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11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010, Singapore, 7-10 December 2010, Proceedings; 01/2010
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ABSTRACT: We address the distributed model predictive control (MPC) for a set of linear local systems with decoupled dynamics and a coupled global cost function. By the decomposition of the global cost function, the distributed control problem is converted to the MPC for each local system associated with a cost involving neighboring system states and inputs. For each local controller, the infinite horizon control moves are parameterized as N free control moves followed by a single state feedback law. An interacting compatibility condition is derived, disassembled and incorporated into the design of each local control so as to achieve the stability of the global closed-loop system. Each local system exchanges with its neighbors the current states and the previous optimal control strategies. The global closed-loop system is shown to be exponentially stable provided that all the local optimizers are feasible at the initial time. Copyright © 2009 John Wiley & Sons, Ltd.
International Journal of Robust and Nonlinear Control 09/2009; 20(11):1285 - 1298. · 1.55 Impact Factor
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ABSTRACT: This paper studies the stabilization problem of networked control systems (NCSs) with bounded random time-varying delays. Both the uncertain time delay of the control channel and the known time-varying delay of the sensor channel are taken into consideration. This networked control system is formulated into a system with polytopic parameter uncertainties and a Markovian jump parameter. A mode-dependent Lyapunov-Krasovskii functional is used to design a mode-dependent static output feedback controller which stabilizes the NCS. A sufficient condition for stabilization is proposed via a parameterized bilinear matrix inequality (BMI) based approach. A numerical example is given to illustrate the effectiveness of the proposed method.
Control, Automation, Robotics and Vision, 2008. ICARCV 2008. 10th International Conference on; 01/2009
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ABSTRACT: In the centralized heating, ventilating and air-conditioning (HVAC) system, air handling units (AHUs) are traditionally controlled by single-loop proportional-integral-derivative (PID) controllers. The control structure is simple, but the performance is usually not satisfactory. In this paper, we propose a cascade control strategy for temperature control of AHU. Instead of a fixed PID controller in the classical cascade control scheme, a neural network (NN) controller is used in the outer control loop. This approach not only overcomes the tedious tuning procedure for the inner and outer loop PID parameters of a classical cascade control system, but also makes the whole control system be adaptive and robust. The multilayer NN is trained online by a special training algorithm-simultaneous perturbation stochastic approximation (SPSA)-based training algorithm. With the SPSA-based training algorithm, the weight convergence of the NN and stability of the control system is guaranteed. The novel cascade control system has been implemented on an experimental HVAC system. Testing results demonstrate the effectiveness of the proposed algorithm over the classical cascade control system
IEEE Transactions on Industrial Electronics 03/2007; · 5.16 Impact Factor
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ABSTRACT: In this paper, a novel two-layer online auto-tuning algorithm is presented for a nonlinear time-varying system. The lower layer consists of a conventional proportional-integral-derivative (PID) controller and a plant process, while the upper layer is composed of identification and tuning modules. The purpose of the upper layer is to find a set of optimal PID parameters for the lower layer via an online receding horizon optimization approach, which result in a time-varying PID controller. Through mathematical analysis, the proposed system performance is equivalent to that of a standard generalized predictive control. Simulation and experiment demonstrate that the new method has a better control system performance compared with conventional PID controllers.
ISA Transactions 11/2005; 44(4):491-500. · 1.11 Impact Factor
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ABSTRACT: This paper proposes a cascade model predictive control scheme for boiler drum level control. By employing generalized predictive control structures for both inner and outer loops, measured and unmeasured disturbances can be effectively rejected, and drum level at constant load is maintained. In addition, nonminimum phase characteristic and system constraints in both loops can be handled effectively by generalized predictive control algorithms. Simulation results are provided to show that cascade generalized predictive control results in better performance than that of well tuned cascade proportional integral differential controllers. The algorithm has also been implemented to control a 75-MW boiler plant, and the results show an improvement over conventional control schemes.
ISA Transactions 08/2005; 44(3):399-411. · 1.11 Impact Factor
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ABSTRACT: This paper is concerned with air handling units (AHUs), the performances of which directly influence those of heating, ventilation and air conditioning systems. An autotuning receding-horizon optimization method is proposed to synthesize a proportional−integral−derivative (PID) type controller for AHUs. This algorithm is composed of two levels of control. The lower level adopts a conventional PID controller to obtain an acceptable, but not necessarily optimal, performance. The higher level provides optimal low-level controller parameters through minimization of the generalized predictive control criterion. Because the method does not require changes in hardware and the definitions of conventional controller parameters, it can be both easily accepted by process engineers and widely applied to industrial areas. Compared with the performance of a well-tuned conventional PID controller, simulation and experimental results show that the proposed method for AHU systems can achieve a better performance under a wide range of operating conditions.
