ABSTRACT
Multiple-input sliding-mode techniques are applied to a planar arm actuated by four groups of pneumatic muscle (PM) actuators in opposing pair configuration. The control objective is end-effector tracking of a desired path in Cartesian space. The inputs to the system are commanded input pressure differentials for the two opposing PM groups. An existing model for the muscle is incorporated into the arm equations of motion to arrive at a two-input, two-output nonlinear model of the planar arm that is affine in the input and, therefore, suitable for sliding-mode techniques. Relationships between static input pressures are derived for suitable arm behavior in the absence of a control signal. Simulation studies are reported.
Subject(s)
Arm/physiology , Artificial Limbs , Biomimetic Materials , Models, Biological , Muscle, Skeletal/physiology , Orthotic Devices , Robotics/methods , Computer Simulation , Computer-Aided Design , Elbow Joint/physiology , Humans , Muscle Contraction/physiology , Postural Balance/physiology , Pressure , Prosthesis Design , Rheology/instrumentation , Rheology/methods , Shoulder Joint/physiologyABSTRACT
Genetic algorithms show powerful capabilities for automatically designing fuzzy systems from data, but many proposed methods must be subjected to some minimal structure assumptions, such as rule base size. In this paper, we also address the design of fuzzy systems from data. A new evolutionary approach is proposed for deriving a compact fuzzy classification system directly from data without any a priori knowledge or assumptions on the distribution of the data. At the beginning of the algorithm, the fuzzy classifier is empty with no rules in the rule base and no membership functions assigned to fuzzy variables. Then, rules and membership functions are automatically created and optimized in an evolutionary process. To accomplish this, parameters of the variable input spread inference training (VISIT) algorithm are used to code fuzzy systems on the training data set. Therefore, we can derive each individual fuzzy system via the VISIT algorithm, and then search the best one via genetic operations. To evaluate the fuzzy classifier, a fuzzy expert system acts as the fitness function. This fuzzy expert system can effectively evaluate the accuracy and compactness at the same time. In the application section, we consider four benchmark classification problems: the iris data, wine data, Wisconsin breast cancer data, and Pima Indian diabetes data. Comparisons of our method with others in the literature show the effectiveness of the proposed method.
Subject(s)
Artificial Intelligence , Breast Neoplasms/diagnosis , Diabetes Mellitus/diagnosis , Fuzzy Logic , Iris/anatomy & histology , Pattern Recognition, Automated , Wine/classification , Algorithms , Computer Simulation , Data Interpretation, Statistical , Diagnosis, Computer-Assisted/methods , Expert Systems , Feedback , HumansABSTRACT
Adaptive tracking techniques are applied to pneumatic muscle actuators arranged in bicep and tricep configurations. The control objective is to force the joint angle to track a specified reference path. Mathematical models are derived for the bicep and tricep configurations. The models are nonlinear and in general time-varying, making adaptive control desirable. Stability results are derived, and the results of simulation studies are presented, contrasting the nonlinear adaptive control to a nonadaptive PID control approach.
