal(G)); [olic,De] = lftdata(P); % Append weighting functions wp = ss(tf([0.5,3.5],[1,3.5e-6])); wu = ss(20*tf([1/0.9,1],[1e-6,1])); Wo = ssbal(blkdiag(eye(7),wp,wu,eye(2))); wolic = Wo*ssbal(olic); % Define scheduling block H{1} = blkdiag(eye(5),zeros(2)); H{2} = blkdiag(zeros(5),eye(2)); La = polydec(pvec('box',[-1,1;-1,1]))'; iqcdeltaOpt.InputChannel = 1:7; iqcdeltaOpt.OutputChannel = 1:7; iqcdeltaOpt.Polytope = La; iqcdeltaOpt.UncertaintyMap = H; iqcdeltaOpt.TimeInvTimeVar = 'TV'; iqcdeltaOpt.Structure = 'FB'; Delta = iqcdelta('Delta',iqcdeltaOpt); % Define synthesis options options.RelaxType = 'CH'; options.FeasbRad = 1e8; options.constants = [1e-8,1e-7,1e-7,1e-9,1e-8,1e-8]; options.subopt = 1.1; options.bounds = [1e5,1,1e5,1]; % Perform synthesis [K,ga] = fLPVsyn(wolic,Delta,[7,2,2],[7,1,1],options);

Here

- The chosen relaxation type is convex hull (CH),
- The feasibility radius is confined to ,
- The LMI constraints are perturbed by the constants ,
- A sub-optimal solution is computed, while is relaxed with 10% and while the norms are minimized with
*options.bounds*=.

This yields the LTI part of the controller, , together with the obtained sub-optimal induced -gain, . The time-domain simulation results are shown in the following figure.