Covariance matrix adaptation evolution strategy based design of fixed structure robust H∞ loop shaping controller

In this paper, a self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented. The controller has been applied to a control study of Fluid Catalytic Cracking (FCC) unit riser temperature, and regenerator temperature respectively. Performance comparison of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID(12.6%)/Fuzzy-PID(6%)); settling time (PID(80 seconds)/Fuzzy-PID(20seconds));Integral absolute error(PID(14.29)/Fuzzy-PID(8.63)); integral square error(PID(6.713).Fuzzy-PID(4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)).Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

This article presents a design method for translating a finite impulse response (FIR) floating-point multiplierless filter design. Conventional wisdom dictates that finite word-length (i.e., quantization) effects can be minimized by dividing a filter into smaller, cascaded sections. In this design method, it is shown how to quantize the cascaded sections so that the finite word-length effects in one section are guaranteed to compensate for the finite word-length effects in the other section. This simple method called, compensating zeros, ensures that: (1) the quantized filter's frequency response closely matches the unquantized filter's frequency response (in both magnitude and phase); and (2) the required hardware remains small and fast.

Verifying fragility in digital systems with uncertainties using DSVerifier v2.0

The effects of finite word length on arithmetic codes are studied. Four causes of performance degradation are identified. Emphasis is placed on the major source of the degradation, i.e., finite word-length registers used to compute the arithmetic codes. Theoretical analysis and computer simulation of this effect are presented. It is found that this degradation is proportional to the size of the alphabet and is reduced by half for each bit of increased word-length. >

Model reduction techniques are simplification methods based on mathematical approaches employed to realize reduced models for the original high order systems. Some existing classical model reduction techniques for multivariable system are considered and compared for their performances. Interlacing property and coefficients matching (IPCM) method gives overall minimum integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE) values compared to other methods. Though the IPCM method is efficient, it may not guarantee for minimization of all objective functions simultaneously. In this paper, model reduction approach based on objectives like ISE, IAE and ITAE using multi-objective differential evolution (MODE) method is proposed for reducing the numerator and the denominator is reduced by interlacing property. MODE method minimizes the small, normal and large errors persisting for long time between original and reduced models. This multi-objective approach is applied for model reduction of 10th order multivariable linear time invariant power system model. Simulation results are demonstrated for single and multi-objective model reduction and compared with multi-objective particle swarm optimization (MOPSO) method to prove the validity of proposed MODE technique.

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

The accuracy of a digital filter is limited by the finite word length used in its implementation. Techniques have been developed to analyze this problem. Good agreement between the theoretical and experimental results has been reported. This paper discusses some of these accuracy problems and reviews some of the approaches used in investigating them. The calculation of the statistical mean-squared error at the output of the filter is discussed in detail.

This paper presents a design synthesis method for robust controllers of active vehicle suspensions (AVSs). Various control techniques have been applied to the design of AVSs for enhancing ride comfort and handling performance of ground vehicles. However, most of these model-based controller designs show poor robustness when the vehicle models are not accurate and operating conditions vary. To address the poor robustness problem of AVSs, a new controller is designed using the H∞ loop-shaping control technique. The controller targets robustness issues on vehicle models with parametric uncertainties and unmodelled dynamics. To facilitate the robust controller design, a design synthesis method is proposed: the H∞ loop-shaping controller design is formulated as a multi-objective optimization problem, the weighting functions’ parameters of the controller are treated as design variables, the expensive computing loads are handled by a parallel computing technique, and the solution of the optimization problem is the desired robust AVS controller. Simulation results demonstrate the benefits of the proposed AVS design.

The effect of the finite word length on the moment of the impulse response of a digital filter is investigated. It is shown that the quantization error in the moment values is almost linear with a slope of 3 dB/b and is well related to the output roundoff noise. An algorithm is also proposed to order the cascade sections of a finite impulse response (FIR) filter with low output roundoff noise. >

Comments on 'Effects of finite word length on FIR filters for MTD processing' by the author of 'Sacrifices in radar clutter suppression due to compromises in implementation of digital Doppler filters'

In implementation of high order digital controllers, the system performance is deteriorated by the finite word length (FWL) effects. To cope with FWL problems, lots of design methods were considered in the signal processing field for the implementation of high-ordered digital filters. Among these methods, the implementation technique based on the state-space realization which minimizes the sensitivity for the purturbation of coefficients has been proposed. In this paper, we show the algorithm to find a state-space realization which minimizes the frequency weighted sensitivity measure for the closed-loop system performance. Some simulation results are shown to verify the effectiveness of the proposed algorithm.

