Optimization of Model Predictive Control Weights for Control of Permanent Magnet Synchronous Motor by Using the Multi Objective Bees Algorithm

In this study, the model predictive control (MPC) method was used within the scope of the control of the permanent magnet synchronous motor (PMSM). The strongest aspect of the MPC, the ability to control multiple components with a single function, is also one of the most difficult parts of its design. The fact that each component of the function has different effects requires assigning different weight coefficients to these components. In this study, the Bees Algorithm (BA) is used to determine the weights. Using the multi-objective function in BA, it has been tried to determine the weights that reduce the current values together with the speed error. Three different PI controllers have been designed to compare the MPC method. The coefficients of one of these are tuned with BA. Good Gain Method and Tyreus-Luyben Method were used in the other two. As a result of experimental studies, it has been observed that MPC can control PMSM more smoothly and accurately than PI controllers, with weights optimized with BA. With MPC, PMSM has been controlled with 15% settling time than other controllers and also with no overshoot.

Modelling, control and supervisory optimization of generalized predictive control in catalytic cracking reactor

As gasoline demand increases, the efficiency of operation of Fluidized Catalytic Cracking Unit (FCCU) becomes paramount importance. In this paper, a dynamic model for FCCU is simulated and integrated with yield model in order to estimate the yield of products namely gasoline, light gases and coke. Conventional PI controllers are designed for the control of reactor and regenerator temperature. Since, the complete reaction occurs in a very short duration, the controllers are tuned so as to achieve shorter settling time and minimum overshot. Further in order to increase the yield, optimization of FCCU using Generalized Predictive Controller (GPC) at supervisory level is attempted. Through optimization of objective function, the GPC will provide optimized set point for the PI controller in order to maintain maximum gasoline yield.

A novel direct torque and flux control of permanent magnet synchronous motor with analytically-tuned PI controllers

<span lang="EN-US">This work presents a novel direct torque and flux control (DTFC) of permanent magnet synchronous motor (PMSM) with analytically-tuned proportional integral (PI) controllers. The proportional (K_p) and integral (K_i) gains of the PI controllers were accurately determined, from first principle, using the model of the control system. The PI flux and torque controllers were then developed in rotor reference frame. The designed PI controllers, together with the torque and flux controllers, were tested on a permanent magnet synchronous motor (PMSM). The results obtained were compared with results from conventional DTFC system using manually-tuned PI controllers. The total harmonic distortion (THD) of motor phase currents is 18.80% and 4.81% for the conventional and proposed models respectively. This confirms a significant reduction in torque ripples. The control system was tested for step torque loading and found to offer excellent performance both during load changes, speed reversal, and constant load conditions.</span>

Decentralised PI controller design based on dynamic interaction decoupling in the closed-loop behaviour of a flotation process

An enhanced method for design of decenralised proportional integral (PI) controllers to control various variables of flotation columns is proposed. These columns are multivariable processes characterised by multiple interacting manipulated and controlled variables. The control of more than one variable is not an easy problem to solve as a change in a specific manipulated variable affects more than one controlled variable. Paper proposes an improved method for design of decentralized PI controllers through the introduction of decoupling of the interconnected model of the process. Decoupling the system model has proven to be an effective strategy to reduce the influence of the interactions in the closed-loop control and consistently to keep the system stable. The mathematical derivations and the algorithm of the design procedure are described in detail. The behaviour and performance of the closed-loop systems without and with the application of the decoupling method was investigated and compared through simulations in MATLAB/Simulink. The results show that the decouplers - based closed-loop system has better performance than the closed-loop system without decouplers. The highest improvement (2 to 50 times) is in the steady-state error and 1.2 to 7 times in the settling and rising time. Controllers can easily be implemented.

