Plant Parameter Variations Research Articles

Automatic Generation Control using Genetic Algorithm Based PID Controller

In this paper Automatic Generation Control (AGC) of a single-area thermal power plant without reheat turbine is introduced using a Proportional Integral Derivative (PID) controller. The gains of the controller are optimized using Genetic Algorithm (GA). The problem of tuning the PID controller is formulated as optimization problem with constraints on proportional, derivative and integral gains. The proposed algorithm uses Darwin’s law of natural selection and survival of the fittest to reach the optimal solution. The simulation results confirm the system’s ability to retain frequency while handling sudden load disturbances. The second part of the investigation includes robustness testing of the system against plant parameter variations. The results are verified and the system performance is found to be robust against parameter uncertainties.

Optimal Linear Control of Modular Multi-Level Converters with a Prescribed Degree of Stability

In this paper, a new control approach using an optimal linear control with prescribed degree of stability for modular multi-level converters (MMC) is presented and analyzed. The proposed controller relies on a linear quadratic regulator with integral action which brings the ability of state variable reference tracking for modular multi-level converters. Since MMC is a complex system with several state variables, a unified control system design for this system is vital. The proposed controller of this study is designed to obtain wider stability margin thanks to the implementation of prescribed degree of stability concept to minimize the quadratic performance index of the control structure. By means of this method, the poles of the closed-loop system will be shifted to the desired places in the left half side of the S-plane. The main advantages of this control strategy compared to previous methods are that it will be possible to control the state of energy for each phase separately, while there will be superior tolerance to nonlinearities and the enhanced stability margin with less sensitivity to plant-parameter variations. The performance of the designed controller is verified through MATLABTM simulations (The MathWorks, Natick, MA, USA) with the nonlinear model of MMC.

Fault-Tolerant Flight Control for Elevator Faults Using S/CAS and Feedforward via Structured H∞ Control

This paper addresses the design problem of Fault-Tolerant Control (FTC) for airplane longitudinal motions against possible elevator efficiency reduction. The supposed FTC is composed of a feedforward gain and the conventional airplane control system, i.e. Stability/Control Augmentation System (S/CAS). The FTC is required to maintain good tracking performance with respect to pitch angle control under time-invariant plant modeling errors as well as time-varying possible elevator faults. The design problem is then formulated as structured robust H∞ controller design for parameter-dependent systems, i.e. single structured H∞ controller design for parameter-varying plant models. It is hard to obtain the globally optimal controllers for the problem due to the structural constraint for the controllers and the parameter-varying plant models. We thus adopt two-step design, viz., we first design a single common structured H∞ controller for finitely many plant models using non-smooth optimization technique, which has been implemented to Matlab® as hinfstruct, under the supposition that the elevator faults are time-invariant, and we then check the control performance of the designed FTC for the parameter-varying closed-loop system via Linear Matrix Inequality (LMI) condition. The control performance is examined via linear simulations as well as hardware-in-the-loop simulations.

Area determination of correct operation of the vibrofuge

The object of the study is a centrifuge, on the rotor of which a pneumatic spring with a mechanical vibrator is fixed. The operation of the pneumatic spring during rotation of the centrifuge rotor and the operation of a mechanical vibrator with varying plant parameters is analyzed. Identified parameters that significantly affect the operation of the plant. The areas of parameters in which the correct operation of the plant is observed are found.

Design of Adaptive Voltage Dither Control Framework Based on Spectral Analysis for Nonlinear Piezoelectric Actuator

Advances in miniaturization of micromachines are receiving considerable industrial attention, with a crucial aspect of research being on precision positioning and manipulation at the micro-nanoscale. Nanopositioners are precision mechatronic systems designed to deal with objects at extremely precise resolution wherein piezoelectric actuators have a high potential to impact emerging markets. However, the major bottleneck in harvesting the advantages of piezoelectric actuator for nanopositioning is the presence of inherent nonlinearity, mostly hysteresis, along with the presence of external dynamical disturbance, and traditional feedback controller cannot handle. Dithering has been used in the parlance of piezoelectric actuation as a surprisingly simple yet powerful means of enhancing system performance. This research presents the design framework of an adaptive voltage dither control logic based on spectral analysis of the system output using normalized harmonic ratio. The proposed controller adaptively tunes the intensity of dither amplitude depending on the system response to an optimum value that yields satisfactory results. A commercially available piezo-actuator has been used to model the system along with hysteresis using Dahl model, and system parameters have been identified experimentally. Performance of the proposed controller has been investigated by subjecting the plant model to several real-time perturbations like plant parameter variation, sinusoidal motion tracking, multi-amplitude multi-frequency input signal, external disturbances like Gaussian and impulse, step response, etc., and with the results showing better control performance and disturbance rejection capability as compared to traditional feedback control.

