小型２輪マルチコプタのディジタル制御系の設計

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.

Based on the ant colony system (ACS) algorithm and fuzzy logic control, a new design method for optimal fuzzy PID controller was proposed. In this method, the ACS algorithm was used to optimize the input/output scaling factors of fuzzy PID controller to generate the optimal fuzzy control rules and optimal real-time control action on a given controlled object. The designed controller, called the Fuzzy-ACS PID controller, was used to control the CIP-I intelligent leg. The simulation experiments demonstrate that this controller has good control performance. Compared with other three optimal PID controllers designed respectively by using the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing, it was verified that the Fuzzy-ACS PID controller has better control performance. Furthermore, the simulation results also verify that the proposed ACS algorithm has quick convergence speed, small solution variation, good dynamic convergence behavior, and high computation efficiency in searching for the optimal input/output scaling factors.

This paper proposes a fractional order PID analytical design framework for industrial robot based on digital twin. The effectiveness of the digital twin real-time optimization framework is verified by the simulation of PMSM motor speed control. The digital twin framework implemented in this paper consists of three following parts: multi-domain modeling, behavioral matching and control optimization. Firstly, the physical system is modeled to realize the omni-directional monitoring of robot and the kinematic simulation of single-axis motor. A model similar to the real physical system can be obtained in the step of behavioral matching, which is conducive to the next step of controller parameter optimization. The analytical design method of the five-parameter fractional order PID controller is then presented to optimize the parameters based on the accurate model after behavioral matching, so that the control system can meet the given frequency domain specifications and the required tracking performance. The simulation results show that the fractional order PID controller is robust to loop gain changes. In terms of time domain performance specifications like rise time, overshoot, and adjustment time, fractional order PID controller performs better than integer order PID controller.

In the process of frozen food production, its refrigeration system is a typical large inertia, nonlinear, strongly coupled system, which is prone to large overshooting during operation, resulting in cost increase and quality control problems. This paper solves the problem of large overshoot in the digital PID control process by designing a fuzzy control, and designs a practical fuzzy controller so that the design method of the fuzzy PID controller can be realized in the simulation and actual control of the freezing system. A genetic algorithm optimization scheme is also designed for the fuzzy PID control system to optimize the fuzzy controller’s affiliation function, control rules, as well as proportionality and quantization factors, to enhance the model’s dynamic characteristics and robustness. Design of food quick-freezing simulation experiments and use the model in the refrigeration system control in practice, compare the performance of this model with the PID, fuzzy PID model, and found that the temperature difference set value of 4 ℃ and 3 ℃, respectively, the parameters of the traditional PID controller in the commissioning stage of the system after the end of the rectification to remain fixed, which makes the actual quality of the cooling water temperature difference control degradation. And the fuzzy genetic PID controller still has good control quality. In this paper, the model curve converges rapidly, the overshoot is only 3.9%, and the regulation time is only 450M seconds. The designed method of the study is feasible and has high industrial application value.

The paper presents a design method of multivariable adaptive digital tracking PID controllers using genetic algorithms (GA) and its application to the attitude control and momentum management (ACMM) system for a space station. The controller comprises two parts: (i) a fast online recursive identifier using the least squares method with variable forgetting factors to provide the updated dynamic parameters of the plant; and (ii) a genetic tuner based on artificial genetic algorithms to tune online the parameter matrices of the controller. The application of the controller using GA to the ACMM system for the space station yields remarkable results in stability robustness and setpoint tracking behaviour with respect to the large moments of inertia variations.

Although the traditional PID controller is widely used in many fields, the system parameters varying and external disturbances existing in the DC servo system will cause large overshoot or poor stability. To improve the performance of the PID controller, a compound servo control system combining the conventional PID control and the fuzzy control is presented to meet the demand of a vehicular antenna servo system in this paper. Incorporating the fuzzy control and the conventional PID control, this paper presents a design method of the fuzzy PID controller that is based on the fuzzy tuning rules and formed by integrating two above control ideas. Simulation results are presented to show the efficiency of the proposed controller. The practical control effect shows that the control system that adopts the fuzzy PID controller has better performance than that of the traditional PID control system, and meets the performance requirements of the servo system.

When a controlled object has a nonlinear characteristic, a good control result is not always obtained with fixed PID gains. This paper proposes a design method of a nonlinear PID controller using a neural network to overcome the problem. In the proposed controller, PID gains are tuned online by a neural network and a controlled object is controlled by the PID controller with the tuned PID gains. The neural network learns by a learning algorithm based on the Extended-Fictitious Reference Iterative Tuning (E-FRIT) and the backpropagation. The FRIT is a method that tunes control parameters directly by using a set of operating data. The main advantage of the FRIT is that the parameters can be determined without any mathematical model. The FRIT evaluates only a control response, whereas the E-FRIT does it as well as the difference of manipulated variable. In this paper, the E-FRIT is utilized for a learning algorithm of a neural network. Simulation examples are provided to show an effectiveness of the proposed method by simulation examples.

