1: Centrer for Développement of advanced Technologies, Algeria. 2: Polytechnic National School, Algiers, Algeria. A. REZOUG, M. HAMERLAIN, and M. TADJINE Adaptive RBFNN Type-2 Fuzzy Sliding Mode Controller for Robot Arm with Pneumatic Muscles. Presented by: M.Amar REZOUG ROBIO’2012

Pneumatic artificial muscle FESTO Muscle Pleated MuscleAir Muscle The (PAMs) are tubular rubber actuators with a special fibber arrangement. The fibres form a diamond pattern in a three-dimensional mesh structure, which allows the actuator to contract when the internal pressure of the hose is increased Metallic extremity

Advantages of Artificial Muscles – Flexibility – Compliant nature of the arm. – Security and Shepp maintenance Coste. – High power/weight ratio – …

Same Robots With Pneumatic Artifilial Muscles Robine ISAC LUCY 7- DOF Robot manipulator AIRIC cafard Robot

Applications domain Industrial service: assistance of operators. medical domain : for the fonctionnel re-education…

System strongly nonlinear; for consequence it is very difficult to modelling; in addition, the parameters of the robot is not kwon with uncertainty. Disadvantages of robots with artificial muscles

Joint 2 Joint 1 Distributors Air Source Valve Stem (1)4 FESTO fluidic muscle, (2)4 FESTO Proportional directional control valve (3)4 FESTO Pressure sensor, (4)1 FESTO High-Flow D-Series Pneumatic Filters (5) 1 FESTO High-Flow D-Series Pneumatic Regulators (6) 1 FESTO High-Flow D-Series Pneumatic Lubricators Economical (7)1 FESTO Branching module (8)1 FESTO Soft-start valve and (9)1 FESTO Distributor block The used robot

The general standard dynamic model of n-link robot manipulator is given as [4]: (1) Where is the inertial symmetric positive matrix, e.g. non-singular and bounded by, with and ……are minimum and maximum eigenvalues. is the vector of centrifugal, coriolis and friction terms. is the vector of gravity terms,, and are respectively the position, velocity and acceleration vectors, is the torque input vector, and is the unknown bounded disturbances. Robot manipulator model

Where: i=1...n is the number of joints, With : is a nonlinear continuous function with unknown uncertainty whose upper bound is known as Is a nonlinear function with lower bound as: is the bound disturbance, with is the control law. System transformation frome MIMO system to n-SISO Systems (2)

With: Sliding mode control (SMC) design for n-SISO Systems (3) Sliding surface The sliding mode Control of the system 2: = constant>0 [8].,k i > 0 and (4) (5) (6)

The synthesis of the equivalent control part is very delicate and the realized performances using that are depend to the exactitude of the robot model. For our case the equivalent control Eq. (5) contains uncalculated uncertainty. Solution : In order to solve this problem, our choice is ported to use the Radial Basis Function (RBF) neural network to approximate the equivalent control. Problems in SMC

(7) (8) (9) (10) (11) (12) RBF function: Output of the RBFNN: Substituting equation (9) and (10) in (2) we obtain

(13) (14) Stability analysis and RBFNN parameters tuning Lyapunov candidat function With:

(16) (17) (18) (19) (20) Adaptive paramaters ajustement Adaptive law:

Disadvantages: – Chattering can excite high frequency, non-modelled dynamics in the system and lead to degradation in performance. Solution Type-2 fuzzy logic

Theoretically, an interval type-2 fuzzy set (IT2FS) in X is characterized as : Where, denotes the union over admissible variables, is the primary variable, is the secondary variable. Type-2 fuzzy control (20)

type-2 fuzzy logic architecture: type-reducer : The type-reducer generates a type-1 fuzzy set output, which is then converted in a crisp output through the defuzzifier. This type-1 fuzzy set is also an interval set. (21)

Type-2 fuzzy Sliding surface Discontinus fuzzy Output control Table of Rules SNsZsPs k.sign(S)UnUn U z UpUp Fuzzy used controller Figure: Input and output membership functions.

Final control law and its structure Figure: Structure of the RBFT2FSMC (22)

The initial condition of position of the all joints is (0,0) degree. The robot must be arrives at the final position of 14 degree for the joint 1 and 10 degree for the joint 2. The simple time is 10 ms. The experimental are accomplished by the implementation in C language on the Pentium 4 PC. The each joint has m of langue, and robot weight is around of 15 Kg. The robot must be used at ambient operating temperature. The simulation and the experimental conditions The corresponding polynomial parameters of each joint are given by: with (23)

Robot joints identification using (PABS) Figure: Identification results

SIMULATIO RESULTS OF JOINT 1: Figure. Simulation results for the application of RBFT1FSMC and RBFT1FSMC to Joint 1.

SIMULATIO RESULTS OF JOINT 2: Figure. Simulation results for the application of RBFT1FSMC and RBFT1FSMC to Joint 2.

Method RBFT1FSMC Simulation Parameters λK 0i K Fuz_in K Fuz_out m Joint Joint MethodRBFT2FSMC Simulation Parameters λK 0i K Fuz_in K Fuz_out m Joint Joint SIMULATIO PARAMATERS:

MethodRBFT2FSMC Parameters λiK 0i K Fuz_in K Fuz_out m Joint Joint Figure. Experimental results of the application of RBFT2FSMC to all joints.

Conclusion : (1) Avoiding the modelling problem in this type of robot, (2) Attenuating the chattering effect of the SMC, (3) Reducing the rules number of the fuzzy control, (4)Guarantying the stability and the robustness of the system, (5) improuving the performences of the controled robot,...

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