A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant i...A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.展开更多
We proposed and implemented a leg-vector water-jet actuated spherical robot and an underwater adaptive motion control system so that the proposed robot could perform exploration tasks in complex environments.Our aim w...We proposed and implemented a leg-vector water-jet actuated spherical robot and an underwater adaptive motion control system so that the proposed robot could perform exploration tasks in complex environments.Our aim was to improve the kinematic performance of spherical robots.We developed mechanical and dynamic models so that we could analyze the motions of the robot on land and in water.The robot was equipped with an Inertial Measurement Unit(IMU)that provided inclination and motion information.We designed three types of walking gait for the robot,with different stabilities and speeds.Furthermore,we proposed an online adjustment mechanism to adjust the gaits so that the robot could climb up slopes in a stable manner.As the system function changed continuously as the robot moved underwater,we implemented an online motion recognition system with a forgetting factor least squares algorithm.We proposed a generalized prediction control algorithm to achieve robust underwater motion control.To ensure real-time performance and reduce power consumption,the robot motion control system was implemented on a Zynq-7000 System-on-Chip(SoC).Our experimental results show that the robot’s motion remains stable at different speeds in a variety of amphibious environments,which meets the requirements for applications in a range of terrains.展开更多
Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-s...Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results, we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.展开更多
针对网络控制系统(networked control system,NCS)中随机时延导致系统性能下降的问题,利用粒子群优化(particle swarm optimization,PSO)的最小二乘支持向量机(least square support vector machine,LSSVM)建立NCS中随机时延预测模型,...针对网络控制系统(networked control system,NCS)中随机时延导致系统性能下降的问题,利用粒子群优化(particle swarm optimization,PSO)的最小二乘支持向量机(least square support vector machine,LSSVM)建立NCS中随机时延预测模型,精确预测未来时刻的时延;同时利用该预测算法预测的时延通过快速隐式广义预测控制算法对NCS随机时延进行补偿。仿真结果表明,PSO优化的LS-SVM算法对随机时延具有较高的预测精度,同时快速隐式广义预测控制算法可使系统的输出很好地跟踪参考轨迹,保证系统良好的控制效果。展开更多
基金This work has been supported by the National Outstanding Youth Science Foundation of China (No. 60025308) and the Teach and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE,China.
文摘A prediction control algorithm is presented based on least squares support vector machines (LS-SVM) model for a class of complex systems with strong nonlinearity. The nonlinear off-line model of the controlled plant is built by LS-SVM with radial basis function (RBF) kernel. In the process of system running, the off-line model is linearized at each sampling instant, and the generalized prediction control (GPC) algorithm is employed to implement the prediction control for the controlled plant. The obtained algorithm is applied to a boiler temperature control system with complicated nonlinearity and large time delay. The results of the experiment verify the effectiveness and merit of the algorithm.
基金National Natural Science Foundation of China(61773064,61503028).
文摘We proposed and implemented a leg-vector water-jet actuated spherical robot and an underwater adaptive motion control system so that the proposed robot could perform exploration tasks in complex environments.Our aim was to improve the kinematic performance of spherical robots.We developed mechanical and dynamic models so that we could analyze the motions of the robot on land and in water.The robot was equipped with an Inertial Measurement Unit(IMU)that provided inclination and motion information.We designed three types of walking gait for the robot,with different stabilities and speeds.Furthermore,we proposed an online adjustment mechanism to adjust the gaits so that the robot could climb up slopes in a stable manner.As the system function changed continuously as the robot moved underwater,we implemented an online motion recognition system with a forgetting factor least squares algorithm.We proposed a generalized prediction control algorithm to achieve robust underwater motion control.To ensure real-time performance and reduce power consumption,the robot motion control system was implemented on a Zynq-7000 System-on-Chip(SoC).Our experimental results show that the robot’s motion remains stable at different speeds in a variety of amphibious environments,which meets the requirements for applications in a range of terrains.
文摘Power control is of paramount importance in combating the near-far problem and co-channel interference in a CDMA cellular system. Due to fast fading and ambient interference in a wireless channel, conventional fixed-step power control schemes have difficulty in compensating for the fast fading channel dynamically and in a timely manner. To acquire flexible power regulation in order to maintain required transmission capacity under the given transmission quality requirement, we propose a hybrid power control scheme which makes full use of the simple fuzzy inference rule refined by an operator in the fuzzy control and prediction property from related previous results in Generalized Prediction Control (GPC). In implementation of this strategy, we classify the fading zone into three levels according to the signal-to-noise-rate (SNR) requirement. In each level the power compensation amount varies with fading gradient and the compensation scheme varies as well. The digital results show that adoption of the fuzzy-GPC power regulation scheme has acquired a reasonable performance improvement when compared with fixed-step and fuzzy schemes. According to theoretic analysis and simulation results, we can conclude that under a variational transmission environment, a flexible power regulation scheme such as fuzzy-GPC is easy to adapt to the environment and thus overcomes the near-far effect and multi-access interference effectively.
文摘针对网络控制系统(networked control system,NCS)中随机时延导致系统性能下降的问题,利用粒子群优化(particle swarm optimization,PSO)的最小二乘支持向量机(least square support vector machine,LSSVM)建立NCS中随机时延预测模型,精确预测未来时刻的时延;同时利用该预测算法预测的时延通过快速隐式广义预测控制算法对NCS随机时延进行补偿。仿真结果表明,PSO优化的LS-SVM算法对随机时延具有较高的预测精度,同时快速隐式广义预测控制算法可使系统的输出很好地跟踪参考轨迹,保证系统良好的控制效果。
