Application of Iterative Learning Genetic Algorithms for PID Parameters Auto-Optimization of Missile controller

In this paper, iterative learning identification algorithms are proposed to estimate the time-varying parameters in multi-input-single-output (MISO) Wiener nonlinear time-varying systems. The regression model of the Wiener system is built by using the polynomial expansion of the nonlinear inverse function. Then, two iterative learning algorithms, including iterative learning gradient identification and iterative learning least squares identification, are presented to estimate the time-varying parameters of the regression model. The convergence performance of the iterative learning identification algorithms is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed algorithms.

PID control is the most mature control method in classical control theory. PID control is widely used in the electromagnetic bearing control system. However, how to design PID control parameters to reduce the adjustment time and overshoot of electromagnetic bearings after disturbance is still an issue that needs to be further investigated. In this paper, the influence of "natural" stiffness and "natural" damping on system stability is evaluated to select the value range of P and D control parameters, and the value range of the I parameter is determined by using the Rouse criterion. Based on the genetic algorithm with a novel fitness function considering both the system phase margin and closed-loop bandwidth, the PID controller parameters are optimized. The simulation results show that the overshoot and the adjustment time of the genetic algorithm optimized PID controller are much smaller than that of the conventional method.

Within impedance control framework, since force tracking accuracy is affected by the robotic tracking error, an adaptive force control scheme combined with an iterative learning algorithm is proposed in this paper. First, the unknown environment stiffness and location are observed in real time based on the actual contact force and robot position, and the adaptive reference trajectory is obtained by the estimated environment information. Then, position-based impedance control scheme is adopted to adjust the dynamical relationship between the end-effect or position and the contact force. Considering the effect of the position tracking error on the force tracking accuracy, an iterative learning control method is utilized to compensate the motion error effectively by the force error. The proposed method can effectively reduce the steady state force error by simply combining the indirectly adaptive reference trajectory generation technique with position tracking error regulation method. Stability and convergence conditions are presented with a Lyapunov function and frequency domain method. Simulations and experiments are conducted on a biaxial platform to demonstrate the effectiveness of the proposed method.

For the long time lag and nonlinear double-tank experimental system, research has been carried on by PID control strategy with more setting parameters of genetic algorithm. During the process of tank level control, a kind of adaptive setting method combined with genetic algorithm crossover probability and mutation probability is applied. By using the method to optimize parameters of the PID controller and writing simulation control program in the MATALB language environment to do the simulation study with the double-tank. The results of simulation and application shows that: the method can be used to realize PID parameters of global optimization and compared with conventional PID control, the tank level control with GA genetic and PID algorithm works with faster response and faster to achieve stability, which improve the performance of a control system.

The performance comparison of PID controllers for idle speed control internal combustion engine (ICE) is presented in this paper. There are three methods of PID tuning applied: heuristic tuning, self-tuning by pole placement, and also iterative learning algorithm (ILA). Prior to that, data collection is gained from four cylinder sparkignition engine modeling and used as the input-output data in the system identification part. The obtained transfer function from RLS technique is used in the development of the control system for the idle speed control by PID controllers. The main objective of the idle speed control is to maintain the idling speed at 650rpm and to reject as quickly as possible the extra load exerted on the engine system during idling mode. Comparing the three methods, it shows that PID tuning by ILA produced better results than heuristic tuning and pole placement.

This paper discusses a complex-valued Hopfield associative memory with an iterative incremental learning algorithm. The mathematical proofs derive that the weight matrix is approximated as a weight matrix by the complex-valued pseudo inverse algorithm. Furthermore, the minimum number of iterations for the learning sequence is defined with maintaining the network stability. From the result of simulation experiment in terms of memory capacity and noise tolerance, the proposed model has the superior ability than the model with a complex-valued pseudo inverse learning algorithm.

The authors show that a local iterative learning algorithm of Diederich and Opper (1987) converges for any set of patterns, including linearly dependent sets, and that the resulting coupling constants for a given set of patterns are the same as those given by the pseudoinverse rule for any maximal linearly independent subset of the given set. Thus, the matrix of couplings produced by the algorithm is the desired projection matrix onto the subspace spanned by the training set.

This paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy is augmented with iterative learning, which automatically updates control parameters based on the error between a target bucket fill weight and the measured fill weight at the end of each excavation pass. The algorithm was validated through full-scale autonomous excavation experiments with a 14-tonne capacity load-haul-dump (LHD) machine and two different types of excavation materials: fragmented rock and gravel. In both excavation scenarios, the iterative learning algorithm is able to update the admittance control parameters for a specified target bucket fill weight, eliminating the need to manually re-tune control parameters as material characteristics change. These results have practical significance for increasing the autonomy of robotic wheel loaders used in mining and construction.

A systematic method was introduced to design a full-scale fuzzy PID controller for power system stability control. The proposed controller uses the incremental form of a conventional PID controller as its underlying structure. To show the effectiveness of this method a form of full-scale fuzzy PID controller is designed. The rule-generated function is used to generate the rule base. The parameters of a conventional PID controller, which can be achieved by several well-known methods in control theory such as Ziegler-Nichols, Cohen-Cool and the genetic algorithm, make it possible to simplify the design process, as is desirable from an industrial point of view. One advantage of the new full-scale fuzzy PID controller is that it has the same structure and simplicity of its conventional counterparts but with more effectiveness and robustness. In order to illustrate the effectiveness of this controller a SVC-based stabiliser is designed using the proposed fuzzy PID controller.

