Self-organizing fuzzy control of active suspension systems

This paper proposes a design approach for a self-organizing self-tuning fuzzy logic controller, and is applied to the design of a field oriented drive system. The basic structure of a fuzzy logic controller is outlined and the design problems associated with the conventional trial-and-error schemes are addressed. The suitability of the genetic algorithm optimization technique as a means to determine and optimize the fuzzy logic controller design is discussed. In the proposed approach normalization factors and/or membership function parameters and/or the controller policy, are translated into bit-strings. These bit-strings are processed by the genetic algorithm and if the selection process as well as the objective function are chosen properly, a near-optimal solution can be found. To examine the efficiency of the proposed approach, a self-tuning and self-organizing fuzzy logic controller for an indirect field oriented induction motor drive is designed in both a sequential and a concurrent manner. A particular objective function (i.e., a performance index) is chosen to achieve a high dynamic performance. The simulation results demonstrate a significant enhancement in shortening the development time, and improving system performance over a manually tuned fuzzy logic controller.

This paper presents a self-organizing fuzzy logic control scheme based on neural networks, which consists of a traditional fuzzy logic (FL) controller and a conventional derivative (D) controller. Since membership functions regarding change-in-error ė represent the feedback of velocity they strongly affect transient behaviors of a system. In the proposed scheme, therefore, such membership functions are parameterized by the use of the cubic splines. Then, neural networks are adopted to optimize them in self-organizing process. To demonstrate the effectiveness of the proposed method, we report a number of simulation results involving both step and tracking control of a nonlinear plant.

In this paper, a fuzzy controller is designed for automotive active suspension systems. A half-car model is used in order to consider the pitch angle of the body and the coupling dynamics of front and rear wheels. It is assumed that the three measurements of body acceleration, front suspension deflection and rear suspension deflection are available. The fuzzy control rules are separately designed for each measurement. After the fuzzy control rules are determined, a genetic algorithm is applied to tune the membership functions of these control rules. To measure the performance of the designed genetic-based fuzzy active suspension system, three road disturbance models are designed to simulate actual road conditions. The performance of the designed system is evaluated with respect to these disturbance models, and it is shown that the designed active suspension system provides good performance in improving ride quality and maintaining vehicle maneuvrability. It is also shown that the designed active suspension system shows robust performance with system model uncertainties.

In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm.

In this paper, a semi-active control method for seismically excited nonlinear benchmark buildings equipped with controllable fluid viscous dampers is presented and evaluated. A Fuzzy Logic Controller (FLC) is designed to alter the damping coefficient of controllable viscous dampers during an earthquake. A Genetic Algorithm (GA) is employed to find optimum fuzzy controller rules, as well as adjusting the membership functions to minimize the overall damage index. This combination of a Genetic algorithm and a Fuzzy Logic Controller (GFLC) is also used to minimize the peak value of the top story displacement response, in order to verify the effectiveness of minimizing displacement on the damage index. The effectiveness of the FLC is verified and illustrated using the simulated response of 3-story and 20-story full-scale nonlinear benchmark buildings excited by several historical earthquake records, through a computer simulation on MATLAB. For comparison, a Linear Quadratic Gaussian (LQG) active controller is designed to minimize the peak value of the top story displacement response. Moreover, the results of a study that has used Self-Organizing Fuzzy Logic Controllers (SOFLC) with active actuators are used. The results demonstrate that the GFLCs are quite effective in overall damage reduction for a wide range of motions, compared with the SOFLC and LQG controllers.

A power system stabilization technique with an on-line self-organizing fuzzy logic controller is developed to enhance the damping of low frequency oscillation in a power system. The frequency deviation of a synchronous machine is utilized to calculate the stabilizing signal. Control rules and membership functions for the fuzzy logic controller are generated automatically without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. To show the effectiveness of the proposed controller, the comparison with a conventional controller is made for a single-machine infinite-bus system.

The traditional PID control method has the disadvantages of slow response speed, low precision and poor anti-interference ability when controlling the brushless DC motor system. In view of these shortcomings, a fuzzy PID control based brushless DC motor control system was proposed. The principle and characteristics of fuzzy control were introduced; The control principle of PID control based on fuzzy rules was analyzed; The fuzzy subsets were established and fuzzy control rules were established; The simulation of brushless DC motor control system was simulated by Matlab/Simulink. The results showed that the PID control algorithm based on fuzzy logic control improved the dynamic performance and stronger adaptive ability of the DC brushless motor.

