Proportional-Integral-Plus Research Articles

Implementation of proportional-integral-plus control to a high redundancy actuator for a track switch system

The railway network is equipped with track switches that enable trains to move from one track to another. Existing track switch actuation is performed in open loop. Closed-loop control and redundant actuation can be applied to provide an opportunity to enhance the reliability of the railway track switch system. This paper looks at the control design development applied to a laboratory scale demonstrator of a redundant actuation concept. Open-loop control is first applied to this High Redundancy Actuation (HRA) track switch lab prototyping. According to the open-loop experimental input and output, a simplified linear model of the system is identified and estimated. A model-based Proportional-Integral-Plus (PIP) controller is then developed and tested, including nominal control and fault tolerant control. The results show that the proposed control method can meet the actuation requirements of the track switch system with high redundancy actuation. Another innovation of this paper is direct model-based control design, and this small HRA prototyping provides valuable insight into the future full-scale system.

The Multivariable Control of Carbon Dioxide Absorption System using the Proportional Integral Plus (PIP) Controller

The present article investigates the implementation of non-minimal state space (NMSS) representation with proportional-integral-plus (PIP) controller for the carbon dioxide absorption process of Shiraz petrochemical ammonia unit. The PIP controller is a logical extension of conventional PI/PID controllers with additional dynamic feedback and input compensators. PIP controller is used for multivariable control without limitation on the number of controlled variables. A Multi Input - Multi Output (MIMO) square model was extracted from step response test. In this way, input water flow rate to carbon dioxide absorption system, the heat duty of input absorbent cooler to tray (1) of absorption tower and re-boiler heat duty of stripping tower are chosen as manipulated variables (inputs), while carbon dioxide mole fraction in absorption tower vapor product, the water mole fraction in absorption tower liquid product and tray temperature No. 36 of stripping tower are determined as controlled ones (outputs). The system identification is performed with three input and three output variables using step response test. As a result, continuous and discrete time transfer function matrices and suitable NMSS model for PIP controller are reported. Finally, in order to evaluate the PIP control performance, the feed flow rate increases by 2%. The results show the proper performance of designed PIP controller for both disturbance rejection and set point tracking.

Analysis and Design of PI Plus Repetitive Control for Grid-Side Converters of Direct-Drive Wind Power Systems Considering the Effect of Hardware Sampling Circuits

In inverters based on a single proportional-integral (PI) or deadbeat (DB) controller, an inherent resonance peak may emerge near their current loop cut-off frequency, which results in harmonic amplification or even resonance. Additionally, inappropriate filter circuits implemented in sampling circuits may result in the expansion of the resonance peak. Thus, this paper further investigates the influence of the sampling circuits on a PI- or DB-based control loop. Then, the RC filter in the sampling circuit is designed to reduce the inherent resonance peak. Moreover, a compound control strategy based on an improved repetitive controller (IRC) plus a PI controller is adopted for the grid-side converter of a direct-drive wind system. This strategy enhances the harmonic and reactive compensation performance by reconstructing the internal model of the classic repetitive controller (CRC) and limiting the bandwidth of the PI-based loop to a low level. The parameters of the presented IRC-plus-PI control are designed for the purpose of resonance peak elimination and system stability. Furthermore, the non-integer delay problem is solved with an inserted fraction compensator (FC), which plays the role of a low-pass filter in the IRC. Finally, the feasibility and effectiveness of the presented control method is verified by the experimental results.

Digital control design for an IPMC actuator using adaptive optimal proportional integral plus method: Simulation and experimental study

In this paper, the application of a novel digital control method, called adaptive optimal proportional-integral-plus (AOPIP), to beam-like ionic polymer-metal composite (IPMC) actuators is investigated. First, as a gray-box system, the dynamical model of the IMPC cantilevers is obtained and then AOPIP method is derived and applied to several IPMCs with different physical properties to evaluate the efficacy of the control method. Basically, AOPIP is a proportional-integral-plus (PIP) controller with two additional terms; an optimal gain tuner mechanism with means of linear quadratic regulator (LQR) method and an adaptation term based on recursive least square (RLS) method with Kalman filter (KF), RLS-KF. Therefore, the main advantages of this controller are a) independence of parameters of the system; and b) an optimal performance. To perform a comparative study, in addition to AOPIP, a digital PID controller is designed and applied to the system in simulation. Besides, the comparison of opened-loop techniques to closed-loop ones is investigated as well. Furthermore, an experimental test for comparing AOPIP and digital PID is performed to show the performance of the IPMC in real-time, and under real conditions such as existence of external noise and disturbances. Using several proper numerical indicators, both simulation and experimental results demonstrate the efficacy of the AOPIP and its superiority to the opened-loop techniques and digital PID controller in terms of control effort, desired tracking precision of the IPMCs actuators and its optimality.

