Control of open-loop unstable processes with time delay using PI/PID controllers specified using tuning rules: An outline survey

The ability of PI and PID controllers to compensate many practical processes has led to their wide acceptance in industrial applications. The requirement to choose two or three controller parameters is conveniently done using tuning rules. Starting with a general discussion of industrial practice, the paper provides a survey of tuning rules for continuous time PI and PID control of open-loop unstable time-delayed single-input, single-output (SISO) processes

System identification and speed control of electro- mechanical dual acting pulley continuously variable transmission

Researchers at Universiti Teknologi Malaysia (UTM) has designed, developed and patented an Electro-Mechanical Dual Acting Pulley Continuously Variable Transmission (EMDAP CVT). The newly developed EMDAP CVT is a complex nonlinear system. Since the system is difficult to be modeled, designing the suitable controller for the EMDAP CVT is a challenging task. However, it is possible to obtain model system and transfer function by employing System Identification (SI) technique. By having mathematical representation of the EMDAP CVT in form of transfer function, controller’s analysis and future works relating to the EMDAP CVT will be much easier. The main part of this research is to develop a model which is able to imitate the current EMDAP CVT system behaviours. Therefore, SI was performed to develop the model system and transfer function. Genetic Algorithm (GA) is used as an estimator with Nonlinear ARX (NARX) as a model structure. The mathematical modelling of the EMDAP CVT system is successfully presented and verified in form of 3rd order nonlinear transfer function. The focus of this research work is more on the implementation of speed control for the EMDAP CVT system based on model obtained from the SI. The EMDAP CVT speed controllers are designed for adjusting speed through providing appropriate CVT ratio to the system. The control objective is to achieve a desired output speed, which is used to specify and maintain the desired CVT ratio for the EMDAP CVT system. Proportional-Integral-Derivative (PID) controller is used as the basis and then fined tuned using conventional Ziegler-Nichols and Particle Swarm Optimization (PSO) method. Three controllers which are Proportional-plus-PSO (PPSO), Proportional-Derivative-plus-PSO (PD-PSO) and Proportional-Integral- Derivative-plus-PSO (PID-PSO) were developed to test the reliability of the obtained model system and transfer function. The performance of the designed controllers was demonstrated and validated through simulations and experiments. The error performance of the developed controllers is evaluated in terms of Integral of Absolute Error (IAE), Integral Square of Errors (ISE), Integral of Time multiplied by Absolute Errors (ITAE), and Mean Square Error (MSE). Based on the results, the PIDPSO speed controller gives a sufficient performance, such as settling time, overshooting and error performance. The validation approach resulted in lower than 5% percentage error thus verified the 95% confidence limit of the model system. Further controller’s analysis using Fuzzy Logic (FL) and Neural Network (NN) controllers were performed on the obtained model system and transfer function. The performance of the tested controllers were evaluated in terms of Steady State Error (SSE) and MSE values. All of the tested controllers produced good performance with steady state response within 5 seconds and SSE percentage lower than 5%. The end results show that, NARMA-L2 neural speed controller gives the best performance with SSE percentage of 0.91% and smallest MSE value of 3.28

Identification and Control of Open Loop Unstable Processes by Relay Methods

The paper describes a reliable automatic PID tuning method for open-loop unstable processes. Identification with low order models is performed by means of two relay tests, one with an additional delay, which does not require a priori knowledge about the process, with the only necessary condition being that the process be gain stabilisable. This paper provides an overview of the method, states conditions that need to be satisfied for its successful implementation, and demonstrates its application on a number of examples

Sviluppo di strategie di monitoraggio ed identificazione di controllori predittivi

Process optimization represents an important task in the management of industrial plants. It is strictly related to plant economics, so there is a high interest in the definition of reliable optimization schemes. At the moment, one of the most successful optimization algorithm is represented by MPC. This is an acronym standing for ``Model Predictive Controller''. MPC optimizes the process to which it is applied by ``predicting'' the state of the system over a future time window, using a process model as a part of its internal structure. MPC has been used since the last two decades, and at the moment it represents a proven optimization scheme, with thousands of applications in chemical and petrochemical industry. It is important to check regularly for MPC performances in order to guarantee optimal operation in spite of unknown disturbances and/or changes in the process dynamics. Such an operation is named “Performance Monitoring” or simply “Monitoring”. Despite its importance, at the moment there has not been an extensive analysis of this task in the literature, which is relatively poor compared to other fields related to MPC. A monitoring technique should be able to discern the cases in which the optimization scheme is working in optimal or sub-optimal conditions, and, in this latter case, it should recognize the causes of performance degradation. In the literature, the causes of performance degradation are usually two, i.e. inadequate estimation of unknown disturbances and a mismatch between the internal model and the real process. In the first case, the operations that are needed to correct the mistake consist in a better definition of the noise level of the system and in the calculation of a new estimator using the correct disturbance information. In the second case, the only way to improve the performances of the system is the definition of a new process model. This is a complex task, which takes a long time and requires a particular attention, and it is named “Identification”. Several different identification techniques were presented in the literature. They use input and output data sets coming from the system to compute a process model In this thesis, identification techniques for systems which can present difficulties have been introduced, i.e. unstable systems and ill-conditioned systems, and a monitoring technique for optimization schemes, tailored on MPC structure, has been discussed. Unstable systems cannot be usually identified with a class of identification schemes that perform a particular regression on data, because numerical problems arise due to the presence of high powers of the ``unstable'' system dynamic matrix. This work introduces an extension of the structure of this class of identification schemes which permits to handle data coming from an unstable system. Ill-conditioned processes give problems because data coming from this kind of processes are aligned in a particular direction, called “strong direction”. For this reason, a high level of information is present in the data set over that direction, but a low information level is present over other directions, resulting in models which cannot describe the system adequately in all directions. This work presents the guidelines of a successful identification method in which data are collected in closed-loop. Finally, the problem of MPC monitoring in this work is addressed analyzing the difference between the value of the real outputs coming from the system and the value of outputs predicted by the internal MPC model, which is usually indicated as ``Prediction error''. This analysis takes into account the statistical properties of the previously mentioned prediction error, in order to define if the optimization scheme works in sub-optimal conditions. Then, if this analysis shows the presence of some issues, the cause that generates these issues is determined by checking the rank of a particular matrix obtained from data, that is the observability matrix of an extended closed-loop system