A Neuro-Fuzzy Model for Online Optimal Tuning of PID Controllers in Industrial System Applications to the Mining Sector

This paper develops a model for optimal and online tuning of PID controllers and evaluates its performance. The proposed model is based on computational intelligence approaches and can be used in industrial plant operating processes. The proposed tuning model is called PID-neuro-fuzzy model, a formulation based on a structured artificial neural network and fuzzy rules. The neural network is used to perform an optimal adjustment of PID controller gains to ensure the operating point of the system required to reduce the time of accommodation and the steady-state error. A fuzzy system is incorporated into a real-time gain-scheduling scheme to compensate for the possible variations in plant parameters. The performance of the proposed model is evaluated based on its ability to deal with uncertainties and disturbances in the process. The efficiency of the model is investigated through computational simulations in five plants of two industrial processes in the mining sector: 1) solid bulk unloading processes by car dumper and 2) solid bulk resumption process by bucket wheel reclaimers. In conclusion, the main advantage of the proposed model is its adaptability in relation to variations in plant parameters during the operational process.

Nonsingular Terminal Sliding Mode Control Based on Binary Particle Swarm Optimization for DC–AC Converters

This paper proposes an improved feedback algorithm by binary particle swarm optimization (BPSO)-based nonsingular terminal sliding mode control (NTSMC) for DC–AC converters. The NTSMC can create limited system state convergence time and allow singularity avoidance. The BPSO is capable of finding the global best solution in real-world application, thus optimizing NTSMC parameters during digital implementation. The association of NTSMC and BPSO extends the design of classical terminal sliding mode to converge to non-singular points more quickly and introduce optimal methodology to avoid falling into local extremum and low convergence precision. Simulation results show that the improved technique can achieve low total harmonic distortion (THD) and fast transients with both plant parameter variations and sudden step load changes. Experimental results of a DC–AC converter prototype controlled by an algorithm based on digital signal processing have been shown to confirm mathematical analysis and enhanced performance under transient and steady-state load conditions. Since the improved DC–AC converter system has significant advantages in tracking accuracy and solution quality over classical terminal sliding mode DC–AC converter systems, this paper will be applicable to designers of relevant robust control and optimal control technique.

Optimal fractional order PIλDμ controller for stabilization of cart‐inverted pendulum system: Experimental results

AbstractThe cart‐inverted pendulum is a non‐minimum phase system having right half s‐plane pole and zero in close vicinity to each other. Linear time invariant (LTI) classical controllers cannot achieve satisfactory loop robustness for such systems. Therefore, in the present work the fractional order PIλDμ (FOPID) controller is addressed for robust stabilization of the system, since fractional order controller design allows more degrees of freedom compared to its integer order counterparts by virtue of its two parameters λ and μ. The controller parameters are tuned by three evolutionary optimization techniques. In order to select the controller parameters optimally, a novel non‐linear fitness function using integral time square error (ITSE), settling‐time, and rise time is proposed here. The control algorithm is implemented successfully in real‐time. Moreover, stability analysis of the system compensated with a fractional order controller is presented using Riemann surface. Robustness of the physical cart‐inverted pendulum system towards multiplicative gain variations and plant parameter variations is verified. In this regard, it is shown that the fractional order controller provides satisfactory robust performance in both simulation and real‐time system.

Weighted Multiple Neural Network Boundary Control for a Flexible Manipulator With Uncertain Parameters

This paper addresses the angle tracking and vibration suppression for a flexible manipulator with uncertain parameters. Based on the partial differential equation (PDE) model, a unified framework of weighted multiple neural network boundary control (WMNNBC) is proposed to deal with the jumping parameters, in which neural networks are designed as the local boundary controllers to suppress vibrations. A novel proportion-derivative-like machine learning algorithm is developed to guarantee the learning convergence. Besides, the weighting algorithm is used to fuse multiple local neural network controllers to generate the appropriate global control signals with the variations of plant parameters. The stability of the overall closed-loop system is proved by the virtual equivalent system (VES) theory. The simulations are implemented to illustrate the feasibility and control performance of the proposed WMNNBC strategy.