03/2005;
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ABSTRACT: In this paper, a new multi-model direct adaptive decoupling controller is presented for multivariable processes, which includes multiple fixed optimal controllers, one free-running adaptive controller, and one re-initialized adaptive controller. The fixed controllers provide initial control to the process if its model lies in the corresponding region. For each controller selected, the re-initialized adaptive controller uses the values of this particular controller to improve the adaptation speed. This controller may replace the fixed controller at a later stage according to the switching criterion which is to select the best one among all controllers. A free-running adaptive controller is also added to guarantee the overall system stability. Different from the multiple models adaptive control structure proposed in Narendra, Balakrishnan, and Ciliz [Adaptation and learning using multiple models, switching, and tuning. IEEE Control Syst. Mag. 15, 37-51 (1995)], the method not only is applicable to the multi-input multi-output processes but also identifies the decoupling controller parameters directly, which reduces both the computational burden and the chances of a singular matrix during the process of determining controller parameters. Several examples for a wind tunnel process are given to demonstrate the effectiveness and practicality of the proposed method.
ISA Transactions 02/2005; 44(1):131-43. · 1.11 Impact Factor
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ABSTRACT: In this paper, we propose a novel neural network assisted proportional-plus-integral (PI) control strategy to improve the supply air pressure control performance of variable air volume (VAV) system. The neural network is trained on-line with a normalized training algorithm, which eliminates the requirement of a bounded regression signal to the system. To ensure the convergence of the training algorithm, an adaptive dead-zone scheme is employed. Stability of the proposed control scheme is guaranteed based on the conic sector theory. To demonstrate the applicability of the proposed method, real-time tests were carried out on a pilot VAV air-conditioning system and good experimental results were obtained.
Robotics, Automation and Mechatronics, 2004 IEEE Conference on; 01/2005
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ABSTRACT: This paper deals with the problem of designing an H2 controller for a networked control system (NCS) with communication delays from the sensor to the controller and/or from the controller to the plant. Our objective is to design a robust controller that will not only stabilize the system but also achieve a sub-opti- mal H2 performance in the face of possible communication delays. Both the state feedback control and output feedback control are considered. The feedback control problem for the original system is first converted to a static output feedback control problem. A recursive linear matrix inequality (LMI) algorithm is then presented to compute a state or output feedback H2 controller for the system. Our approach allows a fixed order controller. Numerical examples are given to demonstrate the effectiveness of the proposed approach.
Asian Journal of Control 02/2004; 6(1):88 - 96. · 1.03 Impact Factor
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ABSTRACT: In this paper, a novel auto-tuning method for a cascade control system is proposed. By employing a simple relay feedback test, both inner and outer loop model parameters can be simultaneously identified. Consequently, well-established proportional-integral-derivative (PID) tuning rules can be applied to tune both loops. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.
