An Analytic Approach to Fuzzy Logic Robot Control Synthesis

A new analytic fuzzy logic robot control synthesis without any rule base is proposed. For this purpose the following objectives are preferred and reached: 1) an introduction of a new shape of fuzzy sets and a new distribution of input fuzzy sets; 2) a determination of an analytic activation function, instead of using of min-max operators; 3) a definition of a new analytic function that determines the position of centres of output fuzzy sets, instead of definition of rule base; and 4) an introduction of an analytic defuzzification formula. A real capability of the proposed fuzzy logic control synthesis for the RRTR-structure type robot is presented.

AN ADAPTIVE FUZZY ROBOT CONTROL WITHOUT A FUZZY RULE BASE

Evolutionary fuzzy modeling (EFM), its application in building complex nonlinear fuzzy control systems, and robustness and optimality related issues have been one of our research focus. EFM permits the integration of subjective expert rules with data driven newly discovered rules and ensures robust performance using a relatively small number of rules. Performance and robustness comparisons of fuzzy controller synthesis using manually generated rules, and EFM evolved fuzzy rules and membership function parameters have indicated the usefulness of EFM. In this study, a single axis (pitch angle) fuzzy controller is first synthesized using EFM for spacecraft attitude control. A 3-axis fuzzy controller is then built from the synthesized single axis controller. Performance comparisons using ad hoc and GA evolved control parameters are then presented. We first introduce the need for robust controller synthesis techniques such as EFM for spacecraft control. The EFM schedule and its related issues are then discussed. This is followed by details of applying EFM to space station 3-axis attitude control. The paper concludes with a discussion on the observed results and further directions. >

In this paper coordination of fuzzy power system stabilizer (FPSS) and flexible ac transmission systems (FACTS) have been considered in a multi-machine power system. The proposed model, has been applied for a wide-area power system. The proposed FPSS presented with local, nonlinear feedbacks, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). For this model, in fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Also, the parameters of FACTS controller have been evaluated by improved honey bee mating optimization (IHBMO). The effectiveness of the proposed method has been applied over two case studies of single-machine infinite-bus (SMIB) and two areas four ma chine (TAFM) Kundur’s power system. The obtained results demonstrate the superiority of proposed strategy.

In this paper, a new fuzzy controller is proposed by introducing nonlinear functions into the analytical fuzzy controller, and the methods of to on-line adjust the rules of this fuzzy controller is present. The application of this new fuzzy controller in generator excitation systems is studied. The structures of the fuzzy logic controller for generator excitation control are established, through which both the voltage regulation accuracy and enhance damping requirements are satisfied. Mathematical analysis indicates that the proposed controller is an intrinsic non-linear PID controller with adaptive rules. In the application, the engineers needn't to design the control rules, instead, they only need to choose initially control parameters according to engineering control experience. So the design and implementation of the controllers are simplified. The simulation results show that the proposed fuzzy controller can improve the accuracy of voltage control and strengthen the stability of electric power systems.

This article investigates the hierarchical synthesis of fuzzy fault-tolerant controllers for networked perturbed mechanical systems to solve the problem of finite-time multitarget surrounding, namely, encircling multiple moving targets in a finite time. Given the system with actuator failures, faults, and perturbations (namely, parametric uncertainties and external disturbances), we propose a hierarchical framework for synthesizing a cascade-form controller to stabilize the system state in a finite time, as well as decouple and solve the abovementioned problem. Specifically, the methods of fuzzy logic systems and finite-time adaptive laws are included within the synthesized controller to approximate the unknown perturbations, and meanwhile to compensate the negative effects of the actuator failures and faults. By employing the theories of perturbations and fault-tolerant analysis, we derive the sufficient conditions on control gains for finite-time convergence of the closed-loop system. Finally, we carry out several simulation experiments on a network of six 2-DOF robots and four moving targets to verify the performance of the synthesis method and the obtained controller.