Neuroadaptive Robust Speed Control for PMSM Servo Drives with Rotor Failure

In this paper, a neuroadaptive robust trajectory tracking controller is utilized to reduce speed ripples of permanent magnet synchronous machine (PMSM) servo drive under the presence of a fracture or fissure in the rotor and external disturbances. The dynamics equations of PMSM servo drive with the presence of a fracture and unknown frictions are described in detail. Due to inherent nonlinearities in PMSM dynamic model, in addition to internal and external disturbances; a traditional PI controller with fixed parameters cannot correctly regulate the PMSM performance under these scenarios. Hence, a neuroadaptive robust controller (NRC) based on a category of on-line trained artificial neural network is used for this purpose to enhance the robustness and adaptive abilities of traditional PI controller. In this paper, the moth-flame optimization algorithm provides the optimal weight parameters of NRC and three PI controllers (off-line) for a PMSM servo drive. The performance of the NRC is evaluated in the presence of a fracture, unknown frictions, and load disturbances, likewise the result outcomes are contrasted with a traditional optimized PID controller and an optimal linear state feedback method.

Multi-model structured H-infinity design of a robust and aggressive turbofan jet engine controller

Jet engine control comprises tracking either the fan speed or engine pressure ratio setpoints. Further, safe operation entails maintaining several additional parameters, such as high-pressure turbine temperature, combustor pressure, core shaft acceleration and other ones within prescribed limits. A Min-Max selector that features PI controllers is frequently used to handle these requirements. However, this arrangement is overly conservative in the limits management, which unnecessarily slows down the engine response. To overcome this shortcoming, a new controller that adopts the traditional Min-Max structure in combination with the Ndot control, the Conditionally Active and the Conditioning Technique approaches is developed. PI regulators are replaced by dynamic output feedback controllers, which are designed according to a multi-model structured H-infinity methodology. This approach makes it possible to marry robustness with performance, which are two conflicting objectives. Singular value analysis tools demonstrate the robustness of the resulting design. Linear and nonlinear simulations indicate that the proposed controller optimizes the engine response time under the constraint of keeping a set of parameters within prescribed bounds. The features of the proposed design are lucrative for actual implementation in the industry.

Design of Feedback Control Strategies in a Plant-Wide Wastewater Treatment Plant for Simultaneous Evaluation of Economics, Energy Usage, and Removal of Nutrients

Simultaneous removal of nitrogen and phosphorous is a recommended practice while treating wastewater. In the present study, control strategies based on proportional-integral (PI), model predictive control (MPC), and fuzzy logic are developed and implemented on a plant-wide wastewater treatment plant. Four combinations of control frameworks are developed in order to reduce the operational cost and improve the effluent quality. As a working platform, a Benchmark simulation model (BSM2-P) is used. A default control framework with PI controllers is used to control nitrate and dissolved oxygen (DO) by manipulating the internal recycle and oxygen mass transfer coefficient (KLa). Hierarchical control topology is proposed in which a lower-level control framework with PI controllers is implemented to DO in the sixth reactor by regulating the KLa of the fifth, sixth, and seventh reactors, and fuzzy and MPC are used at the supervisory level. This supervisory level considers the ammonia in the last aerobic reactor as a feedback signal to alter the DO set-points. PI-fuzzy showed improved effluent quality by 21.1%, total phosphorus removal rate by 33.3% with an increase of operational cost, and a slight increase in the production rates of greenhouse gases. In all the control design frameworks, a trade-off is observed between operational cost and effluent quality.

Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

This paper proposes the adaptive particle swarm optimization (APSO) technique to control the active and reactive power produced by a variable wind energy conversion system and the exchanged power between the electric grid and the system during a voltage dip (VD). Besides, to get the variable speed wind energy maximum power, a maximum power point (MPP) methodology is utilized. The system under study is a 5 MW wind turbine connected via a gearbox to a doubly-fed induction generator (DFIG). The DFIG stator is branched directly to the electrical network, while the Back-to-Back converters couple the rotor to the grid. The decoupled vector control of the rotor side converter and the grid side converter is established primarily by a conventional proportional-integral (PI) and a second level by an intelligent PI whose gains are tuned using the proposed control. The performances and results obtained by APSO tuned PI controllers are analyzed and compared with those attained by classical PI controllers through the MATLAB/Simulink. The superiority of the advised technique is examined during a two-phase short-circuit fault condition and confirmed by the reduced oscillations.