ISA Transactions 02/2003; 42(1):63-72. · 1.11 Impact Factor
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Control and Automation, 2002. ICCA. Final Program and Book of Abstracts. The 2002 International Conference on; 02/2002
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Control and Automation, 2002. ICCA. Final Program and Book of Abstracts. The 2002 International Conference on; 02/2002
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ABSTRACT: Considers dissipative analysis and synthesis problems for a class of signal processing systems with uncertainties satisfying some summed quadratic constraints. For dissipative analysis, we are concerned with deriving conditions such that a given system is of a desired dissipative property. As for the latter problem, we focus on compensation design for each of the subsystems to achieve certain dissipative property such that their interconnections will be stable. Our solution to the above two problems are in terms of the feasibility of one or more linear matrix inequalities which can be solved efficiently
Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on; 02/2000
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ABSTRACT: Under the Environmental Pollution Control Act, all chemical waste treatment industries must monitor and analyze the chemical waste effluent before discharging into the public sewer system to ensure that the waste will not contaminate the drainage system. With the implementation of Newater in Singapore, all care and precaution should be taken to ensure that the source is clean and uncontaminated. The process of analyzing and monitoring using various sensors can be tedious and time consuming for the chemical waste treatment industries. With a centralized monitoring and control system in conjunction with the present chemical processing plant, automation could be achieved which relieves the stress and task of the personnel. The project aims to develop an Intelligent and Portable Multi-Functional Machine which will form the core monitor module for the automated system as well as a waste management system (WMS). The Intelligent and Portable Multi-Functional Machine should be capable of measuring PH, Ion-selective Electrode (ISE), Oxidation-Reduction Potential (ORP), conductivity, Dissolved Oxygen (DO), heavy metal concentration, temperature，Specific Gravity (SG), and heating value. The WMS will include existing technical and historical databases for wastes and chemicals, recommendations and guidelines for the selection of treatment and disposal methods, specific notifications and suggestions for the safety and environmental impact of proper handling factors, reminding and enquiring data/information for further testing and amendment of insufficient and lacking information, and on-line database communication and sharing as well as the capability for new information learning.This report documents the research and the development work of the Intelligent and Portable Multi-Functional Machine and the WMS. It also documents the various procedures used to test the feasibility of concepts in developing the heating value sensor. The experimental testing results which are performed in a real chemical wastewater factory process show that the portable multifunctional machine can give very satisfactory performance. The WMS has been put into operations in Purechem Onyx Pte Ltd since June 2006 and has greatly improved the efficiency and productivity of the company.
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ABSTRACT: In this project, active noise control technique that can be implemented in air conditioning systems are investigated. A fan-duct system is built to conduct active noise control experiments. Feedforward and feedback control algorithms with better robustness and performance are developed and implemented in the experimental system. RG 26/98
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ABSTRACT: This paper presents a practical method to optimize in-building section of centralized Heating, Ventilation and Air-conditioning (HVAC) systems which consist of indoor air loops and chilled water loops. First, through component characteristic analysis, mathematical models associated with cooling loads and energy consumption for heat exchangers and energy consuming devices are established. By considering variation of cooling load of each end user, adaptive neuro-fuzzy inference system (ANFIS) is employed to model duct and pipe networks and obtain optimal differential pressure (DP) set points based on limited sensor information. A mix-integer nonlinear constraint optimization of system energy is formulated and solved by a modified genetic algorithm. The main feature of our paper is a systematic approach in optimizing the overall system energy consumption rather than that of individual component. A simulation study for a typical centralized HVAC system is provided to compare the proposed optimization method with traditional ones. The results show that the proposed method indeed improves the system performance significantly.
Energy and Buildings.
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ABSTRACT: In the centralized heating, ventilating and air-conditioning (HVAC) system, air handling units (AHU) are traditionally controlled by single loop proportional-integral-derivative (PID) controllers. The control structure is simple, but the performance is usually not satisfactory. In this paper, we propose a cascade control strategy for temperature control of AHU. Instead of a fixed PID controller, a neural network controller is used in the outer control loop. This approach not only avoids the tedious tuning procedure for the inner and outer loop PID parameters of a conventional cascade control system, but also makes the whole control system be adaptive and robust. The multilayer neural network is trained online by a special training algorithm simultaneous perturbation stochastic approximation (SPSA) based training algorithm. With the SPSA based training algorithm, the weight convergence of the neural network and stability of the control system is guaranteed. The novel cascade control system has been implemented to improve supply air temperature control performance of AHU in a pilot HVAC system. The experimental results demonstrate the effectiveness of proposed algorithm over classical control systems.
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on;
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ABSTRACT: A neural control scheme for variable air volume (VAV) air-conditioning system is proposed. The neural network is trained online by the simultaneous perturbation stochastic approximation (SPSA) method instead of the standard back-propagation algorithm. The closed-loop stability of the proposed control scheme is guaranteed based on the conic sector theory. The new control scheme provides the desired functionality as well as the adaptation of the VAV control system for a wide range of disturbances and parameter changes. To demonstrate the applicability of the proposed method, real-time experiments were carried out on a pilot VAV air-conditioning system and good testing results are obtained.
Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on;
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ABSTRACT: This paper presents the solution for the global optimization problem for overall heating, ventilating and air conditioning (HVAC) systems using a modified genetic algorithm. The whole implementation procedure of the proposed optimal method is provided. Simulation studies for a pilot scale centralized HVAC plant by the proposed optimal method show that the proposed method indeed improves the system performance significantly compared with traditional control strategies.
Energy Conversion and Management.