Classical pole placement adaptive control revisited: linear-like convolution bounds and exponential stability

While the original classical parameter adaptive controllers do not handle noise or unmodelled dynamics well, redesigned versions have been proven to have some tolerance; however, exponential stabilization and a bounded gain on the noise are rarely proven. Here we consider a classical pole placement adaptive controller using the original projection algorithm rather than the commonly modified version; we impose the assumption that the plant parameters lie in a convex, compact set, although some progress has been made at weakening the convexity requirement. We demonstrate that the closed-loop system exhibits a very desirable property: there are linear-like convolution bounds on the closed-loop behaviour, which confers exponential stability and a bounded noise gain, and which can be leveraged to prove tolerance to unmodelled dynamics and plant parameter variation. We emphasize that there is no persistent excitation requirement of any sort; the improved performance arises from the vigilant nature of the parameter estimator.

Design of a digital controller for linear plant based on model following control system

In this paper, design of a digital controller based on model-following system is proposed for single-input-single-output (SISO) system. The controller needs a design method which could easily be done so the plant responses will follow the model responses. The digital controller could be designed for use when the orders of the model and the plant are same. As a result, a sample of simulation was showed that the proposed method was stable to plant parameter variations.

An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis.

A high-throughput plant phenotyping system automatically observes and grows many plant samples. Many plant sample images are acquired by the system to determine the characteristics of the plants (populations). Stable image acquisition and processing is very important to accurately determine the characteristics. However, hardware for acquiring plant images rapidly and stably, while minimizing plant stress, is lacking. Moreover, most software cannot adequately handle large-scale plant imaging. To address these problems, we developed a new, automated, high-throughput plant phenotyping system using simple and robust hardware, and an automated plant-imaging-analysis pipeline consisting of machine-learning-based plant segmentation. Our hardware acquires images reliably and quickly and minimizes plant stress. Furthermore, the images are processed automatically. In particular, large-scale plant-image datasets can be segmented precisely using a classifier developed using a superpixel-based machine-learning algorithm (Random Forest), and variations in plant parameters (such as area) over time can be assessed using the segmented images. We performed comparative evaluations to identify an appropriate learning algorithm for our proposed system, and tested three robust learning algorithms. We developed not only an automatic analysis pipeline but also a convenient means of plant-growth analysis that provides a learning data interface and visualization of plant growth trends. Thus, our system allows end-users such as plant biologists to analyze plant growth via large-scale plant image data easily.

Wavelet network for in-loop identification and control of frictional nonlinear systems

We propose a novel wavelet network for closed-in-loop identification and control of systems containing nonlinear coulomb friction. Usually an actual system contains both linear and nonlinear dynamics, hence my wavelet network structure yields two parts: (a) nonlinear connection from hidden (middle) layer neurons, (b) linear direct connections from input regressors to output neuron. The wavelet network identifier has appropriate dynamical functional links to identify the friction of the nonlinear system. Moreover, wavelet network controller employs the identified parameters for on-line training to compute appropriate control signal. The simulation results reveal that with this control strategy, the system can track even a periodic input reference signal successfully. In addition, in the presence of external disturbance and plant parameters variations, the compensated system operates facilitative. Furthermore, this identification and control strategy can eliminate chattering in spite of coulomb friction.

AkzoNobel and Gasunie study a large hydrogen plant in the Netherlands

The performance of the dual fluidized bed gasification plant Oberwart was evaluated by means of an extensive measurement campaign and calculation of mass and energy balances using IPSEpro. Process simulation was also applied to identify future optimization potentials. Different aspects are discussed such as the reduction of gasification temperature and the reduction of steam for gasification and air for combustion. Gasification pilot plant experience is integrated in the simulation models to increase significance of the simulation results. The mass and energy balances confirm that the performance of the CHP plant Oberwart is highly satisfactory and currently achieves an electrical efficiency of 29%. The variations of plant parameters provide deeper insight in the process itself and show interdependencies of different process units. With lower gasification temperatures and reduction in combustion air, the electrical efficiency can be increased to 31%.

Fractional PID controller design for nonlinear systems based on singular perturbation technique

This paper deals with the output regulation of nonlinear control systems in order to guarantee desired performances in the presence of plant parameters variations and external disturbances. The proposed control law structures are based on the fractional order PI (FOPI) and PID (FOPID) control schemes. By introducing the two-time-scale notions in the closed-loop system, the presented design methodology of fractional PID control guarantees desired transients. Then, the singular perturbations technique is used to analyse the closed-loop system properties and to get explicit expressions for evaluation of the controller parameters. The tuning of the controller parameters is based on a constrained optimisation algorithm. Simulation examples are presented to show the effectiveness of the proposed method.

