In this paper, a dual mode reference management scheme for discrete-time systems with constraints in input and state is proposed. In this scheme, controllers are switched so that it achieves better performance of servo systems and the reference governor manages reference command so that the violation of constraints does not occure. A new LMI formulation to design controllers which gives rather large maximal admissible sets is proposed.
Grasp planning is a fundamental but difficult problem for multi-contacted grasp and dexterous manipulation. The problem is in general a constrained optimization with nondifferentiable complexity. Gradient or subgradient method is traditionally used for finding solution, however, it is much inefficient and time consuming. This paper presents a new application of bundle trust region method for computing the optimal hand grasp. The algorithm is computationally efficient in the sense of fast convergence and short computation time. The solution corresponds to a force-closured grasp on arbitrary shaped 3-D object with any number of contacts, and furthermore it maximizes the grasp ability to reject disturbance forces. Numerical tests validate the effectiveness of the proposal.
This paper is concerned with control of nonholonomic systems. As is well known, symmetric affine system is unable to control with continuous time invariant state feedback control. In this paper we apply PI Control to a setpoint servo problem for the symmetric affine system. PI control posesses two adjustable parameters Kp, KI-, and in addition the so-called manual reset quantity m0. (Note that adjusting m0 is equivallent to adjusting an initial condition z0 of integrator z = e.) By the PI control with the manual reset m0 appropriately chosen, not only controllable part of symmetric affine system is asymptotically stabilized but also uncontrollable part can be made to converge to the desired point. Applying the PI control with m0, we can control the symmetric affine system without transforming into the “chained form”. We confirmed the effectiveness of the proposed method by the simulation results for various plants like a two-wheeled vehicle and a four-wheeled vehicle, a flying robot, etc.
A piecewise ARX model is a typical model for identification of hybrid dynamical systems. This model consists of several ARX submodels which switch in accordance with the value of the regression vector. There have recently been reported many methods for piecewise ARX model identification based on data classification techniques. This approach first categorizes the observed data into several data sets and then estimates the parameters of the submodels and the switching hyperplanes based on the classification result. However, the result of the data classification procedure contains misclassified data in general, and they may have an adverse effect on the accuracy of the identified model. This paper presents two methods for refining the data classification by re-classifying the candidate for misclassified data based on piecewise linear separability or linear separability of the true data classification on the regression space, respectively. A numerical example also demonstrates the effectiveness of the present methods.
Fuzzy c-Regression Models (FCRM) is a Fuzzy c-Means (FCM) -type switching regression technique that simultaneously performs data clustering and local regression model estimation by using regression errors for clustering criteria. The alternating least squares method handles mixed measurement level data by iteratively quantifying nominal variables into numerical scores so that the scores suit the current model. This paper considers two algorithms for handling mixed measurement level data in FCM-type switching regression based on the alternating least squares method. The first one constructs a single numerical data space for revealing geometrical relationships among data samples while the second one quantifys nominal variables in each cluster for revealing mutual dependencies among numerical and nominal variables.