This paper is concerned with the problem of designing fuzzy controllers for a class of nonlinear dynamic systems. The considered nonlinear systems are described by T-S fuzzy models with nonlinear local models, and the fuzzy models have fewer fuzzy rules than conventional T-S fuzzy models with local linear models. A new fuzzy control scheme with local nonlinear feedbacks is proposed, and the corresponding control synthesis conditions are given in terms of solutions to a set of linear matrix inequalities (LMIs). In contrast to the existing methods for fuzzy control synthesis, the new proposed control design method is based on fewer fuzzy rules and less computational burden. Moreover, the local nonlinear feedback laws in the new fuzzy controllers are also helpful in achieving good control effects. Numerical examples are given to illustrate the effectiveness of the proposed method.

This paper presents a fuzzy logic control strategy on four-wheel steering(4WS) vehicle based on a multi-body vehicle dynamic model. The multi-body vehicle dynamic model based on ADAMS can accurately predict the dynamics performance of the vehicle. Fuzzy logic is applied to track the yaw velocity of the two degrees of freedom ideal model through the co-simulation of ADAMS and Matlab Fuzzy control unit with the optimized membership function. The fuzzy control parameters are optimized and analyzed by a combined optimization algorithm (Genetic Algorithm (GA) and Nonlinear Programming Quadratic Line search (NLPQL) method) combined with response surface model (RSM). Single lane change test is chosen to validate the fuzzy control logic strategy. Simulation result shows that four-wheel steering vehicle with the fuzzy control logic strategy can improve vehicle handling stability greatly comparing with traditional front wheel steering.

With the increasing need for flexibility and adaptivity in computerized systems, the application of fuzzy expert systems is becoming increasingly commonplace in today's industry. Fuzzy logic expert systems often improve performance by allowing knowledge to generalize without requiring the knowledge engineer to anticipate all possible situations. Thus, for many types of applications, soft such as Fuzzy logic can incur lower overhead in terms of representing and engineering task knowledge. My project investigates the application of fuzzy expert systems to motion tracking. Previous research showed that Fuzzy Logic can be used to track the motion of a brightly colored object against a dark background, with relatively low development and run-time costs. The system I am developing identifies, tracks, and predicts the motion of multiple objects using unique identification patterns against a dark background. My poster will describe the fuzzy inference systems for tracking and motion prediction of such objects. An essential step to obtaining the fuzzy inputs for the motion tracking fuzzy inference system is to use convolution correlation data to obtain the centers of mass of the objects. Image processing information from the region around the center of each object provides good fuzzy inputs for recognizing object patterns and determining orientation. I am investigating two fuzzy inference systems for motion tracking and prediction in order to identify their strengths and weaknesses. In the long run, I hope to exploit the strengths of both by dividing the task between the two approaches. The Adaptive Neuro Fuzzy Inference System (ANFIS) is effective for pattern recognition. Fuzzy Proportional Derivative (FPD) is useful for computing errors and tuning inferences. Cascading the two fuzzy systems allows the system first to acquire generalized patterns and then fine-tune them for error tolerance. To perform this work, I am using the Matlab fuzzy logic toolkit, which is a simple and flexible tool for designing fuzzy inference systems.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.

This paper introduces an $H_\infty$ fuzzy control synthesis method for a nonlinear large-scale system with a reduced number of linear matrix inequalities (LMIs). It is well known that a nonlinear large-scale system can be transformed to a Takagi–Sugeno (T–S) fuzzy system by using “sector nonlinearity” or “local approximation in fuzzy partition spaces” methods. Next, in order to achieve the fuzzy control design for this T–S fuzzy system, we solve the stabilization conditions that are represented by the LMIs. However, if the number of LMIs is large, the control design process may become very complicated. In this study, based on the Lyapunov method and S -procedure, several theorems are proposed for the synthesis of parallel distributed compensation (PDC)-type fuzzy control such that the nonlinear large-scale system achieves $H_\infty$ control performance, and the number of LMIs to be solved is reduced explicitly. As a result, the control design process will become much easier. Furthermore, if the modeling error between the nonlinear system and T–S fuzzy system exists, the robust $H_\infty$ control performance and the number reduction of LMIs are also achieved by the proposed theorem. Several examples are presented in this paper to show the number reduction effect of LMIs and the effectiveness of the proposed controller synthesis.

