In this paper, a novel event-triggered data-driven predictive iterative learningcontrol (ET-DDPILC) scheme is proposed for a class of discrete-time nonlinear networked controlsystems (NCSs). Firstly, by using dynami...
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ISBN:
(纸本)9798350321050
In this paper, a novel event-triggered data-driven predictive iterative learningcontrol (ET-DDPILC) scheme is proposed for a class of discrete-time nonlinear networked controlsystems (NCSs). Firstly, by using dynamic linearization technique (DLT) to transform the nonlinear NCSs into a virtual linear data model with an introduced unknown pseudo partial derivative (PPD) parameter, we established a data-driven predictive iterative learningcontrol framework. Secondly, in order to reduce the number of ineffective updates during the iterative process, an event-triggered mechanism is designed, and the controller updates data only when the event-triggered condition is satisfied, saving network communication resources. Finally, the monotonic convergence properties of tracking error are explored through a rigorous theoretical analysis, and the effectiveness of the proposed ET-DDPILC algorithm is verified by simulation.
A data-driven iterative learningcontrol (DDILC) method is proposed for the control of the internal mixer temperature (IMT) system with high complex dynamics. First, the dynamic relationships between plate heat exchan...
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ISBN:
(纸本)9798350321050
A data-driven iterative learningcontrol (DDILC) method is proposed for the control of the internal mixer temperature (IMT) system with high complex dynamics. First, the dynamic relationships between plate heat exchanger and electrical exchanger of IMT system is described as a state space model. Then, by using the iterative dynamic linearization tool, the IMT system is transformed into an iterative linear incremental model without neglecting any unmodeled dynamics, where the unknown time-varying parameters are estimated in real-time with the use of only I/O data of IMT system. An event triggering strategy is further incorporated into the proposed DDILC to reduce the number of controller executions and save computation cost. Finally, the simulation study on IMT system verifies the applicability of the proposed DDILC.
In this article, a new data-driven adaptive sliding-mode PID control (DASPIDC) algorithm is proposed for a class of unknown nonlinear discrete systems based on full form dynamic linearization model. The designed algor...
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This article considers the finite-time consistency control issue of high-order multi-input and multi-output (MIMO) nonlinear multi-agent systems (MASs). In the process of establishment, this article uses fuzzy logic s...
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ISBN:
(纸本)9798350321050
This article considers the finite-time consistency control issue of high-order multi-input and multi-output (MIMO) nonlinear multi-agent systems (MASs). In the process of establishment, this article uses fuzzy logic systems (FLS) to identify unknown nonlinear dynamics. Then, according to the premise of backstepping recursion technology and limited-time stability criterion, an adaptive limited-time consistency control program is designed. In addition, a new integral Lyapunov function is established by introducing the power integral technology, which proves the stability and convergence of the system and tracking deviation. Finally, taking marine surface vehicle (MSV) as an example, the availability of the established consistency control scheme is described.
The iterative learningcontrol problem of moving boundary distributed parameter systems with control delay under sensor/actuator networks is studied. A P-type iterative learning algorithm with known delays is proposed...
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ISBN:
(纸本)9798350321050
The iterative learningcontrol problem of moving boundary distributed parameter systems with control delay under sensor/actuator networks is studied. A P-type iterative learning algorithm with known delays is proposed. The convergence of linear systems with sensor/actuator networks is proved by using compression mapping principle. In order to further verify the feasibility of the algorithm the nonlinear system with control delay is also considered, and its convergence is proved by strict mathematical analysis. Through strict mathematical analysis, the condition of convergence of output error is obtained. Numerical results show the effectiveness of the proposed method.
This article addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuou...
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This article addresses the problem of data-driven computation of controllers that are correct by design for safety-critical systems and can provably satisfy (complex) functional requirements. With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage. The first stage utilizes available Bayesian regression results to compute robust credible sets for the true parameters of the system. For the second stage, we introduce methods for systems subject to both stochastic and parametric uncertainties. We provide simulation relations for enabling correct-by-design control refinement that are founded on coupling uncertainties of stochastic systems via subprobability measures. The presented relations are essential for constructing abstract models that are related to not only one model but to a set of parameterized models. The results are demonstrated on three case studies, including a nonlinear and a high-dimensional system.
The batch process is a typical manufacturing mode in industry. In this article, an adaptive ILC method is proposed for the batch process with time-varying and unknown parameters. The proposed method involves merging a...
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ISBN:
(纸本)9798350321050
The batch process is a typical manufacturing mode in industry. In this article, an adaptive ILC method is proposed for the batch process with time-varying and unknown parameters. The proposed method involves merging an adaptive updating law that utilizes the steepest descent method to estimate unknown parameters with a controller that adjusts the estimated system. The proposed condition ensures that the estimated parameter error remains bounded and that the estimated state error is stabilized. The controller utilizes the estimated results to steer the estimated system to track the reference trajectory. A numerical experiment is presented to demonstrate the efficiency of the proposed method.
This paper addresses the optimization problem of quantized iterative learningcontrol (ILC) for networked controlsystems (NCSs) with limited bandwidth. For linear time-invariant systems with quantized input signals, ...
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ISBN:
(纸本)9798350321050
This paper addresses the optimization problem of quantized iterative learningcontrol (ILC) for networked controlsystems (NCSs) with limited bandwidth. For linear time-invariant systems with quantized input signals, a mathematical cost function is constructed to obtain a gradient-based ILC law that rests with the system model, and the learning gain is updated in the trial domain. By combining the infinite logarithmic quantizer with the encoding and decoding mechanism to encode and decode the signals, the quantization accuracy is enhanced and the system tracking capability is improved. Compared with the traditional gradient descent method with fixed learning gain, the gradient-based ILC law can obtain faster error convergence. Simulation based on industrial robot system is given to substantiate the suggested method.
This paper researches the synchronisation problem of complex dynamic networks with time-varying coupling delays based on sampled datacontrol. First, a new Lyapunov-Krasovskii function (LKF) is constructed and then li...
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ISBN:
(纸本)9798350321050
This paper researches the synchronisation problem of complex dynamic networks with time-varying coupling delays based on sampled datacontrol. First, a new Lyapunov-Krasovskii function (LKF) is constructed and then linear matrix inequality (LMI) is gained using Wirtinger's inequality. Then, by solving the LMI, the unconservative condition for guaranteeing the synchronisation of a time-varying coupled time delay complex network for the control of sampled data is obtained. Finally, the example of numerical simulations shows that has broad application prospects.
This paper studies the optimal tracking control problem of discrete-time linear multi-input systems from the perspective of Non-Zero-Sum Games (NZSG) using reinforcement Q-learning technique. Firstly, an augmented mul...
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ISBN:
(纸本)9798350321050
This paper studies the optimal tracking control problem of discrete-time linear multi-input systems from the perspective of Non-Zero-Sum Games (NZSG) using reinforcement Q-learning technique. Firstly, an augmented multi-input systems is constructed by combining the original multi-input systems and the reference trajectory dynamics. Then, the original optimal tracking control problem can be transformed into the NZSG optimal control problem of the constructed augmented multi-input systems. In order to obtain the Nash equilibrium solution of the NZSG optimal control problem, a Q-function is introduced and an reinforcement Q-learning algorithm is designed to learn the Nash equilibrium solution. The convergence of the reinforcement Q-learning algorithm is also given. Finally, a simulation example is given to verify the effectiveness of the proposed reinforcement Q-learning algorithm.
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