Robust PID tuning. Application to a Mobile Robot Pathtraking problem.

IFAC Digital Control: Past,Present and Future of PlO Control.Terrassa.Spain.2000This paper presents a methodology for tuning PIDs considering the nominal performance and the robustness as control specifications. The synthesis procedure is similar to the Ziegler-Nichols method for PID controllers and can be easily used for industrial processes. As a workbench for testing the PID controller a mobile robot has been used. The path tracking problem of a mobile robot has been used as a workbench for testing the PID controller

On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes

In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modeling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.Comment: 27 pages, 10 figure

A Robust Adaptive Dead-Time Compensator with Application to A Solar Collector Field

This paper describes an easy-to-use PI controller with dead-time compensation that presents robust behaviour and can be applied to plants with variable dead-time. The formulation is based on an adaptive Smith predictor structure plus the addition of a filter acting on the error between the output and its prediction in order to improve robustness. The implementation of the control law is straightforward, and the filter needs no adjustment, since it is directly related to the plant dead-time. An application to an experimentally validated nonlinear model of a solar plant shows that this controller can improve the performance of classical PID controllers without the need of complex calculations.Ministerio de Ciencia y Tecnología TAP95-37

Fractional order control for the temperature loop in a chemical reactor

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Robust PI Controller Design Satisfying Gain and Phase Margin Constraints

This paper presents a control design algorithm for determining PI-type controllers satisfying specifications on gain margin, phase margin, and an upper bound on the (complementary) sensitivity for a finite set of plants. Important properties of the algorithm are: (i) it can be applied to plants of any order including plants with delay, unstable plants, and plants given by measured data, (ii) it is efficient and fast, and as such can be used in near real-time to determine controller parameters (for on-line modification of the plant model including its uncertainty and/or the specifications), (iii) it can be used to identify the optimal controller for a practical definition of optimality, and (iv) it enables graphical portrayal of design tradeoffs in a single plot (highlighting tradeoffs among the gain margin, complementary sensitivity bound, low frequency sensitivity and high frequency sensor noise amplification)

Design of PID Controllers Satisfying Gain Margin and Sensitivity Constraints on a Set of Plants

This paper presents a method for the design of PID-type controllers, including those augmented by a filter on the D element, satisfying a required gain margin and an upper bound on the (complementary) sensitivity for a finite set of plants. Important properties of the method are: (i) it can be applied to plants of any order including non-minimum phase plants, plants with delay, plants characterized by quasi-polynomials, unstable plants and plants described by measured data, (ii) the sensors associated with the PI terms and the D term can be different (i.e., they can have different transfer function models), (iii) the algorithm relies on explicit equations that can be solved efficiently, (iv) the algorithm can be used in near real-time to determine a controller for on-line modification of a plant accounting for its uncertainty and closed-loop specifications, (v) a single plot can be generated that graphically highlights tradeoffs among the gain margin, (complementary) sensitivity bound, low-frequency sensitivity and high-frequency sensor noise amplification, and (vi) the optimal controller for a practical definition of optimality can readily be identified

Robust PI Controller Design Satisfying Sensitivity and Uncertainty Specifications

This paper presents a control design method for determining proportional-integral-type controllers satisfying specifications on gain margin, phase margin, and an upper bound on the (complementary) sensitivity for a finite set of plants. The approach can be applied to plants that are stable or unstable, plants given by a model or measured data, and plants of any order, including plants with delays. The algorithm is efficient and fast, and as such can be used in near real-time to determine controller parameters (for online modification of the plant model including its uncertainty and/or the specifications). The method gives an optimal controller for a practical definition of optimality. Furthermore, it enables the graphical portrayal of design tradeoffs in a single plot, highlighting the effects of the gain margin, complementary sensitivity bound, low frequency sensitivity and high frequency sensor noise amplification

Performance-based control system design automation via evolutionary computing

This paper develops an evolutionary algorithm (EA) based methodology for computer-aided control system design (CACSD) automation in both the time and frequency domains under performance satisfactions. The approach is automated by efficient evolution from plant step response data, bypassing the system identification or linearization stage as required by conventional designs. Intelligently guided by the evolutionary optimization, control engineers are able to obtain a near-optimal ‘‘off-thecomputer’’ controller by feeding the developed CACSD system with plant I/O data and customer specifications without the need of a differentiable performance index. A speedup of near-linear pipelineability is also observed for the EA parallelism implemented on a network of transputers of Parsytec SuperCluster. Validation results against linear and nonlinear physical plants are convincing, with good closed-loop performance and robustness in the presence of practical constraints and perturbations