Optimal PIP Control of a pH Neutralization Process Based on State-Dependent Parameter Model

In this paper, an optimal Proportional-Integral-Plus (PIP) controller based on State-Dependent Parameter (SDP) model is developed to control a highly nonlinear and time-varying pH neutralization process. Since the reaction invariants of the pH process are unavailable for direct measurement, a state observer is required for their estimation. Unfortunately, a closed-loop state observer cannot be designed for the pH process due to the decoupled nature of the reaction invariant dynamics. However, this problem is circumvented by engaging the power of State Variable Feedback (SVF) in the context of a Non-Minimum State Space (NMSS) framework. Furthermore, to compensate for process nonlinearities and unmeasured disturbances, an SDP model is identified using data collected from open-loop experiment, and a straightforwardly tuned optimal PIP controller is subsequently designed based on the SDP model to obtain the SDP-PIP controller. For benchmark purposes, a digital PI controller is designed and results are compared with those from the SDP-PIP controller. Simulation results show that SDP-PIP outperforms PI both in set point and disturbance changes.

Nonsquare multivariable non-minimal state space-proportional integral plus (NMSS-PIP) control for atmospheric crude oil distillation column

The goal of this paper is to show implementation of non-minimal state space (NMSS) representation with proportional-integral-plus (PIP) controller for industrial crude distillation column. The control structure was designed and developed for MIMO nonsquare configuration. Weighting matrices of LQ optimal PIP control structure were achieved based on ITAE criteria for the column. The control performance of PIP control was compared with conventional PI control with different scenarios. The results show that PIP control handles the process interactions truly in order to utilize all variables simultaneously. In feed composition change scenario, the PIP control was stable and behaved robustly while the PI control had some difficulties. Furthermore, the proposed control structure indicates efficient performance for both disturbance rejection and set point tracking.

Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units

Abstract This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit.

Improved control of individual blade pitch for wind turbines

This paper is concerned with improved control of individual blade pitch for wind turbines. The controller is based on the proportional-integral-plus (PIP) algorithm, considered an extension of conventional proportional-integral/proportional-integral-derivative (PI/PID) controllers, in which full state variable feedback control is implemented directly from the measured input and output signals. In particular, the design and implementation of a PIP algorithm based on either standard or advanced individual blade pitch control is presented in the paper. The preliminary results reported in this article, based upon simulations using a linear transfer function representation of the pitch control system, have demonstrated that the proposed approach is robust to stochastic influences arising from both parametric uncertainty and system disturbance. The work presented in this paper constitutes a step towards the development of an advanced individual blade pitch controller.

Linear and nonlinear control of a robotic excavator

Various control systems for a robotic excavator named LUCIE (Lancaster University Computerized and Intelligent Excavator), were investigated. The excavator is being developed to dig trenches autonomously. One stumbling block is the achievement of adequate, accurate, quick and smooth movement under automatic control. Here, both classical and modern approaches are considered, including proportional-integral-derivative (PID) control tuned by conventional Zigler-Nichols rules, linear proportional-integral-plus (PIP) control, and a novel nonlinear PIP controller based on a state-dependent parameter (SDP) model structure, in which the parameters are functionally dependent on other variables in the system. Implementation results for the excavator joint arms control demonstrate that SDP-PIP controller provides the improved performance with fast, smooth and accurate response in comparison with both PID and linearized PIP control.

Improved control of intelligent excavator using proportional-integral-plus gain scheduling

Consider the design and implementation of an electro-hydraulic control system for a robotic excavator, namely the Lancaster University computerized and intelligent excavator (LUCIE). The excavator was developed to autonomously dig trenches without human intervention. One stumbling block is the achievement of adequate, accurate, quick and smooth movement under automatic control, which is difficult for traditional control algorithm, e.g. PI/PID. A gain scheduling design, based on the true digital proportional-integral-plus (PIP) control methodology, was utilized to regulate the nonlinear joint dynamics. Simulation and initial field tests both demonstrated the feasibility and robustness of proposed technique to the uncertainties of parameters, time delay and load disturbances, with the excavator arm directed along specified trajectories in a smooth, fast and accurate manner. The tracking error magnitudes for oblique straight line and horizontal straight line are less than 20 mm and 50 mm, respectively, while the velocity reaches 9 m/min.