Demonstrative fractional order – PID controller based DC motor drive on digital platform

In industrial drives applications, fractional order controllers can exhibit phenomenal impact due to realization through digital implementation. Digital fractional order controllers have created wide scope as it possess the inherent advantages like robustness against the plant parameter variation. This paper provides brief design procedure of fractional order proportional-integral-derivative (FO-PID) controller through the indirect approach of approximation using constant phase technique. The new modified dynamic particle swarm optimization (IdPSO) technique is proposed to find controller parameters. The FO-PID controller is implemented using floating point digital signal processor. The building blocks are designed and assembled with all peripheral components for the 1.5kW industrial DC motor drive. The robust operation for parametric variation is ascertained by testing the controller with two separately excited DC motors with the same rating but different parameters.

Robust and optimal technical method with application to hydrogen fuel cell systems

In this paper, a robust nonsingular fast converging sliding mode control (RNFCSMC) with particle swarm optimization (PSO)-based radial basis function (RBF) neural network is presented and applied in hydrogen fuel cell systems capable to maintain low harmonic distortion even in case of nonlinear load. The proposed technical method is a modified structure: a RNFCSMC plus a PSO-based RBF neural network. Though the classic sliding mode control has inherent robustness against plant parameter variations and load disturbances, the convergence of the system states to the zero is usually asymptotical in infinite time. The RNFCSMC is introduced to assure the finite time convergence of the system states and there is no singularity problem. But, once a severe load disturbance is applied, the chattering or steady-state error still exists in RNFCSMC. The PSO-based RBF neural network is employed to determine the control gains of the RNFCSMC, thus eliminating the chattering or steady-state error so that the system performance reaches the optimal point. The proposed technical method has been realized (1 kW, 110Vrms/60 Hz) for the actual single-phase hydrogen fuel cell inverters controlled by a TI DSP. Simulation and experimental results reveal that even under nonlinear load circumstances the proposed technical method yields voltage total harmonic distortion (THD) less than 1.4%, which excels the IEEE standard 519, thus demonstrating the effectiveness of the proposed technical method. Because the proposed hydrogen fuel cell system is considerably simpler to implement than classic sliding mode system and offers faster computational speed, this paper will be a beneficial reference to related control designers of hydrogen fuel cell systems.

Fundamental Property Shifts in MIMO V-Interconnected Systems for Control and Diagnostics

Abstract Theoretically inherent properties in the class of MIMO V-interconnected systems incline options for arising arbitrary shifts in their fundamental properties, thus demonstrating full-scale plant behaviors when running in a large process range. This curious effect leads to far more serious functional changes in the process properties from mere plant parameter variations. The phenomenon is commented occasionally by some authors in somewhat empirical forms, but nevertheless, it is not yet comprehensively examined in the light of the aggravating subsequences to the control efforts and the task of efficient process diagnostics to that class of plants.

Design of a H∞ Robust Controller with μ-Analysis for Steam Turbine Power Generation Applications

Concentrated Solar Power plants are complex systems subjected to quite sensitive variations of the steam production profile and external disturbances, thus advanced control techniques that ensure system stability and suitable performance criteria are required. In this work, a multi-objective H∞ robust controller is designed and applied to the power control of a Concentered Solar Power plant composed by two turbines, a gear and a generator. In order to provide robust performance and stability in presence of disturbances, not modeled plant dynamics and plant-parameter variations, the advanced features of the μ-analysis are exploited. A high order controller is obtained from the process of synthesis that makes the implementation of the controller difficult and computational more demanding for a Programmable Logic Controller. Therefore, the controller order is reduced through the Balanced Truncation method and then discretized. The obtained robust control is compared to the current Proportional Integral Derivative-based governing system in order to evaluate its performance, considering unperturbed as well as perturbed scenarios, taking into account variations of steam conditions, sensor measurement delays and power losses. The simulations results show that the proposed controller achieves better robustness and performance compared to the existing Proportional Integral Derivative controller.

Cyber‐physical robust control framework for enhancing transient stability of smart grids

Transient stability of power systems has become even more critical due to increasing complexity created by large penetration of renewable energy sources and massive deployment of information and communication technology. Fortunately, the two-way real-time data exchange capacity of smart grids allows designing advanced digital control schemes to better address the power system stability. In this study, a non-linear model-free-based robust controller in conjunction with a state estimation architecture is designed to enhance transient stability margins. The designed controller addresses uncertainties arising from communication and control input delay, sensor errors, varying plant parameters, and unmodelled dynamics effects. A novel time-delay compensation technique is presented in the control development to mitigate the effect of delay and the robustness of the proposed controller is proven by conducting a Lyapunov stability analysis with respect to additive disturbance and time delay. Furthermore, the proposed control framework is validated on the IEEE 39 bus test system through MATLAB simulation. The results show that the proposed framework is capable of stabilising the power system after a fault, also showing robustness to noise, latency in communication, delay in control input, and malicious data injection.