C.J. Taylor

Non-linear control by input–output state variable feedback pole assignment

This article considers pole assignment control of non-linear dynamic systems described by state-dependent parameter (SDP) models. The approach follows from earlier research into linear proportional-integral-plus methods but, in SDP system control, the control coefficients are updated at each sampling instant on the basis of the latest SDP relationships. Alternatively, algebraic solutions can be derived off-line to yield a practically useful control algorithm that is relatively straightforward to implement on a digital computer, requiring only the storage of delayed system variables, coupled with straightforward arithmetic expressions in the control software. Although the analysis is limited to the case when the open-loop system has no zeros, time delays are handled automatically. This article shows that the closed-loop system reduces to a linear transfer function with the specified (design) poles. Hence, assuming pole assignability at each sample, global stability of the non-linear system is guaranteed at the design stage.

Modelling and PIP control design for open-top chambers

The paper first describes the identification of a control model for carbon dioxide concentration in an open-top chamber (OTC) used in plant physiology atmospheric change experiments. This model is then employed in the design of a gain-scheduled controller utilising the Proportional-Integral-Plus (PIP) control design methodology developed by Young et al. (1987). The system has been evaluated in a number of field trials, yielding good control, well within the required design specifications.

Proportional-integral-plus (PIP) control of the ALSTOM gasifier problem

Abstract Although it is able to exploit the full power of optimal state variable feedback within a non-minimum state-space (NMSS) setting, the proportional-integral-plus (PIP) controller is simple to implement and provides a logical extension of conventional proportional-integral and proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically by the NMSS formulation of the problem when the process is of greater than first order or has appreciable pure time delays. The present paper applies the PIP methodology to the ALSTOM benchmark challenge, which takes the form of a highly coupled multi-variable linear model, representing the gasifier system of an integrated gasification combined cycle (IGCC) power plant. In particular, a straightforwardly tuned discrete-time PIP control system based on a reduced-order backward-shift model of the gasifier is found to yield good control of the benchmark, meeting most of the specified performance requirements at three different operating points.

Proportional-integral-plus (PIP) control of ventilation rate in agricultural buildings

This paper is concerned with proportional-integral-plus (PIP) control of ventilation rate in mechanically ventilated agricultural buildings. The PIP controller can be interpreted as a logical extension of conventional proportional-integral/proportional-integral-derivative (PI/PID) controllers, but with inherent model-based predictive control action. In particular, the paper considers the design of an optimal, scheduled gain PIP algorithm for a 22 m3 forced ventilation test chamber at the Katholieke Universiteit Leuven. Such a PIP approach proves more robust to pressure disturbances than an equivalent PID design and constitutes a preliminary step towards the development of the complete micro-climate controller.

Proportional-integral-plus (PIP) control of time delay systems:

Abstract The paper shows that the digital proportional-integral-plus (PIP) controller formulated within the context of non-minimum state space (NMSS) control system design methodology is directly equivalent, under certain non-restrictive pole assignment conditions, to the equivalent digital Smith predictor (SP) control system for time delay systems. This allows SP controllers to be considered within the context of NMSS state variable feedback control, so that optimal design methods can be exploited to enhance the performance of the SP controller. Alternatively, since the PIP design strategy provides a more flexible approach, which subsumes the SP controller as one option, it provides a superior basis for general control system design. The paper also discusses the robustness and disturbance response characteristics of the two PIP control structures that emerge from the analysis and demonstrates the efficacy of the design methods through simulation examples and the design of a climate control system for a large horticultural glasshouse system.

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

Abstract 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.

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

ABSTRACT This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual–manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of ‘true’ parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

Comparison of classical and modern control applied to an excavator-arm

Abstract The excavation of foundations, general earthworks and earth removal tasks are activities which involve the machine operator in a series of repetitive operations. Automation is likely to provide a number of benefits such as improving efficiency, quality and safety. However, a persistent stumbling block for system developers is the achievement of fast smooth movement of the excavator arm under automatic control. In this regard, the paper develops two very different design methods, a model-based, full state feedback approach and a classical frequency domain technique based on the Nichols chart. The advantages and limitations of these contrasting approaches are identified in terms of both performance and design effort.

Model development and energy management control for hybrid electric race vehicles

A Hybrid Electric Vehicle longitudinal dynamics model for the control of energy management is developed. The model is implemented using Simulink® and consists of a transitional vehicle speed input parameterized by, for example, the New European Driving Cycle. It is a backward looking model in that engine and motor on/off states are determined by the controller, dependent on wheel torque requirements and output targets. The objective of the simulation is to calculate tractive effort and resistance forces to determine longitudinal net vehicle force at the road. This article addresses model development and initial investigations of its dynamic behaviour in order to establish appropriate energy management strategies for the Hybrid Electric system. In particular, All Wheel Drive, Front Wheel Drive and Rear Wheel Drive drivetrain architectures are evaluated to determine minimum fuel usage and battery state of charge. The use of a logic controller allows a reduction of simulation time and ensures accurate results for charge depletion and harvesting. Simulated fuel consumption is within 1% of actual usage.

A new approach to improve the parameter estimation accuracy in robotic manipulators using a multi-objective output error identification technique

The research behind this article primarily concerns the development of mobile robots for nuclear decommissioning. The robotic platform under study has dual, seven-function, hydraulically actuated manipulators, for which the authors are developing a vision based, assisted teleoperation interface for common decommissioning tasks such as pipe cutting. However, to improve safety, task execution speed and operator training-time, high performance control of the nonlinear manipulator dynamics is required. Hence, the present article focuses on an associated dynamic model, and addresses the challenging generic task of parameter estimation for a highly non-convex and nonlinear system. A novel approach for estimation of the fundamental parameters of the manipulator, based on the idea of multi-objectivization, is proposed. Here, a single objective output error identification problem is converted into a multi-objective optimization problem. This is solved using a multi-objective genetic algorithm with non-dominated sorting. Numerical and experimental results using the nuclear decommissioning robot, show that the performance of the proposed approach, in terms of both the output error index and the accuracy of the estimated parameters, is superior to the previously studied single-objective identification problem.