On-line automatic controller tuning using Bayesian optimisation - a bulk tailings treatment plant case study

The automatic tuning problem of multiple-input-multiple-output (MIMO) controllers is considered within the framework of Bayesian optimisation and applied in simulation to a bulk tailings treatment process. The aim is to develop a model free, on-line, automatic tuner which can optimise the performance of a given controller to the task at hand. The automatic tuning procedure can be conducted during commissioning, when poor controller performance is observed or when the process has changed. Simulations indicate that the method is able to locate the optimal tuning parameters for the bulk tailings treatment process as compared to a de-coupled controller developed from a model of the process. The parameters were obtained from an objective function which was balanced and weighted according to the response required.https://www.journals.elsevier.com/ifac-papersonlineElectrical, Electronic and Computer Engineerin

Development and evaluation of methods for control and modelling of multiple-input multiple-output systems

In control, a common type of system is the multiple-input multiple-output (MIMO) system, where the same input may affect multiple outputs, or conversely, the same output is affected by multiple inputs. In this thesis two methods for controlling MIMO systems are examined, namely linear quadratic Gaussian (LQG) control and decentralized control, and some of the difficulties associated with them.One difficulty when implementing decentralized control is to decide which inputs should control which outputs, also called the input-output pairing problem. There are multiple ways to solve this problem, among them using gramian based measures, which include the Hankel interaction index array, the participation matrix and the Σ2 method.\ua0 These methods take into account system dynamics as opposed to many other methods which only consider the steady-state system. However, the gramian based methods have issues with input and output scaling. Generally, this is handled by scaling all inputs and outputs to have equal range. However, in this thesis it is demonstrated how this can cause an incorrect pairing. Furthermore, this thesis examines other methods of scaling the gramian based measures, using either row or column sums, or by utilizing the Sinkhorn-Knopp algorithm. It is shown that there are considerable benefits to be gained from the alternative scaling of the gramian based measures, especially when using the Sinkhorn-Knopp algorithm. The use of this method also has the advantage that the results are completely independent of the original scaling of the inputs and outputs.An expansion to the decentralized control structure is the sparse control, in which a decentralized controller is expanded to include feed-forward or MIMO blocks. In this thesis we explore how to best use the gramian based measures to find sparse control structures, and propose a method which demonstrates considerable improvement compared to existing methods of sparse control structure design.A prerequisite to implementing control configuration methods is an understanding of the processes in question. In this thesis we examine the pulp refining process and design both static and dynamic models for pulp and paper properties such as shives width, fiber length and tensile index, and various available inputs. We demonstrate that utilizing internal variables (primarily consistencies) estimated from temperature measurements yields improved results compared to using solely measured variables. The measurement data from the refiners is noisy, sometimes sparse and generally irregularly sampled. This thesis discusses the challenges posed by these constraints and how they can be resolved.\ua0\ua0 An alternative way to control a MIMO system is to implement an LQG controller, which yields a single control structure for the entire system using a state based controller. It has been proposed that LQG control can be an effective control scheme to be used on networked control systems with wireless channels. These channels have a tendency to be unreliable with packet delays and packet losses. This thesis examines how to implement an LQG controller over such unreliable communication channels, and derives the optimal controller minimizing the cost function expressed in actuated controls.When new methods of control system design and analysis are introduced in the control engineering field, it is important to compare the new results with existing methods. Often this requires application of the methods on examples, and for this purpose benchmark processes are introduced. However, in many areas of control engineering research the number of examples are relatively few, in particular when MIMO systems are considered. For a thorough assessment of a method, however, as large number of relevant models as possible should be used. As a remedy, a framework has been developed for generating linear MIMO models based on predefined system properties, such as model type, size, stability, time constants, delays etc. This MIMO generator, which is presented in this thesis, is demonstrated by using it to evaluate the previously described scaling methods for the gramian based pairing methods

Design of a Predictive PID Controller using Particle Swarm Optimization

The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds)