Researching optimization problems of controller is need of industrial process control ,in which PID controller is widely used,which its parameters can be equivalent to optimization problems. In the industrial control the PID controller is widely used in excitation control system to improve its control performance.In order to find the optimal PID controller parameters effectively ,a kind of Adaptive Particle Swarm Optimization method (CAPSO) based on Cloud Theory is applied to fuzzy PID controller. Through the establishment of particle swarm algorithm of fuzzy PID controller parameters optimization model, which it can be used to optimize the membership function of fuzzy PID controller. Particle code adopts real coding. Particle dimension is related with the number of the input variables divided by fuzzy set and the number of control rules of whole fuzzy control system, thus the parameters of PID control are optimized on real time. The result from the simulation shows that compared with PID control and fuzzy control this system has several advantages which are small overshoots ,fast response ,and good stable performance to improve the control performance of excitation control system.

An Interactive PID Learning Module for Educational Purposes

In this paper, an innovative design method of PID controller based on genetic algorithm tuning parameters, VHDL description and FPGA implementation is proposed. The parameters of the PID controller are optimized by using genetic algorithm. The intelligent PID controller based on genetic algorithm is realized by using FPGA. During the realization process, the functional modules of the system are divided based on top-down design method. Each module of the system is described with VHDL language. Finally, the closed loop tests of the intelligent PID controller based on FPGA and genetic algorithm are implemented by means of DSP Builder and Matlab/Simulink. Simulation results have shown that the design method of the intelligent PID controller based on FPGA and genetic algorithm is verified, and it has some advantages such as design flexible, self-tuning on line, high reliability, short cycle of technical development, high running speed and so on. This intelligent PID controller based on FPGA and genetic algorithm can be applied in high control precision digital control system with high speed responsibility and high stability.

Different industrial applications frequently use overhead cranes for moving and lifting huge loads. It applies to civil construction, metallurgical production, rivers, and seaports. The primary purpose of this paper is to control the motion/position of the overhead crane using a PID controller using Genetic Algorithms (GA) and Bee Algorithms (BA) as optimization tools. Moreover, Fuzzy Logic modified PID Controller is applied to obtain better controller parameters. The mathematical model uses an analytical method, and the PID model employs Simulink in MATLAB. The paper presents the PID parameters determination with a different approach. The development of membership functions, fuzzy rules employ the Fuzzy Logic toolbox. Both inputs and outputs use triangular membership functions. The result shows that the optimized value of the PID controller with the Ziegler-Nichols approach is time-consuming and will provide only the initial parameters. However, PID parameters obtained with the optimization method using GA and BA reached the target values. The results obtained with the fuzzy logic controller (0.227% overshoot) show improvement in overshoot than the conventional PID controller (0.271% overshoot).

Proportional-integral-differential controllers (PID controllers) are one of the most widespread controller architectures and are used in a large number of technical systems [1]. When operating highly dynamic test benches to investigate the system behaviour of powertrain components with rotating movements, the controllers are often used as speed controllers. In order to reproduce the complex phenomena in such a powertrain as accurately as possible, the high dynamics of the test bench is required. This means that the test bench must be able to vary the speed highly dynamically and at the same time have the least possible unwanted influences on the system under investigation. This enables the system to be disconnected from the overall system to be specifically examined on the test bench with a virtual representation of the remaining system. A large part of this required system dynamics depends on the parameters of the PID controller. The adjustment of the parameters is often based on empirical knowledge and empirical methods, which have existed for several decades and consider a highly simplified system dynamics [2]. These procedures are easy to use, but are characterized by a high experimental effort and limited optimization potential. In addition, there are more and more approaches to perform the controller adjustment using metaheuristic algorithms [3, 4]. These work with detailed system models and are able to map the system behaviour in detail. Consequently, they offer a high optimization potential. Since these approaches are based on detailed knowledge about the adjustment procedure and the system to be optimized, they are not yet practicable for test bench operation with varying systems. Therefore, the authors present in this paper a method that allows to combine the easy handling of the empirical procedures with the optimization potential of metaheuristics. Thus, the user does not need to have a profound knowledge of control engineering and is guided through the adjustment process in the form of targeted support. For this purpose, the construction of system models by using a library based approach and the possibility of automatic system identification as well as a new optimization function are presented. For these only characteristic values such as overshoot, settling time or rise time have to be selected and a qualitative decision has to be made. This enables a simple and robust adjustment of the controllers. Literature [1] Tiwari, P.: Optimization of PID Parameter In Control System Tuning With Multi-Objective Genetic Algorithm 4 (2014) 5, S. 7 [2] Ziegler, J. G. u. Nichols, N. B.: Optimum Settings for Automatic Controllers. Journal of Dynamic Systems, Measurement, and Control 115 (1993) 2B, S. 220 [3] Zhang, J., Zhuang, J., Du, H. u. Wang, S.'a.: Self-organizing genetic algorithm based tuning of PID controllers. Information Sciences 179 (2009) 7, S. 1007-1018 [4] Fan u. Joo (Hrsg.): Design for auto-tuning PID controller based on genetic algorithms. 2009

PID controllers are essential for industrial and academic studies. A robust performance and an easy design of PID controllers have gained them a wide spread usage and they have been utilized over 90 percent of industrial implementation. In recent years, researches have been conducted on fractional order PID controllers that generalize the PID controller. Fractional order allows precise modeling and improvement of performance. However, in the conventional fractional order controller design, controller parameters are often determined by trial and error, and the load of the designer increases. This paper proposes a design method of optimal fractional order controller parameters. The proposed method utilizes frequency responses to design an optimal fractional order controller. The effectiveness of the proposed method is verified through the simulation result.