Stabilization of Rössler chaotic dynamical system using fuzzy logic control algorithm

This paper proposes a fuzzy logic control algorithm (FLCA) to stabilize the Rössler chaotic dynamical system. The fuzzy logic control system is based on a Takagi-Sugeno-Kang inference engine and the stability analysis in the sense of Lyapunov is carried out using Lyapunov’s direct method. The new FLCA is formulated to offer sufficient inequality stability conditions. The asymptotic complexity of our algorithm is analyzed and proved to be lower in comparison with that of linear matrix inequality-based FLCAs. A set of simulation results illustrates the effectiveness of the proposed FLCA.     

Iterative performance improvement of fuzzy control systems for three tank systems

This paper suggests the performance improvement of fuzzy control systems (FCSs) for three tank systems using iterative feedback tuning (IFT). The stable design of Takagi–Sugeno–Kang fuzzy controllers is guaranteed by means of a stability theorem based on LaSalle’s global invariant set theorem formulated for a class of multi input-multi output (MIMO) nonlinear processes. An IFT algorithm characterized by setting the step size to guarantee the FCS stability is proposed. The theoretical approaches are applied in a case study that deals with the IFT-based stable design of fuzzy controllers dedicated to the level control of a cylindrical three tank system as a representative MIMO system. A set of experimental results for a laboratory setup illustrates the performance improvement.     

Classification Using Φ-Machines and Constructive Function Approximation

This article presents a new classification algorithm, called CLEF, which induces a -machine by constructing its own features based on the training data. The features can be viewed as defining subsets of the instance space, and they allow CLEF to create useful non-linear functions over the input variables. The algorithm is guaranteed to find a classifier that separates the training instances, if such a separation is possible. We compare CLEF empirically to several other classification algorithms, including a well-known decision tree inducer, an artificial neural network inducer, and a support vector machine inducer. Our results show that the CLEF-induced -machines and support vector machines have similar accuracy on the suite tested, and that both are significantly more accurate than the other classifiers produced. We argue that the classifiers produced by CLEF are easy to interpret, and hence may be preferred over support vector machines in certain circumstances.     

Fuzzy Controllers With Maximum Sensitivity for Servosystems

In this paper, new Takagi-Sugeno proportional-integral-fuzzy controllers (PI-FCs) to control a class of servosystems are proposed. The controlled plants in these control systems (CSs) are of integral type. In the first phase, there are designed linear PI controllers tuned in terms of the extended symmetrical optimum method to ensure the imposed overshoot and settling time with respect to the set point and to three possible types of load disturbance inputs. The connections between the two design parameters of the linear controllers and the desired maximum sensitivity and complementary sensitivity considering one of the disturbance inputs are derived. Then, accepting the approximate equivalence between the fuzzy controllers and the linear ones in certain conditions and using the modal equivalence principle, an attractive design method for the PI-FCs is proposed. With this respect, the design method guarantees maximum imposed sensitivity and complementary sensitivity for the CSs and, therefore, good responses with respect to modifications of the set point and of the disturbance inputs, and robustness with respect to model uncertainties. An application in speed control of a nonlinear servosystem with variable load, accompanied by experimental results, is provided to validate the new results, the fuzzy controllers, and a design method     

Design and Experiments for a Class of Fuzzy Controlled Servo Systems

This paper proposes a new fuzzy control solution employing 2-DOF proportional-integral-fuzzy controllers dedicated to a class of servo systems. The controlled plants in these systems, widely used in mechatronics applications, can be characterized by second-order dynamics with integral type. The original design method suggested here starts with linear design results in terms of the extended symmetrical optimum method accompanied by an iterative feedback tuning (IFT) algorithm. Next, these results are transferred to the fuzzy case by the modal equivalence principle. The convergence of the IFT algorithm is guaranteed by the derivation of sufficient global asymptotic stability conditions based on Krasovskii-LaSalle's invariant set theorem with quadratic Lyapunov function candidate. Real-time experimental results corresponding to a low-cost fuzzy controlled servo system validate the theoretical approaches.     

Anytime similarity measures for faster alignment

Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often optimizing a similarity measure between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one could use only a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. These kinds of tradeoffs between the amount of computation and the accuracy of the result have been addressed in the field of real-time artificial intelligence as deliberation control problems. We propose that the optimization of a similarity measure is a natural application domain for deliberation control using the anytime algorithm framework. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.     

On the design of an obstacle avoiding trajectory: Method and simulation

The paper suggests a new mathematical construction for the potential field used in the design of obstacle avoiding trajectories. The main benefits of the proposed construction are the quickness of minimum computation and the compensation for the main drawbacks specific to the “traditional approaches” belonging to the potential field method in general. The potential field definition and its minimum computation concept are presented. Next the concept is included in a design method for obstacle avoiding trajectories. The method is expressed in the form of an algorithm for obstacle avoidance. In the following step a state-space controller is designed in order to control the car along that trajectory. Digital simulation results obtained for the complete dynamic model of a car well validate the method.     

Multiple periodic solutions with prescribed minimal period to second-order Hamiltonian systems

A new variational method based on Ekeland’s principle is introduced for the existence, localization and multiplicity of periodic solutions of a prescribed minimal period to second-order Hamiltonian systems. The oscillatory property at zero or infinity of only one component of the gradient of the potential function is sufficient for the existence of infinitely many solutions. Also, oscillating properties of several components of the gradient of the potential function yield sequences of solutions with some of the components tending in norm to zero and others to infinity.     

Linear and fuzzy control solutions for tape drives

This paper suggests new linear and fuzzy control solutions for tape drive-based transport systems. The linear control solution involves a cascade control system with inner state-feedback controller and outer PI controllers. The fuzzy control solution employs a Takagi–Sugeno fuzzy controller to schedule 11 separately designed linear controllers depending on measured take-up reel radius. Two simulation scenarios illustrate the control system performance enhancement ensured by the fuzzy control solution.