Proportional-Integral-Plus Control of Robotic Excavator Arm Utilising State-Dependent Parameter Model

This paper is concerned with the design of a control system for a robotic excavator known as the Lancaster University Computerised and Intelligent Excavator (LUCIE). A nonlinear proportional-integral-plus (PIP) control algorithm is developed for regulating movement of excavator arm. The nonlinear dynamics of the hydraulic driven arm is represented using the quasi-linear state-dependent parameter (SDP) model, in which the parameters are functionally dependent on other variables in the system. The model is subsequently utilised to develop a new approach to control system design, based on nonlinear PIP pole assignment. Implementation results demonstrate improved tracking performance of excavator arm in comparison with both linear proportional-integral-derivative (PID) and conventional (linearised) fixed-gain PIP control.

Forward path model predictive control using a non-minimal state-space form

This paper considers model predictive control (MPC) using a non-minimal state-space (NMSS) form, in which the state vector consists only of the directly measured system variables. Two control structures emerge from the analysis, namely the conventional feedback form and an alternative forward path structure. There is a close analogy with proportional-integral-plus (PIP) control system design, which is also based on the definition of an NMSS model with two control structures. However, the MPC/NMSS approach has the advantage of handling system constraints at the design stage. Also, since the NMSS model is obtained directly from the identified transfer function model, the covariance matrix for the parameter estimates can be used to evaluate the robustness of the predictive control system to model uncertainty using Monte Carlo simulation. The effectiveness of the approach is demonstrated by means of simulation examples, including the IFAC′93 benchmark and the ALSTOM non-linear gasifier problem. For the simulation examples considered here, the forward path form preserves the good performance properties of the original MPC/NMSS controller, while at the same time yielding improved robustness.

Development of an automated verticality alignment system for a vibro-lance

This paper describes the automation of a vibro-lance for ground improvement on a construction site. Here, a vibrating probe is lowered into the ground and penetrates downwards by means of a two-arm excavator, compacting the surrounding soil. The control system is straightforward to install on a conventional, hydraulically controlled excavator, such as the one utilised for the field tests reported in the paper. The new system supports the operator by automatically maintaining the verticality of the probe, increasing the speed of operation by a factor of three, whilst also yielding improved accuracy and, therefore, a potential increase in tool life. In particular, the research demonstrates the successful design and implementation of Proportional-Integral-Plus (PIP) control systems for joint control on-site. To the authors' knowledge, this represents the first operational use of automation for vibro-lance ground compaction.

Development of a Multi-Arm Mobile Robot for Nuclear Decommissioning Tasks

This paper concerns the design of a two-arm mobile delivery platform for application within nuclear decommissioning tasks. The adoption of the human arm as a model of manoeuvrability, scale and dexterity is the starting point for operation of two seven-function arms within the context of nuclear decommissioning tasks, the selection of hardware and its integration, and the development of suitable control methods. The forward and inverse kinematics for the manipulators are derived and the proposed software architecture identified to control the movements of the arm joints and the performance of selected decommissioning tasks. We discuss the adoption of a BROKK demolition machine as a mobile platform and the integration with its hydraulic system to operate the two seven-function manipulators separately. The paper examines the modelling and development of a real-time control method using Proportional-Integral-Derivative (PID) and Proportional-Integral-Plus (PIP) control algorithms in the host computer with National Instruments functions and tools to control the manipulators and obtain feedback through wireless communication. Finally we consider the application of a third party device, such as a personal mobile phone, and its interface with LabVIEW software in order to operate the robot arms remotely.

Proportional-integral-plus control applications of state-dependent parameter models

This paper considers proportional-integral-plus (PIP) control of non-linear systems defined by state-dependent parameter models, with particular emphasis on three practical demonstrators: a microclimate test chamber, a 1/5th-scale laboratory representation of an intelligent excavator, and a full-scale (commercial) vibrolance system used for ground improvement on a construction site. In each case, the system is represented using a quasi-linear state-dependent parameter (SDP) model structure, in which the parameters are functionally dependent on other variables in the system. The approach yields novel SDP-PIP control algorithms with improved performance and robustness in comparison with conventional linear PIP control. In particular, the new approach better handles the large disturbances and other non-linearities typical in the application areas considered.

