An Optimal Fuzzy Control Method for Nonlinear Time-Delayed Batch Processes

Data-driven two-dimensional integrated control for nonlinear batch processes

A novel design technique for high performance drives employing both H/spl infin/ optimal control and fuzzy control is proposed in this paper. The fuzzy control design is equipped with an adaptive learning algorithm to achieve H/spl infin/ tracking performance with external disturbances. It gives elevation to the selection of optimal performance weights without any trial and error attempt. The idea is to establish a link between H/spl infin/ optimal control design and fuzzy control design, so as to provide H/spl infin/ tracking design with more intelligence and achieve better performance with fuzzy control design. The effect of both fuzzy logic approximation error and external disturbance on the tracking error is attenuated to an assigned level. The control strategy does not require explicit knowledge of the motor/load dynamics, which is a useful feature when dealing with parameter and load uncertainties. The robustness of the proposed methodology is displayed for different types of trajectories. Experimental and simulation results suggest that the effect of both the fuzzy approximation error and external disturbance on the tracking error can be attenuated efficiently by the H/spl infin/ adaptive fuzzy control. Accordingly, the proposed design method is suitable for the robust tracking control of the uncertain nonlinear drive systems and is an attractive control design philosophy. Test results obtained indicate excellent tracking performance for both speed and position. Test results illustrate the validity of the proposed approach.

A magnetically levitated (MagLev) vehicle prototype has independent levitation (attraction) and propulsion dynamics. We focus on the levitation behavior to obtain precise gap control of a 1/4 vehicle. An electromagnetic levitation system is highly nonlinear and naturally unstable, and its equilibrium region is severely restricted. It is therefore a tough task to achieve high-performance vehicle-levitated control. In this paper, a MagLev system is modeled by two self-organizing neural-fuzzy techniques to achieve linear and affine Takagi-Sugeno (T-S) fuzzy systems. The corresponding linear-type optimal fuzzy controllers are then used to regulate both physical systems (voltage- and current-controlled systems). On the other hand, an affine-type fuzzy control design scheme is proposed for the affine-type systems. Control performance and robustness to an external disturbance are shown in simulation results. Affine T-S fuzzy representation provides one more adjustable parameter in the neural-fuzzy learning process. Therefore, an affine T-S-based controller possesses better performance for a current-controlled system since it is nonlinear not only to system states but also to system inputs. This phenomenon is shown in simulation results. Technical contributions include a nonlinear affine-type optimal fuzzy control design scheme, self-organizing neural-learning-based linear and affine T-S fuzzy modeling for both MagLev systems, and the achievement of an integrated neural-fuzzy technique to stabilize current- and voltage-controlled MagLev systems under minimal energy-consumption conditions.

In this paper, we bring about the idea of water level monitoring and controlling with IOT and Mobile applications. The vast amount of water wasted in the current scenario, mostly due to overflowing tanks is not acceptable. Existing water tanks control systems can monitor and control the water level in tank, which leads to reduction in amount of wastage of ample water but the type of sensors which are used in such a system does not specify exact changes in water level leading to unstable control parameters. Other technologies had some drawbacks based on speed of detection and approximation of sensor data. The need of improvement of these short-comings and providing an accurate and ethinic solution has been the main aim of this project. The project has been further improvised by using electronic water level sensors which uses potentiometric techniques to measure water level in water tanks, along with ultrasonic sensors for better metrics and control. Further up, this project can be implemented on large scale to control and detect rising water levels in dams and reservoirs to avoid flash floods and excess pressure in dams. The proposed system will help to identify the smallest changes in water level in case of rainfall measurement as well.

Presents an LMI approach to optimal fuzzy control based on a quadratic performance function. First, stability conditions are presented by relaxing the previous stability results. The relaxed stability conditions are represented in terms of LMIs. The optimal fuzzy controller design utilizing the relaxed stability conditions is proposed. The optimal fuzzy controller is designed by solving the minimization problem that minimizes the upper bound of a given quadratic performance function. Finally, a design example for the well-known nonlinear control benchmark problem, the TORA system, demonstrates the utility of the optimal fuzzy control based on a quadratic performance function.

In this paper, we present a global optimal and stable fuzzy controller design method for both continuous- and discrete-time fuzzy systems under both finite and infinite horizons. First, a sufficient condition is proposed which indicates that the global optimal effect can be achieved by the fuzzily combined local optimal controllers. Based on this sufficient condition, we derive a local concept approach to designing the optimal fuzzy controller by applying traditional linear optimal control theory. The stability of the entire closed-loop continuous fuzzy system can be ensured by the designed optimal fuzzy controller. The optimal feedback continuous fuzzy system can not only be guaranteed to be exponentially stable, but also be stabilized to any desired degree. Also, the total energy of system output is absolutely finite. Moreover, the resultant feedback continuous fuzzy system possesses an infinite gain margin; that is, its stability is guaranteed no matter how large the feedback gain becomes. Two examples are given to illustrate the proposed optimal fuzzy controller design approach and to demonstrate the proved stability properties.

Many signal processing methods have already been successfully translated from their original analog implementations into DSP (Digital Signal Processing). However, digital implementation of the nonlinear processing methods leads to spectral distortions which are not present in the analog processing. The paper presents two methods that minimize the spectral distortion associated with the nonlinear processing of digital signals. The first one uses oversampling of a processing signal. The second one, proposed by author uses the methods of analysis and synthesis of the signals. The article also presents an analysis of the use of the proposed methods in nonlinear processing of audio signals. Implementation of the nonlinear signal processing methods in digital systems.