In this paper, a frequency response based design method of PID controller is proposed for higher order (HO)/-plus delay time (HOPDT) systems. The HO/-PDT models are converted into real and imaginary part at a frequency where a criteria similar to amplitude and phase margin is utilized to get the constraints on the parameters of the controllers. To reject noise and load disturbances, the constraints given by sensitivity and complementary sensitivity function are also considered. The proposed method has superior features for systems with high order, time delays and monotonic or oscillatory responses. Three simulation examples are included to show the effectiveness, simplicity and applicability of the proposed method.

A new design method for PID controller using user-specified gain margin and phase margin for delayed systems has been proposed. The proposed PID controller is particularly useful in auto-tuning. The controller parameters are calculated using several approximations and elimination processes. The robustness of the proposed controller is verified by different simulation examples.

This paper compares two approaches to tune PI controllers in a multivariable process. A modified Direct Synthesis method extends the technique to a multivariable system. Results show that the modified Direct Synthesis technique relies on model accuracy and suitability of the desired transfer function and process model. The effectiveness of this technique may break down when these conditions are not met. Genetic algorithms provide a method based on the evolution of species via natural selection. The GA method allows the use of a specific structure, such as PI controller, and then gets the best possible results for that structure. The concept has been adapted into an optimization technique that can be used to find the best controller parameters to meet user defined transient specifications for a complex process. Thus, the robustness of the method allows it to outperform the former technique. Simulation results are shown to illustrate the comparisons between these two methods and the effectiveness of Gas.

The hover, transition, and fixed-wing modes are the three flight configurations available for the tilt tri-rotor unmanned aerial vehicle (UAV). . PID and SMC controllers were commonly used to build flight controllers to help realize stable flight for this UAV. The PID control method has the ability to adjust the error as low as possible, increase the response speed, reduce the overshoot, and limit the oscillation. The outstanding advantage of the sliding controller is its stability and stability even when the system has noise or when the object's parameters change over time. This paper will build flight control including attitude control, and position control for the Tilt tri-rotor model in hover mode based on PID control and SMC to evaluate their performance. First, the kinematic equations of Tilt tri-rotor UAV are built based on coordinate systems and Newton's laws. Secondly, two control diagrams and built two flight controllers based on PID and SMC control methods are investigated. Finally, the simulation results of these two methods are shown and discussed. The results show that the SMC controllers give better stability and better orbital tracking in both cases of noise and no noise.

This paper proposes a novel method to graphically compute all feasible gain and phase margin specifications-oriented robust and optimal fractional PID controllers to stabilize time delay processes (TD) of any order. The gain-phase margin tester method is utilized to guarantee the concerned systems certain robust safety margins and test the constant gain margin boundary, constant phase margin boundary, and stability boundary in the parameter plane. The overlapping region of these plotted boundaries, which represents all feasible robust stabilizing PID controllers and allows the considered TD processes to retain pre-specified safety margins, is graphically determined as the gain and phase margin specifications-oriented region (GPMSOR). The variations of the GPMSOR and the stability region with respect to the variations of the noninteger orders of the integral and derivative terms have been investigated. According this, it is possible to slightly enlarge the GPMSOR by altering the noninterger order of the integral and derivative terms. This modifications in GPMSOR through altering the noninteger orders of the integral and derivative terms allows us to find the robust and optimal PID controller gain set for the considered TD systems. In other words, the selected fractional order PID controllers from the GPMSOR not only retain the prescribed GM and PM specifications but minimize the integral of the absolute error (IAE) or the integral of the squared error (ISE) performance criterion. Finally, an illustrative example cited from the literature with computer simulations is provided to demonstrate the effectiveness and confirm the validity of the proposed methodology.

This paper presents a now method of optimal digital PID feedback control for a linear multivariate system by using a, parameter optimization technique. The most difficult points for the design of optimal digital PID feedback control system by the modern control theory are : (1) the control structure of it is specified in advance, (2) unless all the state variables are fed back, the optimal control cannot be obtained by dynamic programming or the maximum principle. However, the optimal digital PID feedback gains are determined by using the parameter optimization technique in this paper. When there are some states which are not fed back in the control system, the optimal feedback gains depend on the initial states. First, the optimal solution is presented in the case where all the initial Btates are known. Next, it is extended to the case where the initial states are random variables and their statistical properties are known,

An optimal adaptive robust PID controller subject to fuzzy rules and sliding modes for MIMO uncertain chaotic systems

This paper presents a method for PID controller design which can achieve dominant pole assignment using two of the controller parameters. The non-dominant poles are restricted on the left of the line s = σ̂, where σ̂ is the minimum feasible value, called as the feasibility border. It is obvious that a dominant pole assignment is not practical if σ̂ is close to the real parts of the required dominant poles. Hence, finding σ̂ for a given system is very important. The method, which parameterizes all such controllers in order to allow further design criteria, can be applied to other kinds of low-order compensators.