During blueberry picking operations, changes in the blueberry harvester’s body posture (BHBP) caused by undulating farmland surfaces significantly affect the operational stability of the harvesting device. To mitigate these effects, an active suspension (AS) control system was developed. First, considering the coherence and time lag of the four-wheel tracks, the filtered white noise method was chosen to generate road excitations. Subsequently, the virtual prototype model of the blueberry harvester (BH) was built in ADAMS with the nonlinear properties of the passive suspension. A fuzzy proportion integral differential (PID) control algorithm and decoupled control strategy were employed to design the AS control system. Finally, ADAMS-MATLAB co-simulations and field tests were conducted. The results indicate that the measured farmland road profile conforms to standard D-level road excitations according to ISO8608, and the co-simulation model accurately predicts the BH’s dynamic response. Under AS control, the vertical acceleration, pitch acceleration, and roll acceleration of the BH decreased by 38.52%, 37.39%, and 34.29%, respectively, during simulations compared to the passive suspension. In field tests, these reductions were 36.51%, 33.84%, and 30.21%, respectively. The AS control system proposed in this study significantly improves the stability of the BH under farmland road excitation, effectively mitigating equipment impacts and wear caused by machine jolts while enhancing harvesting performance. This research addresses the gap in applying AS technology within the field of harvesting machinery, offering a novel technical approach for the development of vehicle control systems for harvesting machinery targeting blueberries and other shrub crops. It holds considerable theoretical value and practical significance.

Based on the genetic algorithm (GA), an approach is proposed for simultaneous design of membership functions and fuzzy control rules since these two components are interdependent in designing a fuzzy logic controller (FLC). With triangular membership functions, the left and right widths of these functions, the locations of their peaks, and the fuzzy control rules corresponding to every possible combination of input linguistic variables are chosen as parameters to be optimized. By using a proportional scaling method, these parameters are then transformed into real-coded chromosomes, over which the offspring are generated by rank-based reproduction, convex crossover, and nonuniform mutation. Meanwhile, the concept of enlarged sampling space is used to expedite the convergence of the evolutionary process. To show the feasibility and validity of the proposed method, a cart-centering example will be given. The simulation results will show that the designed FLC can drive the cart system from any given initial state to the desired final state even when the cart mass varies within a wide range.

The aim of this paper is to integrate the artificial immune systems and adaptive fuzzy control for the automobile suspension system, which is regarded as a multiobjective optimization problem. Moreover, the fuzzy control rules and membership controls are then introduced for identification and memorization. It leads fast convergence in the search process. Afterwards, by using the diversity of the antibody group, trapping into local optimum can be avoided, and the system possesses a global search capacity and a faster local search for finding a global optimal solution. Experimental results show that the artificial immune system with the recognition and memory functions allows the system to rapidly converge and search for the global optimal approximate solutions.

DVRs commonly use PI controllers. In spite of the fact that the required Proportional Integral controller does not alone provide the output, by using gains which are fixed when there is a variations in the operating conditions or system parameters. This problem is solved by an adaptive Proportional integral with fuzzy logic. The fuzzy controller will adjust the error and rate of error of the Proportional Integral controller according to respective readings and fuzzy control rules, in order to get adjusted to any operating conditions variations. The corresponding results of simulation proved that the method of adaptive Proportional Integral with fuzzy logic control considerably improves the DVR performance as compared to the normal Proportional Integral controller.

Fuzzy control rules and their natural control laws

In this paper a fuzzy Sugeno rule based controller is used to manage a semi-active suspension of 1/4 vehicle model. Our semi-active suspensions have the characteristic to change the damping constant in order to reduce vertical oscillation of the vehicle, to restrain the shell vehicle acceleration and consequently to be more comfortable for passengers. On the other hand, if no control is present, the suspension works in passive mode, i.e. without damping parameters changing. The inputs of our fuzzy controller, three in total, are the dynamic wheel load described by five membership functions, its derivative and the vertical acceleration both described by three membership functions. By comparing the performances of the suspension with and without control, the Sugeno based model controller results in a more comfortable and safe system.

Design of fuzzy logic controller for smart base isolation system using genetic algorithm

Aiming at increasing the performance of fuzzy control and keeping a fuzzy controller maintain an ideal given track to its objective value, and also aiming at finding an easy way to abstract fuzzy control rule set for the complex controlled process, this paper presents a new fuzzy control method, namely model based self-abstracting rule fuzzy control method (MBSAR). The MBSAR takes the one order inertia system as the reference model and abstracts the fuzzy control rules by self-organizing and self-tuning the consequences of each functioning fuzzy control rule both offline and online. A self-tuning rule set of the self-abstracting rules has been built by analyzing the dynamical response process of a controlled object. The simulations show that the MBSAR can decrease the overshoot significantly and has a wonderful control performance, which are better than that of the self-abstracting rule fuzzy control method without model and PI control method.