Non-linear Control of Ventilation Rate using State-dependent Parameter Models

The objective of this paper is to develop non-linear proportional-integral-plus (PIP) control algorithms for regulating ventilation rate in mechanically ventilated agricultural buildings. State-dependent parameter (SDP) models are developed for an environmental test chamber, representing a section of a livestock building or glasshouse. Here, the system is modelled using the quasi-linear SDP model structure in which the parameters are functionally dependent on other variables in the system. The model is subsequently utilised to develop a new approach to control system design, based on non-linear PIP pole assignment, with a discrete-time Smith Predictor to handle the sampled time delays. Implementation results for the test chamber demonstrate improved control of ventilation rate, with a faster response to disturbances in comparison with both linear and conventional (linearised) scheduled PIP control. The approach has application to a wide class of other non-linear systems, as demonstrated by simulation examples.

Multivariable proportional-integral-plus (PIP) control of the ALSTOM nonlinear gasifier simulation

Multivariable proportional-integral-plus (PIP) control methods are applied to the nonlinear ALSTOM Benchmark Challenge II. The approach utilises a data-based combined model reduction and linearisation step, which plays an essential role in satisfying the design specifications. The discrete-time transfer function models obtained in this manner are represented in a non-minimum state space form suitable for PIP control system design. Here, full state variable feedback control can be implemented directly from the measured input and output signals of the controlled process, without resorting to the design and implementation of a deterministic state reconstructor or a stochastic Kalman filter. Furthermore, the non-minimal formulation provides more design freedom than the equivalent minimal case, a characteristic that proves particularly useful in tuning the algorithm to meet the Benchmark specifications. The latter requirements are comfortably met for all three operating conditions by using a straightforward to implement, fixed gain, linear PIP algorithm.

The data-based mechanistic approach to the modelling, forecasting and control of environmental systems

The paper presents a unified approach to the modelling, forecasting and control of natural and man-made environmental systems. The modelling approach exploits the author’s Data-Based Mechanistic (DBM) modelling philosophy, combined with powerful methods of recursive statistical estimation. These provide the basis for two major stages of model building: first, the critical evaluation of the over-parametrized simulation models that are currently the most common vehicle used in environmental planning and management studies; and second, the adaptive, data-based estimation of parsimonious, ‘top-down’ models that can be used for adaptive forecasting and data assimilation, as well as operational control and management system design. The associated control system design methodology is based on the Non-Minimal State Space (NMSS) approach to the design of Proportional-Integral-Plus (PIP) control systems, based on the DBM models obtained at the previous modelling stage. The paper includes a case study concerned with the modelling and control of globally averaged levels of CO 2 in the atmosphere.

Cost effective combined axial fan and throttling valve control of ventilation rate

This paper is concerned with Proportional-Integral-Plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. In particular, it develops a unique fan and throttling valve control system for a 22m3 test chamber, representing a section of a livestock building or glasshouse, at the Katholieke Universiteit Leuven. Here, the throttling valve is employed to restrict airflow at the outlet, so generating a higher static pressure difference over the control fan. In contrast with previous approaches, however, the throttling valve is directly employed as a second control actuator, utilising airflow from either the axial fan or natural ventilation. The new combined fan/valve configuration is compared with a commercially available PID-based controller and a previously developed scheduled PIP design, yielding a reduction in power consumption in both cases of up to 45%.

Macroscopic traffic flow modelling and ramp metering control using Matlab/Simulink

Computer programs to simulate traffic flow offer an opportunity to evaluate new strategies for reducing delays, congestion, fuel consumption and pollution. This paper describes a Statistical Traffic Model or STM, which is based on accepted macro-modelling concepts, such as the conservation of vehicles and the fundamental traffic diagram. In this case, the model is constructed using the well known Matlab/Simulink ™ software package, so providing an integrated approach for data processing, graphical presentation of data, control system design and macroscopic simulation in one straightforward to use, widely available environment. To illustrate the methodology, the STM is applied to a section of the M3/M27 Ramp Metering Pilot Scheme in the UK. This Highways Agency sponsored project, based in the Southampton area, utilises traffic lights at the on-ramp entrances to regulate access to the main carriageway of the motorway, in an attempt to maintain flow close to the capacity. The paper utilises the model to help design a locally-coordinated ramp metering algorithm, based on proportional-integral-plus (PIP) control methods. In this manner, the STM proves particularly valuable for the application of multi-objective optimisation techniques in the design of new traffic management systems.