Permodelan dan Simulasi Pid Kontrol pada Alat Penukar Panas

Alat penukar panas secara luas digunakan untuk memindahkan panas dari fluida panas ke fluida dingin, sehingga pengendalian temperatur fluida aliran keluar dari alat penukar panas menjadi sangat penting. Pengendali PID secara konvensional dapat digunakan untuk mengoptimalkan suhu keluar dari alat penukar panas. Untuk pengendali PID alat penukar panas, nilai parameter tuning dihitung dengan metode tangent. Perancangan pengendali mengatur suhu fluida keluar pada pada set point yang diinginkan dalam waktu sesingkat mungkin tanpa memperhatikan massa aliran dan proses gangguan, ketidakstabilan peralatan dan tidak linier. Pengendali IMC Model memberikan hasil yang sangat baik pada overshoot dari kurva proses dibandingkan dengan pengendali klasik

Evolutionary learning and global search for multi-optimal PID tuning rules

With the advances in microprocessor technology, control systems are widely seen not only in industry but now also in household appliances and consumer electronics. Among all control schemes developed so far, Proportional plus Integral plus Derivative (PID) control is the most widely adopted in practice. Today, more than 90% of industrial controllers have a built-in PID function. Their wide applications have stimulated and sustained the research and development of PID tuning techniques, patents, software packages and hardware modules. Due to parameter interaction and format variation, tuning a PID controller is not as straightforward as one would have anticipated. Therefore, designing speedy tuning rules should greatly reduce the burden on new installation and ‘time-to-market’ and should also enhance the competitive advantages of the PID system under offer. A multi-objective evolutionary algorithm (MOEA) would be an ideal candidate to conduct the learning and search for multi-objective PID tuning rules. A simple to implement MOEA, termed s-MOEA, is devised and compared with MOEAs developed elsewhere. Extensive study and analysis are performed on metrics for evaluating MOEA performance, so as to help with this comparison and development. As a result, a novel visualisation technique, termed “Distance and Distribution” (DD)” chart, is developed to overcome some of the limitations of existing metrics and visualisation techniques. The DD chart allows a user to view the comparison of multiple sets of high order non-dominated solutions in a two-dimensional space. The capability of DD chart is shown in the comparison process and it is shown to be a useful tool for gathering more in-depth information of an MOEA which is not possible in existing empirical studies. Truly multi-objective global PID tuning rules are then evolved as a result of interfacing the s-MOEA with closed-loop simulations under practical constraints. It takes into account multiple, and often conflicting, objectives such as steady-state accuracy and transient responsiveness against stability and overshoots, as well as tracking performance against load disturbance rejection. These evolved rules are compared against other tuning rules both offline on a set of well-recognised PID benchmark test systems and online on three laboratory systems of different dynamics and transport delays. The results show that the rules significantly outperform all existing tuning rules, with multi-criterion optimality. This is made possible as the evolved rules can cover a delay to time constant ratio from zero to infinity based on first-order plus delay plant models. For second-order plus delay plant models, they can also cover all possible dynamics found in practice

Graphical PID tuning method for uncertain fractional-order multivariable systems

In this paper, a graphical tuning method for controllers parameters based on the open-loop fractional transfer function (FO-EOTF) method is proposed for fractional-order parameter uncertain multivariable system. The FO-EOTF method is proposed to transform the parameter uncertain fractional-order multivariable system into a set of independent parameter uncertain fractional-order univariate systems and determine the parameters regions of the univariate systems. The gain phase margin tester is used to further guarantee the robust performance of the controlled system. Finally, simulation result from the numerical simulation is presented to demonstrate the effectiveness of this method

Trajectory-scheduling control systems and their multi-objective design automation

This thesis encompasses the analysis of TSN systems and their multi-objective design methods. TSN nodes are networked through interpolation and activation, similar to a gain-scheduling or local model/controller network. However, to achieve accuracy and ease of commissioning without requiring a large number of nodes, an algorithm has been developed first to identify optimum transition nodes within the entire operating envelope. Then the TSN approaches a nonlinear plant globally, not just locally, without requiring linearization. If desired or necessary, global optimisation provides an enhancement in the design process for TSNs. Since optimising only one aspect (a single objective) of performance while compromising others is undesirable, multi-objective designs have been developed concurrently to deliver or improve multiple aspects of performance. Following the development of a TSN, it is applied to nonlinear system modelling, and this TSN is termed a Trajectory-Scheduling Model (TSM). A TSM possesses the same properties and design features as the TSN generic framework. A nonlinear system, a coupled liquid-tank, is used to examine this modelling technique. Results verify the feasibility and effectiveness of the methods developed and validates the TSM. Further, the TSN technique is applied to nonlinear controller design, by way of a Trajectory-Scheduling Controller (TSC) network. It is illustrated through the design of a networked, easy-to-understand and easy-to-use PID control system for the coupled liquid-tank. Results show that the methods developed offer a high-performance linear control system with nonlinear capabilities to handle practical systems operating in a broad range and to cope with conflict between setpoint following at transient and disturbance rejection at steady state. This method is then applied to the PID network design problems for two nonlinear chemical processes

Performance analysis of robust stable PID controllers using dominant pole placement for SOPTD process models