Design of a Predictive PID Controller using Particle Swarm Optimization

The proportional-integral-derivative (PID) controller is widely used in various industrial applications such as process control, motor drives, magnetic and optical memory, automotive, flight control and instrumentation. PID tuning refers to the generation of PID parameters (Kp, Ki, Kd) to obtain the optimum fitness value for any system. The determination of the PID parameters is essential for any system that relies on it to function in a stable mode. This paper proposes a method in designing a predictive PID controller system using particle swarm optimization (PSO) algorithm for direct current (DC) motor application. Extensive numerical simulations have been done using the Mathwork’s Matlab simulation environment. In order to gain full benefits from the PSO algorithm, the PSO parameters such as inertia weight, iteration number, acceleration constant and particle number need to be carefully adjusted and determined. Therefore, the first investigation of this study is to present a comparative analysis between two important PSO parameters; inertia weight and number of iteration, to assist the predictive PID controller design. Simulation results show that inertia weight of 0.9 and iteration number 100 provide a good fitness achievement with low overshoot and fast rise and settling time. Next, a comparison between the performance of the DC motor with PID-PSO, with PID of gain 1, and without PID were also discussed. From the analysis, it can be concluded that by tuning the PID parameters using PSO method, the best gain in performance may be found. Finally, when comparing between the PID-PSO and its counterpart, the PI-PSO, the PID-PSO controller gives better performance in terms of robustness, low overshoot (0.005%), low minimum rise time (0.2806 seconds) and low settling time (0.4326 seconds)

Intelligent methods for complex systems control engineering

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions

Integration of MATLAB and LabVIEW with Aspen Plus Dynamics: Using control strategies for a High-fidelity distillation column

The energy intensive distillation process has become a widely discussed topic as industry attempts to minimise energy consumption. The implementation of Model Predictive Control (MPC) can aid in the reduction of plant energy consumption. However, the leading chemical and petroleum software packages Aspen Plus and Aspen HYSYS do not currently support MPC. This project successfully integrated both MATLAB and LabVIEW with Aspen Plus Dynamics (APD), which enables the implementation of MPC schemes. This integration was established using Microsoft’s ActiveX Technology. In order to implement MPC from within MATLAB and LabVIEW, their respective MPC toolboxes were explored; these toolboxes possess several major flaws in their functionality. In particular, neither have the ability to perform RGA analysis or determine the model of the plant through data-driven modelling. To overcome these drawbacks a MATLAB script was developed which determines the model of the plant from automatic step tests in Simulink. Once the communication was established, and toolboxes documented, a high-fidelity distillation column was constructed in Aspen Plus before being exported to APD. This plant model was developed as a reference to compare the effectiveness of the PI and MPC control schemes, employing the Integral of Time-Weighted Absolute Error (ITAE) performance criterion. MPC outperformed the PI control schemes in all but one scenario. On average the ITAE values were 1000% lower for MPC, due to its ability to quickly track the set point and avoid overshoot. Further research has been highlighted on a number of toolbox features and dynamic communication options. Importantly, the use of the integrated software packages can provide a number of benefits for students and personnel. By developing a dynamic template it will be possible to implement these ideas into university, laboratory and workplace training. This could increase confidence in predictive control schemes, operator plant knowledge and reduce unsafe plant operation

Multivariable PID control by decoupling

This paper presents a new methodology to design multivariable PID controllers based on decoupling control. The method is presented for general n×n processes. In the design procedure, an ideal decoupling control with integral action is designed to minimize interactions. It depends on the desired open loop processes that are specified according to realizability conditions and desired closed loop performance specifications. These realizability conditions are stated and three common cases to define the open loop processes are studied and proposed. Then, controller elements are approximated to PID structure. From a practical point of view, the windup problem is also considered and a new anti-windup scheme for multivariable PID controller is proposed. Comparisons with other works demonstrate the effectiveness of the methodology through the use of several simulation examples and an experimental lab process

Controlling the non-parametric modeling of Double Link Flexible Robotic Manipulator using Hybrid PID tuned by P-Type ILA

Utilization of robotic manipulator with multi-link structure encompasses a great influence in most of the present industries. However, controlling the motion of multi-link manipulator has become a troublesome errand particularly once the flexible structure is employed. As of now, the framework utilizes the complicated arithmetic to resolve desired hub angle with the coupling result and vibration within the framework. Hence, this research aims to develop a dynamic system and controller for double-link flexible robotics manipulator (DLFRM) with the enhancement on hub angle position and vibration concealment. The research utilised neural network because the model estimation supported NARX model structure. In the controllers’ development, this research focuses on self-tuning controller. P-Type iterative learning algorithm (ILA) control theme was enforced to adapt the controller parameters to fulfill the required performances once there is changes to the system. The hybrid of proportional-integral-derivate (PID) controller was developed for hub motion and end-point vibration suppression of every link respectively. The controllers were tested in MATLAB/Simulink simulation setting. The performance of the controller was compared with the fixed hybrid PID-PID controller in term of input tracking and vibration concealment. The results indicated that the proposed controller was effective to maneuver the double-link flexible robotic manipulator to the specified position with reduction of the vibration at the tip of the DLFRM structure