Genetic synthesis of fuzzy logic controllers in turning

Fuzzy logic control based on the Takagi–Sugeno inference method has been applied for the yeast alcoholic fermentation running in a continuous-time biochemical reactor in this paper. A type-1 fuzzy PID controller was designed to temperature control in a biochemical reactor. The fuzzy PID controller was also designed using type-2 fuzzy sets. The advantage of the fuzzy control is that it can be used very successfully for control of strongly non-linear processes and processes that are difficult to model because of complicated reaction kinetics. Obtained simulation results confirm this fact. The disadvantage of the fuzzy control design lies in the time-consuming tuning of controllers. The subtractive clustering method was used to identify the rule base. This approach was chosen to minimize the number of rules of the designed fuzzy logic controllers and to simplify the fuzzy controller design. Simulation results confirm that fuzzy PID controllers can assure better performance than conventional PID controllers. Yeast alcoholic fermentation is an exothermic process and the cooling is necessary to maintain the optimal temperature in the reactor. Fuzzy PID control reduces coolant consumption in comparison with conventional PID control and so it assures energy-efficient control of continuous-time alcoholic fermentation. Using type-2 fuzzy sets in the fuzzy PID controller design is very promising as the type-2 fuzzy controller offers even better results than the type-1 fuzzy controller.

A well known method to control dynamic systems is the dynamic model inversion technique. Compensators obtained by this method may be used as feedforward controllers in the open-loop or closed-loop schemes. This technique has been widely used in the control of robotics arms. The method is conceptually simple but often requires a large amount of computation. In order to overcome this problem, a new method to design a fuzzy logic feedforward controller is presented in this paper. When designing a fuzzy logic controller, most of the time is spent in developing the fuzzy rules base to describe how the fuzzy controller should respond to the various inputs. Obtaining the rule base is usually a trial and error process. A starting set of rules are obtained and tested. The results are studied and the rules are then adjusted. The process is repeated until the desired results are achieved. An automated process would greatly simplify the design process of a fuzzy logic controller. In this paper, a two-level learning algorithm is used to obtain the inverse dynamics of a system. Changes to the learning algorithm that improves the performance of the existing algorithm are also presented. The learning algorithm allows the rules to be obtained by processing data collected from the system. The inverse dynamics are then implemented as a feedforward controller. To demonstrate the effectiveness of the learning algorithm, simulation results are presented.

An interval type-2 fuzzy logic system (IT2FLS) can function well with uncertain data, with which a type-1 fuzzy logic system (T1FLS) is ineffective because its membership function rests upon crisp values. However, similar to T1FLSs, there are challenges associated with IT2FLSs in selecting parameters, which can significantly affect the accuracy of the classification results with their relatively high sensitivity. This paper discusses and proposes a hybrid model based on IT2FLS and particle swarm optimization (PSO) for prediction problems. The main objective of this paper is to find the optimal solution for the unknown fuzzy systems using labelled data for training the fuzzy system. The PSO technique was used to find the optimal parameters of the Gaussian membership functions which utilized for IT2FLSs. The authors tested two data sets for each of the two prediction problems, namely: burnt forest area prediction and wine quality prediction. The predictive results were compared with other predictive methods including random forest (RF), support vector machines (SVM), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and IT2FLS with parameters generated by using the fuzzy c-means algorithm (IT2FLS-FCM). Experiment results showed that the proposed method could significantly improve accuracy compared to several other predictive techniques.