The aim of this paper is to explain the way to scale back the dimension of an influence system stabilizer (PSS) supported H∞ management theory. In recent years, sturdy PSS styles that adopt associate in nursing H∞ controller are investigated so as to ensure the performance once the state of the system configuration and power flow modification. However the H∞ controller has not been wide adopted into sensible use attributable to the tangled nature of its theory and structure. We tends to think about the H∞ management downside below the condition that PSS structure is mounted to be a lead-lag compensator. Infinity norm of transfer function from disturbance to output is subjected to be minimized via searching and evolutionary computation. The resulted optimal parameters make the system stable and also guarantee robust performance. We applied the evolutionary robust controller to a pneumatic servo system. For performances comparison, three controllers; PID with derivative first order filter controller, PI controller and H- loop shaping controller are investigated. We tends to optimize the parameters of the Lead-Lag PSS by a genetic algorithm program that has associate in nursing analysis perform that takes under consideration a closed-loop system H∞ norm and a desired response. During this manner, we tends to design a PSS that encompasses a standard managementler structure and guarantees its control performance.

Using dimensional analysis and numerical optimisation techniques, an optimal method for tuning PID controllers for first order plus time delay systems is presented. Considering integral square error (ISE), integral absolute error (IAE) and integral time absolute error (ITAE) performance criteria, optimal equations for obtaining PID parameters are proposed. Simulation results show that the proposed method has a considerable superiority over conventional techniques. In addition, the closed loop system shows a robust performance in the face of model parameters uncertainty.

Two-wheeled wheelchairs have been used as alternatives for the elderly and disabled people to perform physical activities due to their restriction of movement. Significant challenges posed by two-wheeled wheelchairs control due to their inherent instability, resembling that inverted pendulum system. This research addresses these challenges by developing a dynamic non-linear model and stability control using computational algorithms. A neural network-based Nonlinear Autoregressive model with Exogenous Inputs (NARX) was developed, capturing and behaving similar to the complex dynamics of the wheelchair system with the identification on the experimental input–output data. Ensuring stable and responsive control, design of optimized PD-type and PID-type fuzzy logic controllers using Particle Swarm Optimization (PSO) were established and were tested under a simulation environment. The performance was evaluated across various metrics, including Integral Squared Error (ISE), Integral Absolute Error (IAE), Mean Squared (MSE), and Integral Time Absolute Error (ITAE). The result demonstrates that the PSO Optimized PID-type fuzzy logic controller with scaling factor from MSE index performance come out as best overall, significantly outperforms PD-type fuzzy logic controller, reducing its settling time by 12.5% to 35 s, minimizing overshoot to 0.81°, and achieving a negligible steady-state error of 0.046%. These results highlight the significant of integrating fuzzy logic control and PSO to the neural network model in enhancing the stability and performance of two-wheeled wheelchair systems, offering user safety and comfort.

Aircraft are the most common kind of transportation utilized by the government, commercial, military, and so on. Controlling the motion of an aircraft is a highly nonlinear operation. However, regulating direction and motion is critical in the aviation industry to prevent accidents, injury, and other problems. It is vital to pay attention to factors like stability in an aircraft. For stabilization, 3 orientationswere used to determine the movements: yaw, pitch, and roll. The main purpose of this research is to create a controller that could be used to regulate the pitch of an aircraft. This part of control will assist the plane in dropping or rising. Pitch can be varied by altering the elevator, which is located on the plane's rear portion. The longitudinal mechanics of aircraft were used to create the aircraft pitch system mathematically. The advanced controller, neuro-fuzzy, is used to regulate the pitch automatically. The performance of the neuro-fuzzy controller is equated to the traditional Proportional Integral Derivative (PID) controller. Two parameters are used for comparison: time characteristics and assessment metrics. Integral square error (ISE), Integral Absolute Error (IAE), and Integral Time Absolute Error (ITAE) is the assessment measures used in this study. The simulation outcomedemonstrates that the neuro-fuzzy controller outperforms the PID controller.