Kritchai Witheephanich

Control for a Variable Speed Wind Turbine Equipped with a Permanent Magnet Synchronous Generator (PMSG)

In wind turbine systems, one of the operational problems is the unpredictable factor of wind. This leads the quality of produced energy becomes an important problem in the system. Several control techniques have been applied to improve the quality of power generated from wind turbines. Pitch control is the most efficient and popular power control method, especially for variable-speed wind turbines. In this paper, a traditional PI controller is developed. Simulations were carried out to validate the proposed control design when realistic data of wind speed collected in Borj Cedria, Tunissia, are taken into account.

A Min–Max Model Predictive Control Approach to Robust Power Management in Ambulatory Wireless Sensor Networks

This paper addresses the problem of transmission power control within a network of resource-constrained wireless sensors that operate within a particular ambient healthcare environment. Sensor data transmitted to a remote base station within the network arrive subject to node location, orientation, and movement. Power is optimally allocated to all channels using a novel resource efficient algorithm. The proposed algorithm is based on a computationally efficient min-max model predictive controller that uses an uncertain linear state-space model of the tracking error that is estimated via local received signal strength feedback. An explicit solution for the power controller is computed offline using a multiparametric quadratic solver. It is shown that the proposed design leads to a robust control law that can be implemented quite readily on a commercial sensor node platform where computational and memory resources are extremely limited. The design is validated using a fully IEEE 802.15.4 compliant testbed using Tmote Sky sensor nodes mounted on fully autonomous MIABOT Pro miniature mobile robots. A repeatable representative selection of scaled ambulatory scenarios is presented that is quite typical of the data that will be generated in this space. The experimental results illustrate that the algorithm performs optimal power assignments, thereby ensuring a balance between energy consumption and a particular outage-based quality of service requirement while robustly compensating for disturbance uncertainties such as channel fading, interference, quantization error, noise, and nonlinear effects.

Ambulatory wireless sensor network power management using constrained explicit generalised predictive control

This work considers the problem of controlling transmit power within a wireless sensor network (WSN), where the practical constraints typically posed by an ambulatory healthcare setting are explicitly taken into account, as a constrained received signal strength indicator (RSSI) tracking control problem. The problem is formulated using an explicit generalised predictive control (GPC) strategy for dynamic transmission power control that ensures a balance between energy consumption and quality of service (QoS) through the creation of a stable floor on information throughput. Optimal power assignment is achieved by an explicit solution of the constrained GPC problem that is computed off-line using a multi-parametric quadratic program (mpQP). The solution is shown to be a piecewise-affine function. The new design is demonstrated to be practically feasible via a resource-constrained, fully IEEE 802.15.4 compliant, Moteivs Tmote Sky sensor node platform. Design utility is benchmarked experimentally using a representative selection of scaled ambulatory scenarios.

Explicitly constrained generalised predictive control strategies for power management in ambulatory wireless sensor network systems

This paper develops an explicit generalised predictive control (GPC) strategy for a wireless sensor network (WSN) power control problem that addresses practical constraints typically posed by an ambulatory healthcare problem scenario. An explicit solution of the particular GPC problem that arises facilitates the implementation of traditional on-line optimisation control strategies on a commercial sensor node platform with limited computational performance. The problem is shown to reduce to the evaluation of a piecewise linear function that can offer good performance with existing Mote type devices. The control law is validated experimentally against a number of existing strategies using a scaled, fully IEEE 802.15.4 compliant, testbed that emulates a selection of realistic wireless healthcare scenarios.

Robust Controller Design for Plant Uncertainty

This paper demonstrates how robust nominal model following control (RNMFC) motivated by the adaptive model following concept is developed and proposed in this paper. The control structure of RNMFC is quite different from and much simpler than those of adaptive model following control schemes. RNMFC has three main features: the use of a nominal model of the plant as a reference model, the design of a model controller which fulfills the reference tracking requirement and the inclusion of a simple PID correction mechanism which copes with all dynamic deviations of the real plant from its nominal model. With its robust control structure, RNMFC separates the reference tracking and robustness fulfillment into two independent problems. The results illustrate the robustness of RNMFC that can be manipulated parametric uncertainty of motor servo system

Representations of robustly stabilizing controller structure for distributed control architectures with plant uncertainties

There is increasing interest in controlling systems over communication networks. Using a robust model following control (RMFC) scheme, this paper proposes robustly stabilizing loop controller designed and then added to local location of a distributed system to cope with the plant uncertainties. A systematic design methodology is established and it is linked to the conventional control system design. The robust loop controller designs for differently distributed architectures are shown to illustrate the benefits of the proposed technique with a number of simulation examples.

Robust controller synthesis for simultaneous plant family stabilization

This paper demonstrates how finite inclusions theorem (FIT) synthesis can enhance the robustness properties of nominal controller designs. FIT used to generate a simultaneous plant family stabilization algorithm. Major objective is to expand the controller synthesis methodology and create an algorithm to synthesize a single, simultaneously stabilizing controller for a finite set of plant families. Finally, the capabilities of this controller synthesis tool will be demonstrated in example. The example is shown how FIT synthesis can be used to achieve robustness properties by redesigning an initial controller.

Min-max model predictive control with robust zonotope-based observer

This paper considers the problem of robust estimation and constrained model predictive control (MPC). The paper deals with a discrete linear time-invariant system affected by additive bounded disturbances, whose states are measurable, but not directly accessible. In order to improve the control performance, a state estimator is desirable. The design problem of an observer based on zonotopes to estimate the system states of the uncertain system is addressed. Then, the min-max MPC optimisation problem formulation based on the designed robust observer as a quadratic program (QP) is described. An efficient implementation of the proposed robust observer-based control algorithm that can be solved by a standard QP is validated by simulation through the regulator problem of a cart pendulum system.

Fuzzy Model Based Predictive Control of Reaction Temperature in a Pilot Plant

A fuzzy model with reduced complexity has been developed to capture the nonlinear dynamics of a pilot plant in which the temperature of a reactor is controlled. The use of Functional Principal Component Analysis provides an ability to reduce the complexity of the model permitting the application of linear MPC for the nonlinear control problem.

Input variables selection of fuzzy dynamic models by using genetic algorithm

In this paper, an input selection technique for fuzzy-based dynamic modelling is presented. When a system is composed of a large number of input-output variables with strong coupled dynamics, a systematic method is required in order to minimise the modelling error through the selection of proper variables. The selection approach is based on a Genetic algorithm optimisation to obtain solutions as binary strings that effectively determine a proper input field structure.