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThis paper derives new formulations for designing dominant pole placement based proportionalintegral-derivative (PID) controllers to handle second order processes with time delays (SOPTD). Previously, similar attempts have been made for pole placement in delay-free systems. The presence of the time delay term manifests itself as a higher order system with variable number of interlaced poles and zeros upon Pade approximation, which makes it difficult to achieve precise pole placement control. We here report the analytical expressions to constrain the closed loop dominant and nondominant poles at the desired locations in the complex s-plane, using a third order Pade approximation for the delay term. However, invariance of the closed loop performance with different time delay approximation has also been verified using increasing order of Pade, representing a closed to reality higher order delay dynamics. The choice of the nature of non-dominant poles e.g. all being complex, real or a combination of them modifies the characteristic equation and influences the achievable stability regions. The effect of different types of non-dominant poles and the corresponding stability regions are obtained for nine test-bench processes indicating different levels of open-loop damping and lag to delay ratio. Next, we investigate which expression yields a wider stability region in the design parameter space by using Monte Carlo simulations while uniformly sampling a chosen design parameter space. The accepted data-points from the stabilizing region in the design parameter space can then be mapped on to the PID controller parameter space, relating these two sets of parameters. The widest stability region is then used to find out the most robust solution which are investigated using an unsupervised data clustering algorithm yielding the optimal centroid location of the arbitrary shaped stability regions. Various time and frequency domain control performance parameters are investigated next, as well as their deviations with uncertain process parameters, using thousands of Monte Carlo simulations, around the robust stable solution for each of the nine test-bench processes. We also report, PID controller tuning rules for the robust stable solutions using the test-bench processes while also providing computational complexity analysis of the algorithm and carry out hypothesis testing for the distribution of sampled data-points for different classes of process dynamics and non-dominant pole types.KH acknowledges the support from the University Grants Commission (UGC), Govt. of India under its Basic Scientific Research (BSR) schem

Trajectory-scheduling control systems and their multi-objective design automation

This thesis encompasses the analysis of TSN systems and their multi-objective design methods. TSN nodes are networked through interpolation and activation, similar to a gain-scheduling or local model/controller network. However, to achieve accuracy and ease of commissioning without requiring a large number of nodes, an algorithm has been developed first to identify optimum transition nodes within the entire operating envelope. Then the TSN approaches a nonlinear plant globally, not just locally, without requiring linearization. If desired or necessary, global optimisation provides an enhancement in the design process for TSNs. Since optimising only one aspect (a single objective) of performance while compromising others is undesirable, multi-objective designs have been developed concurrently to deliver or improve multiple aspects of performance. Following the development of a TSN, it is applied to nonlinear system modelling, and this TSN is termed a Trajectory-Scheduling Model (TSM). A TSM possesses the same properties and design features as the TSN generic framework. A nonlinear system, a coupled liquid-tank, is used to examine this modelling technique. Results verify the feasibility and effectiveness of the methods developed and validates the TSM. Further, the TSN technique is applied to nonlinear controller design, by way of a Trajectory-Scheduling Controller (TSC) network. It is illustrated through the design of a networked, easy-to-understand and easy-to-use PID control system for the coupled liquid-tank. Results show that the methods developed offer a high-performance linear control system with nonlinear capabilities to handle practical systems operating in a broad range and to cope with conflict between setpoint following at transient and disturbance rejection at steady state. This method is then applied to the PID network design problems for two nonlinear chemical processes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Modern Design of Classical Controllers

Classical controller design emphasizes simple low-order controllers. These classical controllers include Proportional-Integral (PI), Proportional-Integral-Derivative (PID), and First Order. In modern control theory, it is customary to design high-order controllers based on models, even for simple plants. However, it was shown that such controllers are invariably fragile, and this led to a renewal of interest in classical design methods. In the present research, a modern approach to the design of classical controllers (by introducing a complete stabilizing set in the space of the design parameters) is described. When classical specifications such as gain margin, phase margin, bandwidth, and time-delay tolerance are imposed, the achievable performance can be easily determined graphically. The objective of this research is to determine the controller gains, contained in the stabilizing set, which satisfy desired performance specifications such as crossover frequency and closed-loop stability margins. The design procedure starts with the calculation of the stabilizing set using recent methods. Then, a simple parametrization produces ellipses and straight lines (for PI controller design) and cylinders and planes (for PID controller design) in the space of controller gains. Each set of ellipses/cylinders and straight lines/planes represents constant magnitude and constant phase loci for the controller. The main result is that the crossing points, which are the intersection of ellipses/cylinders and straight lines/planes, are selected such that they are contained in the stabilizing set of controllers. They provide the controller gains that we need to satisfy our desired robust performance, seen as desired gain margin, phase margin, gain crossover frequency, and time-delay tolerance in our system. Then, using these crossing points contained in the stabilizing set, a new plot with information about the achievable performance in terms of gain margin, phase margin, and gain crossover frequency is constructed. Each point of this achievable performance can be used to retrieve the controller’s gains, which are contained in the stabilizing set. This result provides the possibility to analyze the system’s achievable performance by exploring the stabilizing set and considering different desired configurations in the performance capabilities for the system using a PI or PID controller. This expands our possibilities when designing controllers by considering different classical controller’s configurations. This research considers the discrete-time and continuous-time linear time invariant systems and cases including First Order with time-delay in the system, and the extension to the controller design for multivariable systems. Finally, the design procedure is illustrated with different examples and real applications for all such cases