To improve the performance of process monitoring method based on support vector data description (SVDD), a local kernel distance-SVDD (LKD-SVDD)-based process monitoring method for multiphase batch processes is proposed. First, the batches are partitioned into phases by using a warped k-means (WKM)-based clustering approach to addresses the sequential characteristics of batch process data. Then, considering the nonlinear and high-dimensional characteristics of batch process data, an LKD-SVDD-based modeling method is proposed for online monitoring and fault diagnosis, which effectively improves the computation efficiency and the process monitoring performance. A fed-batch penicillin fermentation process is used to demonstrate the feasibility of the proposed method.

To achieve on-board real-time processing for sliding-spotlight mode synthetic aperture radar (SAR), on the one hand, this paper proposes an adaptive and efficient imaging algorithm for the sliding-spotlight mode. On the other hand, a batch processing method was designed and optimized based on the AGX Orin platform to implement the algorithm effectively. Based on the chirp scaling (CS) algorithm, sliding-spotlight mode imaging can be achieved by adding Deramp preprocessing along with either zero-padding or performing an extra chirp scaling operation. This article analyzes the computational complexity of the two algorithms and provides a criterion called the Method Choice Indicator (MCI) for selecting the appropriate method. Additionally, the mathematical expressions for time–frequency transformation are derived, providing the theoretical basis for calculating the equivalent PRF and the azimuth width represented by a single pixel. To increase the size of the data that AGX Orin can process, the batch processing method was proposed to reduce peak memory usage during imaging, so that the limited memory could be better utilized. Meanwhile, this algorithm was also compatible with strip mode and TOPSAR (Terrain Observation by Progressive scans SAR) mode imaging. While batch processing increased data transfers, the integrated architecture of AGX Orin minimized the negative impact. Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. As a result, it took 19.25 s to complete the imaging process for sliding-spotlight mode data with a size of 42,966 × 27,648. Since satellite data acquisition time was 11.43 s, it can be considered that this method achieved near-real-time imaging. The experimental results demonstrate the feasibility of on-board processing.

In this paper a two-step design methodology of the near optimal Mamdani type fuzzy logic controller (FLC) has been applied to three types of systems: a linear time invariant system (LTI), a LTI system with time delay and an nonlinear system. In the first step, the tuning/learning procedure of a data base/knowledge base of the FLC is based on the model of the system using genetic algorithms (GA). The achieved solution is the optimal one with regard to the model of the system. In the second step experiments are performed on the real system at the vicinity of the optimum (achieved by GA) and using response surface methodology (RSM) control parameters are readjusted in order to achieve the near optimal solution for the real system. The proposed two step methodology gives a systematic way of the near optimal FLC tuning/learning when confronted with the real system and a very efficient combination of off-line and online part of design procedure.

The function of the robot is being complex and various. The robot with various inputs generates various outputs. A proper controller is needed to control such a complex robot. We introduce a 2-layer fuzzy controller (2LFC). It can control robustly and has the small number of rules in many input variables. It can not only deal with various inputs but also generate various outputs. The main problem in the fuzzy system is how to design the fuzzy knowledge base. For this specific problem, we optimize the fuzzy controller using the schema co-evolutionary algorithm (SCEA) to find a global optimal solution. Through the design of the optimal fuzzy controller using the SCEA and the simulation of the mobile robot, we verify the efficacy of the 2-layer fuzzy controller and the schema co-evolutionary algorithm.

SummaryThis paper presents a PI‐type adaptive iterative learning control (PI‐AILC) method for nonlinear processes, which targets enhancing system tracking capabilities by adapting setpoints of the PI controller. First, the proposed method employs compact form dynamic linearization technology to obtain a local linear representation of unknown nonlinear systems. Subsequently, the iterative learning controller gain is adaptively updated using the local linear expression to ensure the optimality of the setpoints. Finally, a pre‐learning mechanism for offline data is introduced to augment the efficiency of the iterative mechanism further. The proof of strict convergence for PI‐AILC is established. Experimental results substantiate the efficacy of PI‐AILC.

In batch processing, not only the characteristics of different phases are different, but also there may be different characteristics between batches. These characteristics of different phases and batches will have different effects on the final product quality. In order to enhance the safety of batch processes, it is necessary to establish an appropriate monitoring system to monitor the production process based on quality-related information. In this work, based on multi-phase and multi-mode quality prediction, a new quality-analysis-based process-monitoring strategy is developed for batch processes. Firstly, the time-slice models are established to determine the critical-to-quality phases. Secondly, a multi-phase residual recursive model is established using each quality residual of the phase mean models. Subsequently, a new process-monitoring strategy based on quality analysis is proposed for a single mode. After that, multi-mode quality analysis is carried out to judge the relevance between the historical modes and the new mode. Further, online quality prediction is achieved applying the selected model based on multi-mode quality analysis, and an according process-monitoring strategy is developed. The simulation results show the availability of this method for multi-phase multi-mode batch processes.

Water conservation and efficient resource management are crucial in modern times. This project presents an IoT-based smart water management system that integrates automatic water dispensing, water level monitoring, and temperature control. The system ensures optimal water usage by automatically dispensing water in predefined quantities, monitoring real-time water levels in tanks, and adjusting water temperature as needed.

PID Controller has contributed significantly towards industrial control system due to its simplified structure and implementation. After reviewing the existing system, it has been noticed that there is a potential challenge associated with tuning the traditional PID controller, which depends upon the maximum inclusion of the manual process. Considering an industrial water tank system, the proposed system develops a concept of a PID controller to maintain the desired level of water in the tank to achieve higher efficiency. The proposed study constructs a Reinforcement learning Agent to achieve optimal control parameters. The performance of the proposed study is compared with the existing tuning method. Based on the simulated study outcome, it has been analyzed that the proposed study provides intelligent and automatic control for maintaining the desired level of water in the tank. The proposed study is able to continuously maintain the water level in tanks with a smooth transition control process.